diff --git a/.RData b/.RData deleted file mode 100644 index c18bcffd3..000000000 Binary files a/.RData and /dev/null differ diff --git a/.Rbuildignore b/.Rbuildignore index 38a2c484d..d82f4d649 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -27,4 +27,8 @@ Spreadsheets ^tic\.R$ rcppExports.cpp stanExports_* -^data-raw$ \ No newline at end of file +^data-raw$ +version_lookup.R +version_lookup.csv +.gitsum +Presentations diff --git a/.gitignore b/.gitignore index 6580fc6a8..75320bd7e 100644 --- a/.gitignore +++ b/.gitignore @@ -19,5 +19,8 @@ Spreadsheets *.h *.o *.so -docs/ +.gitsum .RData +/doc/ +/Meta/ +Presentations diff --git a/DESCRIPTION b/DESCRIPTION index 13d51e14e..e8bb066c2 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: DemoTools Type: Package Title: Standardize, Evaluate, and Adjust Demographic Data -Version: 01.13.39 -Date: 2021-03-23 +Version: 01.13.80 +Date: 2023-12-29 Authors@R: c( person("Tim", "Riffe", role = c("aut", "cre"), email = "tim.riffe@gmail.com", comment = c(ORCID = "0000-0002-2673-4622")), @@ -22,7 +22,7 @@ License: file LICENSE LazyLoad: yes LazyData: true Roxygen: list(markdown = TRUE) -RoxygenNote: 7.1.1 +RoxygenNote: 7.2.3 Depends: R (>= 3.6), Rcpp (>= 0.12.0), @@ -31,15 +31,16 @@ Suggests: testthat (>= 2.1.0), knitr, rmarkdown, + markdown, DT, ggplot2 RdMacros: Rdpack Imports: data.table (>= 1.13.6), demogR, - DemoToolsData (>= 0.1.1), + DemoToolsData (>= 0.1.5), dplyr, - fertestr (>= 0.0.5), + fertestr (>= 0.10.00), lubridate, magrittr, MortalityLaws (>= 1.7.0), @@ -48,10 +49,11 @@ Imports: rstan (>= 2.18.1), tibble, tidybayes, - ungroup + ungroup (>= 1.4.2) BugReports: https://github.com/timriffe/DemoTools/issues Remotes: - github::josehcms/fertestr, - github::timriffe/DemoToolsData + github::timriffe/fertestr, + github::timriffe/DemoToolsData, + github::timriffe/MortalityLaws Encoding: UTF-8 VignetteBuilder: knitr diff --git a/NAMESPACE b/NAMESPACE index cbc1bafd5..0f09fb057 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -3,6 +3,7 @@ export("%>%") export(ADM) export(AGEN) +export(HMD_old_logquad) export(ID) export(IRD) export(OPAG) @@ -71,6 +72,7 @@ export(is_age_redundant) export(is_age_sequential) export(is_single) export(loess_smth1) +export(logquad_augmented) export(lt_a_closeout) export(lt_a_pas) export(lt_a_un) @@ -161,6 +163,7 @@ importFrom(data.table,rbindlist) importFrom(data.table,setDT) importFrom(data.table,uniqueN) importFrom(demogR,cdmltw) +importFrom(dplyr,case_when) importFrom(dplyr,group_by) importFrom(dplyr,mutate) importFrom(dplyr,rename) @@ -192,3 +195,4 @@ importFrom(tibble,tibble) importFrom(tidybayes,gather_draws) importFrom(ungroup,pclm) importFrom(utils,head) +importFrom(utils,tail) diff --git a/NEWS.md b/NEWS.md index fd983f7e1..e1b87183d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,14 +1,17 @@ # Changes in this update -2018-08-15 + +## 2018-08-15 - NEWS.md file added. When a first release comes out, major functionality and features will be listed here at that time. -2019-11-28 +## 2019-11-28 - new function mig_calculate_rc() added - several function names harmonized, see ?DemoTools-renamed - - graduate_sprage() and other graduation functions no longer support matrix inputs + - graduate_sprague() and other graduation functions no longer support matrix inputs +## 2023-12-29 + - minor fixes to pass checks \ No newline at end of file diff --git a/R/AGEINT.R b/R/AGEINT.R index e875e4b16..790cc5c5d 100644 --- a/R/AGEINT.R +++ b/R/AGEINT.R @@ -89,7 +89,8 @@ interpolatePop <- #' @param datesOut vector of dates. The desired dates to interpolate to. See details for ways to express it. #' @param method string. The method to use for the interpolation, either \code{"linear"}, \code{"exponential"}, or \code{"power"}. Default \code{"linear"}. #' @param power numeric power to interpolate by, if \code{method = "power"}. Default 2. -#' @param extrap logical. In case \code{datesOut} is out of range of datesIn, do extrapolation using slope in extreme pairwise. Deafult \code{FALSE}. +#' @param extrap logical. In case \code{datesOut} is out of range of `datesIn`, do extrapolation using slope in extreme pairwise. Default \code{FALSE}. +#' @param negatives logical. In case negative output are accepted, set to \code{TRUE}. Default \code{FALSE}. #' @param ... arguments passed to \code{stats::approx}. For example, \code{rule}, which controls extrapolation behavior. #' @details The age group structure of the output is the same as that of the input. Ideally, \code{datesOut} should be within the range of \code{datesIn}. If not, the left-side and right-side output are held constant outside the range if \code{rule = 2} is passed in, otherwise \code{NA} is returned (see examples). Dates can be given in three ways 1) a \code{Date} class object, 2) an unambiguous character string in the format \code{"YYYY-MM-DD"}, or 3) as a decimal date consisting in the year plus the fraction of the year passed as of the given date. #' @@ -198,6 +199,7 @@ interp <- function(popmat, method = c("linear", "exponential", "power"), power = 2, extrap = FALSE, + negatives = FALSE, ...) { # ... args passed to stats::approx . Can give control over extrap assumptions # IW: extrap=T for extrapolate following each slope in extreme pairwise. @@ -282,10 +284,10 @@ interp <- function(popmat, int <- int ^ power } - # IW: no negatives when extrapolate. Thinking in pop and lt expressions - if(all(!is.na(int)) & any(int<0)){ - cat("Negative values were turned 0. No accepted in population counts, fertility rates or life table functions.\n") - int[int<0] <- 0 + # IW: no negatives when extrapolate. Thinking in pop and lt expressions. Inactive when explicitly are accepted negatives + if(!negatives & all(!is.na(int)) & any(int < 0)){ + cat("Negative values have been replaced with 0s.\nNegatives not accepted in population counts,\n fertility rates or life table functions.\nYou can allow negatives (e.g. interpolating net migration)\n by specifying negatives = TRUE") + int[int < 0] <- 0 } int diff --git a/R/AGESEX.R b/R/AGESEX.R index 836b4a067..3b5f5634e 100644 --- a/R/AGESEX.R +++ b/R/AGESEX.R @@ -19,7 +19,7 @@ #' It is also assumed that the final age group is open, unless \code{ageMax < max(Age)}. #' Setting \code{OAG = FALSE} will override this and potentially include \code{max(Age)} in calculations. #' The method argument determines the weighting of numerators and denominators, where the UN method puts -#' twice the numerator over the sum of the adjacent ages classes, Zelnich does thrice the +#' twice the numerator over the sum of the adjacent ages classes, Zelnik does thrice the #' numerator over the sum of the whole range from the next lowest to the next highest age, and #' Ramachandran does four times the numerator over the same sum, but with the central age #' double-counted in the numerator. diff --git a/R/MAV.R b/R/MAV.R index f57291a54..659dfca6f 100644 --- a/R/MAV.R +++ b/R/MAV.R @@ -1,22 +1,22 @@ -# Author: JG -# Edited 9-Dec-2017 by TR -# Edited Aug 2018 by TR - -############################################################################### #' Calculate the moving average (mav) over 3 or 5 years. #' @description This arithmetic smoothing technique aims to eliminate irregularities of the population pyramid by averaging values in a moving window of user-defined width. -#' @details The moving window is applied symmetrically. Data endpoints are imputed with \code{NA}s in output: the is nothing under 0 or over the highest closed age group to average with. The open age group is imputed with \code{NA} prior to calculations, since it cannot be averaged into the next lowest group. For example, for \code{n=3}, age 0 will be \code{NA}, as will the open age group and next lowest age. Age intervals are assumed uniform. This function could be used with either single or 5-year age groups. +#' @details +#' The moving window is applied symmetrically. By default (`tails = FALSE`) data endpoints are imputed with `NA`s in output: the is nothing under 0 or over the highest closed age group to average with. The open age group is not used in averaging, and it is returned as-is. Age intervals are assumed uniform. This function could be used with either single or 5-year age groups. +#' +#' If `tails` is set to `TRUE`, then tails have been imputed using moving averages with successively smaller values of `n`, the cascade method. #' @param Value numeric. A vector of demographic counts in single age groups. #' @param n integer. A single number, (often 3 or 5), indicating the number of years taken to smooth the population distribution by single ages. #' @param Age integer. A vector of ages corresponding to the lower integer bound of the counts. -#' @param OAG logical. Whether or not the top age group is open. Default \code{TRUE}. -#' @details Ages may be single or grouped, but all age intervals are assumed equal. +#' @param OAG logical. Whether or not the top age group is open. Default `TRUE`. +#' @param tails logical. If set to `TRUE`, smaller-n moving averages are applied on both tails +#' such that all values are non-NA. If `FALSE` (default), tails are set to NA +#' due to the lag of moving averages. #' @return Vector with the smoothed demographic counts. +#' #' @export -#' @author Juan Galeano #' @references #' \insertRef{GDA1981IREDA}{DemoTools} @@ -31,9 +31,16 @@ #' 323263,9535,13906,9063,8294,90459,9817,6376,8884,3773,160609) #'Age <- 0:70 #'# final age group assumed open -#'mav(Pop, n = 3, Age = Age) +#' mav(Pop, n = 3, Age = Age) +#' #'\dontrun{ -#' nwindows <- sapply(seq(3, 11, by = 2),mav, Value = Pop, Age = Age) +#' odds <- seq(3, 11, by = 2) +#' nwindows <- sapply(odds, +#' mav, +#' Value = Pop, +#' Age = Age, +#' OAG = TRUE, +#' tails = FALSE) #' cols <- gray(seq(.8, 0, length = 5)) #' lwds <- seq(3, 1, length = 5) #' plot(Age,Pop, col = "red", xlab = "Age", ylab = "The counts", pch=16, @@ -45,21 +52,92 @@ #' lwd = lwds, #' legend = paste0("n=",seq(3,11,by=2))) #'} +#' +#' # For cascading smoothing on the tails: +#' mav(Pop, Age, tails = TRUE) +#' +#'\dontrun{ +#'# Compare +#' nwindows_tails <- sapply(odds, +#' mav, +#' Value = Pop, +#' Age = Age, +#' OAG = TRUE, +#' tails = TRUE) +#' +#' colnames(nwindows) <- odds +#' colnamaes(nwindows_tails) <- odds +#' +#' # NA triangles are completed with +#' # successively smaller ns. +#' head(nwindows) +#' head(nwindows_tails) +#' +#' tail(nwindows) +#' tail(nwindows_tails) +#' } -mav <- function(Value, Age, n = 3, OAG = TRUE) { +mav <- function(Value, Age, n = 3, OAG = TRUE, tails = FALSE) { In <- Value if (missing(Age)) { Age <- as.integer(names(Value)) } + + # save OAG if (OAG) { + OrigOAGpop <- Value[length(Value)] Value[length(Value)] <- NA } - # TR: not sure why n needs to be hard coded + Out <- ma(Value, n) + # apply cascading tails if needed + if (tails){ + Out <- mav_tails(Value = Value, + Age = Age, + MavOut = Out, + n = n, + OAG = OAG) + } + # plug OAG back in + if (OAG){ + Out[length(Value)] <- OrigOAGpop + } + structure(Out, names = Age) } +# Not exported since it can be called with tails = FALSE on mav. +mav_tails <- function(Value, Age, MavOut, n = 3, OAG = TRUE) { + NewMavOut <- MavOut + + #Last should point to last age group to use + Last <- length(Age) + if (OAG) { + Last <- Last - 1 + NewMavOut[Last+1] <- Value[Last+1] + } + + NewMavOut[1] <- Value[1] + NewMavOut[Last] <- Value[Last] + + MavLev <- c(1,2,4,6,8) + + if (n >= 2) { + for(i in 2:(as.integer(n/2))) { + + # TR: why not just calculate the whole thing and once and pick out + # the two values as needed? + NewMavOut[i] <- ma(Value[1:(MavLev[i]+1)], n = MavLev[i])[i] + # subscripts right and select just the correct age + NewMavOut[Last - i + 1] <- ma(Value[(Last - MavLev[i] ):Last], + n = MavLev[i])[ i ] + + } + } + + NewMavOut +} #Pop <-c(303583,390782,523903,458546,517996,400630,485606,325423,471481,189710, diff --git a/R/OGIVE.R b/R/OGIVE.R index 4b9be88e8..48665b544 100644 --- a/R/OGIVE.R +++ b/R/OGIVE.R @@ -182,7 +182,7 @@ poly_smth1 <- #' @details LOESS (locally weighted smoothing) helps to smooth data over age, preserving the open age group if necessary. #' It is a popular tool to create a smooth line through a timeplot or scatter plot. #' Loess smoothness may be tweaked by specifying an optional \code{"span"} argument. -#' Polynomial fitting is used to mooth data over age or time fitting linear models. +#' Polynomial fitting is used to smooth data over age or time fitting linear models. #' It can be tweaked by changing the degree and by either log or power transforming. #' The open age group can be kept as-is if desired by specifying \code{OAG = TRUE}. #' May be used on any age groups, including irregularly spaced, single age, or 5-year age groups. diff --git a/R/OPAG.R b/R/OPAG.R index 047b35dd7..0516cd6ce 100644 --- a/R/OPAG.R +++ b/R/OPAG.R @@ -2,22 +2,29 @@ # [ ] All choice of Age_fit / AgeInt_fit based on criteria (see PJ Drive folder) # [ ] ensure age groups are flexible as required. # [ ] what happens when one input is in a different age group than another? -# [ ] OPAG_simple() should allow for non-single ages +# [x] OPAG_simple() should allow for non-single ages # [ ] add unit tests # [ ] check for age group commensurability and warn if necessary # [ ] add more examples to OPAG? -# [ ] remove rownames message from DownloadLx(), haha +# [ ] remove rownames message from DownloadLx(), haha I DON'T SEE THIS # [ ] test OPAG_simple() with non-single ages groups, update documentation if necessary. # [ ] harmonize args betwenn OPAG_simple and OPAG family. +# [x] make AgeInt not required +# [ ] document Age_fit better +# [x] change open age formula in warp function +# [x] fix continuous = FALSE formula/ then removed +# [x] ensure Lx is single ages once for use throughout +# [x] change default method to "mono" + # Author: tim ############################################################################### # distribute population in open age group over higher ages. # The PAS implementation uses stable populations, and it will be added -# here in the future, as well as other optiond. The main missing piece +# here in the future, as well as other options. The main missing piece # is a good collection of model lifetables. -#' redistripute an open age group count over higher ages proportional to an arbitrary standard +#' redistribute an open age group count over higher ages proportional to an arbitrary standard #' @description This method could be useful whenever a reasonable standard is available. At present the standard must be supplied by the user. #' @details In this implementation both the original population counts and the standard must be in single ages. #' @param Pop numeric vector of population counts @@ -72,7 +79,7 @@ OPAG_simple <- StPop, StAge, OAnew = max(StAge)) { - # # assume single + # # assume single NOT NEEDED See age concordance # stopifnot(is_single(Age)) # stopifnot(is_single(StAge)) # OAG can be less than or equal to max age @@ -80,13 +87,15 @@ OPAG_simple <- stopifnot(OAnew %in% StAge) # age and pop vectors must match lengths, assume ordered stopifnot(length(Pop) == length(Age)) + stopifnot(length(StPop) == length(StAge)) # age concordance - #stopifnot(all(Age %in% StAge)) + minStAge = min(StAge) + stopifnot(all(Age[Age >= minStAge] %in% StAge)) # group pop down to OAG Pop <- groupOAG(Pop, Age, OAnow) StPop <- groupOAG(StPop, StAge, OAnew) - + # even up lengths N <- length(Pop) Age <- Age[1:N] @@ -94,19 +103,19 @@ OPAG_simple <- # same for standard StN <- length(StPop) StAge <- StAge[1:StN] - - # make stadnard distribution. + + # make standard distribution. standard <- rescale_vector(StPop[StAge >= OAnow], scale = 1) # redistribute OAG PopUpper <- OAtot * standard # keep lower ages of Pop PopLower <- Pop[1:(N - 1)] - + # graft, name, and return out <- c(PopLower, PopUpper) Ageout <- sort(unique(c(Age, StAge))) names(out) <- Ageout - + out } @@ -114,16 +123,13 @@ OPAG_simple <- #' Warps a given stationary population into a stable population #' @description We take `nLx` as indicative of a stationary population age structure, #' then subject the population structure to long-term growth by a constant rate, `r`. -#' @details `nLx` could be any population structure of any scale, as long as you're comfortable +#' @details `Lx1` could be any population structure of any scale, as long as you're comfortable #' assuming it's stationary and can be warped into stable. For the oldest ages, this is probably #' quite often an acceptable and useful approximation. The transformation is applied at the single-age scale, even if the input `nLx` is in wider (e.g. abridged) age groups. When needed, we reduce to single ages using (default) `graduate_uniform()`, then apply the transformation, then group back. This is innocuous if `nLx` is given in single ages. You may want to change `method` to `"mono"` or `"pclm"`. #' -#' @param nLx numeric vector of stationary population age structure in arbitrary integer age groups -#' @param Age interger vector of lower bounds of age groups of `nLx` +#' @param Lx1 numeric vector of stationary population age structure in arbitrary integer age groups +#' @param Age_Lx1 integer vector of lower bounds of age groups of `nLx` #' @param r stable growth rate -#' @param AgeInt optional integer vector of widths of age groups, inferred if not given. -#' @param continuous logical. If `TRUE` we use the growth adjustment. `e^(-age*r)`. If `FALSE` we assume `r` is geometric growth, and we use `(1+r)^age` for the growth adjustment. -#' @param method character, graduation method used for intermediate graduation. Default `"uniform"`. Other reasonable choices include `"mono"` or `"pclm"`. #' @return numeric vector of the transformed `nLx`. Note, this vector sums to `1`. #' @export #' @examples @@ -138,47 +144,40 @@ OPAG_simple <- #' } #' -OPAG_nLx_warp_r <- function(nLx, - Age, - r, - AgeInt = NULL, - continuous = TRUE, - method = "uniform"){ - # Let's do this in single ages :-) - # for now, just uniform, but could pass in args to graduate of course - # if that is preferred. - Lx1 <- graduate(nLx, Age, method = method, constrain = TRUE) - a1 <- names2age(Lx1) - if (continuous){ - wLx <- exp(-r * (a1 + .5)) * Lx1 +OPAG_nLx_warp_r <- function(Lx1, + Age_Lx1, + r +){ + a1 <- Age_Lx1 + w1Lx <- exp(-r * (a1 + .5)) * Lx1 + nAges <- length(Age_Lx1) + + if (r == 0 ) { + w1Lx[nAges] <- Lx1[nAges] } else { - # then geometric - w <- (1 + r) ^ (a1 + .5) - wLx <- w * nLx - } - wLx <- wLx / sum(wLx) - if (is.null(AgeInt)){ - AgeInt <- age2int(Age, OAvalue = 1) + Tlast <- Lx1[nAges] + Tprev <- Tlast + Lx1[nAges-1] + # PJ's closeout + abar <- -((log(Lx1[nAges-1])-r*a1[nAges-1])-log(Tprev*exp(r)-Tlast)) / r + w1Lx[nAges] <- exp(-r * abar) * Tlast } - a12A <- rep(Age, AgeInt) - nwLx <- groupAges(wLx, Age = a1, AgeN = a12A) - nwLx + + w1Lx <- w1Lx / sum(w1Lx) + + w1Lx } #' calculates residual for optimizing growth rate r for OPAG family #' @description For a given set of age groups to fit against, and a given stable growth rate, $r$, #' what is the error implied given the current $r$ and stationary standard? -#' @details This is a utiltiy function for `OPAG()`, which needs to optimize $r$ for a +#' @details This is a utility function for `OPAG()`, which needs to optimize $r$ for a #' given population vector and stationary standard. #' @param r given stable growth rate #' @param Pop_fit numeric vector of at least two population counts to use for fitting #' @param Age_fit integer vector of lower bounds for age groups of `Pop_fit` #' @param AgeInt_fit integer vector of widths of age groups of `Pop_fit` -#' @param nLx numeric vector of stable population standard -#' @param Age_nLx integer vector of lower bounds for age groups of `nLx` -#' @param AgeInt_nLx optional integer vector of widths of age groups of `nLx`, inferred if not given. -#' @param continuous logical. If `TRUE` we use the growth adjustment. `e^(-age*r)`. If `FALSE` we assume `r` is geometric growth, and we use `(1+r)^age` for the growth adjustment. -#' @param method character. Graduation method, default `"uniform"`. `"mono"` or `"pclm"` would also be good choices. +#' @param Lx1 numeric vector of stable population standard by single ages +#' @param Age_Lx1 integer vector of lower bounds for age groups of `Lx1` #' @return numeric. A residual that you're presumably trying to minimize. #' @export @@ -188,95 +187,70 @@ OPAG_nLx_warp_r <- function(nLx, #' Age_fit <- c(70,80) #' AgeInt_fit <- c(10,10) #' nLx <- downloadnLx(NULL, "Spain","female",1971) -#' Age_nLx <- names2age(nLx) +#' # graduate(nLx, Age_nLx, method = method, constrain = TRUE) +#' Ageab <- names2age(nLx) +#' Lx1 <- graduate(c(nLx), Ageab, method = "mono", constrain = TRUE) +#' Age_Lx1 <- 0:100 #' r <- .01 #' #' OPAG_r_min(r, -#' Pop_fit, -#' Age_fit, -#' AgeInt_fit, -#' nLx, -#' Age_nLx) +#' Pop_fit = Pop_fit, +#' Age_fit = Age_fit, +#' AgeInt_fit = AgeInt_fit, +#' Lx1 = Lx1, +#' Age_Lx1 = Age_Lx1) #' #' (r_opt <- optimize(OPAG_r_min, #' Pop_fit = Pop_fit, #' Age_fit = Age_fit, #' AgeInt_fit = AgeInt_fit, -#' nLx = nLx, -#' Age_nLx = Age_nLx, +#' Lx1 = Lx1, +#' Age_Lx1 = Age_Lx1, #' interval = c(-0.05,.05))$min) #' -#' ai <- age2int(Age_nLx) -#' -#' # Note the whole age range is being scaled to 1 here, but in practice -#' # you'd only be doing this in the highest ages. If only two fitting -#' # ages are given, then we can get an r that matches them perfectly, -#' # at least within reasonable bounds. -#' \dontrun{ -#' plot(Age, nLx / sum(nLx) / ai, type = 's') -#' lines(Age,OPAG_nLx_warp_r(Lx,Age,r=r_opt)/ai,type='s',col = "red") -#' } OPAG_r_min <- function(r, - Pop_fit, Age_fit, + Pop_fit, AgeInt_fit, # necessary - nLx, - Age_nLx, - AgeInt_nLx = NULL, - continuous = TRUE, - method = "uniform"){ - if (is.null(AgeInt_nLx)){ - AgeInt_nLx <- age2int(Age_nLx, OAvalue = 1) - } - # This is the standard we want to match to Pop, + Lx1, + Age_Lx1 +){ + AgeInt_nLx <- age2int(Age_Lx1, OAvalue = 1) + + # 1) This is the standard we want to match to Pop, # which has presumably been cut down / grouped to the # ages we want to calibrate to. - wnLx <- OPAG_nLx_warp_r( - nLx = nLx, - Age = Age_nLx, - r = r, - AgeInt = AgeInt_nLx, - continuous = continuous) - - # now need to get it to the same age groups as Pop + w1Lx <- OPAG_nLx_warp_r( + Lx1 = Lx1, + Age_Lx1 = Age_Lx1, + r = r + ) + + # 2) now need to get it to the same age groups as Pop # so that we can get a residual - - # 1) Move stable pop to single ages - w1Lx <- graduate( - wnLx, - Age = Age_nLx, - AgeInt = AgeInt_nLx, - method = method) - a1t <- names2age(w1Lx) - a1t <- as.integer(a1t) - - # 2) which single ages implied by Pop? - N <- length(AgeInt_fit) - a1match <- Age_fit[1]:(max(Age_fit) + AgeInt_fit[N] - 1) - a1match <- as.integer(a1match) - - # 3) select down to just those ages: - ind <- a1t %in% a1match - w1Lx <- w1Lx[ind] - - # 4) group w1Lx to same as Pop_fit - ageN <- rep(Age_fit, times = AgeInt_fit) - stand <- groupAges(w1Lx, Age = a1match, AgeN = ageN) - - # 5) rescale standard and Pop_fit to sum to 1 - stand <- rescale_vector(stand, scale = 1) + + w1Lx_fit <- rep(NA, length(Age_fit)) + + for (i in 1:length(Age_fit)){ + ind <- Age_Lx1 >= Age_fit[i] & Age_Lx1 < (Age_fit[i] + AgeInt_fit[i]) + w1Lx_fit[i] <- sum(w1Lx[ind]) + } + + # 3) rescale standard and Pop_fit to sum to 1 + stand <- rescale_vector(w1Lx_fit, scale = 1) Pop_fit <- rescale_vector(Pop_fit, scale = 1) - - # 6) return the residual + + # 4) return the residual sum(abs(stand - Pop_fit)) } #' creates stable standard based on optimizing the growth rate #' @description The stationary standard, `nLx` is transformed into a stable standard by optimizing a growth rate, `r` such that the stable standard matches observed population counts in selected age groups. Usually the ages used for fitting are wide age groups in older ages preceding the open age group. The standard output by this function is used by `OPAG` to create the standard used to redistribute counts over older age groups up to a specified open age group, such as 100. -#' @details The arguments `method` and `continous` don't have much leverage on the result. In short, the stable population transformation is done by ungrouping `nLx` to single ages (if it isn't already), and `method` controls which graduation method is used for this, where `"uniform"`, `"mono"`, `"pclm"` are the reasonable choices at this writing. In single ages, the difference between using a geometric `r` versus continuous `r` are quite small for this task. -#' +#' @details The argument `method` don't have much leverage on the result. In short, the stable population transformation is done by ungrouping `nLx` to single ages (if it isn't already), and `method` controls which graduation method is used for this, where `"uniform"`, `"mono"`, `"pclm"` are the reasonable choices at this writing. +#' +#' #' @inheritParams OPAG_r_min #' @return #' list constaining @@ -289,71 +263,64 @@ OPAG_r_min <- function(r, #' @examples #' Pop_fit <- c(85000,37000) #' Age_fit <- c(70,80) -#' AgeInt_fit <- c(10,10) #' nLx <- downloadnLx(NULL, "Spain","female",1971) #' Age_nLx <- names2age(nLx) -#' -#' # India Males, 1991 +#' Lx1 <- graduate(nLx,Age=Age_nLx,method = "mono") +#' Age_Lx1 <- 0:100 +#' # India Males, 1971 #' Pop <- smooth_age_5(pop1m_ind, #' Age = 0:100, #' method = "Arriaga") #' Pop80 <- groupOAG(Pop, names2age(Pop), 80) #' Age <- names2age(Pop80) -#' AgeInt <- age2int(Age, OAvalue = 1) -#' -#' nLx <- downloadnLx(NULL, "India","male",1991) +#' +#' nLx <- downloadnLx(NULL, "India","male",1971) #' Age_nLx <- names2age(nLx) -#' AgeInt_nLx <- age2int(Age_nLx,OAvalue = 1) -#' +#' +#' #' Pop_fit <- groupAges(Pop80, Age, N = 10)[c("60","70")] #' Age_fit <- c(60,70) #' AgeInt_fit <- c(10,10) -#' +#' #' Standard <- OPAG_fit_stable_standard( -#' Pop_fit, -#' Age_fit, -#' AgeInt_fit, -#' nLx = nLx, -#' Age_nLx = Age_nLx, -#' AgeInt_nLx = AgeInt_nLx, -#' method = "uniform", -#' continuous = TRUE) -#' +#' Pop_fit = Pop_fit, +#' Age_fit = Age_fit, +#' AgeInt_fit = AgeInt_fit, +#' Lx1=Lx1, +#' Age_Lx1 = Age_Lx1 +#' ) +#' #' # A visual comparison: #' nL60 <- rescale_vector(nLx[Age_nLx >= 60]) -#' St60p <- rescale_vector( Standard$Standard[Age_nLx >= 60] ) +#' St60p <- rescale_vector( Standard$Standard[c(0:100) >= 60] ) #' ages_plot <- seq(60,100,by=5) #' \dontrun{ -#' plot(ages_plot,nL60, type = 'l') -#' lines(ages_plot, St60p, col = "blue") +#' plot(ages_plot,nL60, type = 'l') +#' lines(60:100, St60p, col = "blue") #' } OPAG_fit_stable_standard <- function(Pop_fit, Age_fit, AgeInt_fit, - nLx, - Age_nLx, - AgeInt_nLx, - method = "uniform", - continuous = TRUE){ - - + Lx1, + Age_Lx1 +){ + + # optimize the parameter r r_opt <- optimize(OPAG_r_min, Pop_fit = Pop_fit, Age_fit = Age_fit, AgeInt_fit = AgeInt_fit, - nLx = nLx, - Age_nLx = Age_nLx, - interval = c(-0.05, .05)) - - - standard <- OPAG_nLx_warp_r(nLx = nLx, - Age = Age_nLx, - r = r_opt$min, - AgeInt = AgeInt_nLx, - continuous = continuous, - method = method) + Lx1 = Lx1, + Age_Lx1 = Age_Lx1, + interval = c(-0.02, .05)) # changed interval + + + standard <- OPAG_nLx_warp_r(Lx1 = Lx1, + Age_Lx1 = Age_Lx1, + r = r_opt$min + ) # return both stable standard and the optimization output, # which will let us know if r is simply unreasonable or similar. out <- list(Standard = standard, @@ -373,44 +340,39 @@ OPAG_fit_stable_standard <- function(Pop_fit, #' @details It may be helpful to try more than one fitting possibility, #' and more than one `Redistribute_from` cut point, as results may vary. #' -#' The argument `"method"` refers to which graduation method (see `?graduate`) -#' is only relevant if input data are in grouped ages. This is innocuous if -#' ages are single to begin with. The choice of whether to assume -#' `continuous = TRUE` constant growth versus geometric (`FALSE`) growth -#' has little leverage. -#' #' `Redistribute_from` can be lower than your current open age group, #' and `OAnew` can be higher, as long as it is within the range of `Age_nLx`. #' If `Age_nLx` doesn't go high enough for your needs, you can extrapolate -#' it ahead of time. For this, you'd want the `nMx` the underly it, and you +#' it ahead of time. For this, you'd want the `nMx` the underlie it, and you #' can use `lt_abridged()`, specifying a higher open age, and then #' extracting `nLx` again from it. #' #' @inheritParams OPAG_r_min #' @param Pop numeric vector of population counts #' @param Age_Pop integer vector of the lower bounds of the population age groups -#' @param AgeInt_Pop integer vector of the population age group interval widths, using `Inf` for the open age group. +#' @param nLx numeric vector of stationary population age structure in arbitrary integer age groups +#' @param Age_nLx integer vector of lower bounds of age groups of `nLx` #' @param Redistribute_from integer lower age bound that forms the cutoff, above which we redistribute counts using the stable standard. #' @param OAnew integer. Desired open age group in the output (must being element of `Age_nLx`) +#' @param method character, graduation method used for intermediate graduation. Default `"mono"`. Other reasonable choices include `"pclm"` or `"uniform"`. #' @export +#' @importFrom utils tail #' @examples -#' # India Males, 1991 +#' # India Males, 1971 #' Pop <- smooth_age_5(pop1m_ind, #' Age = 0:100, #' method = "Arriaga") #' Age_Pop <- names2age(Pop) #' AgeInt_Pop <- age2int(Age_Pop, OAvalue = 1) #' -#' nLx <- downloadnLx(NULL, "India","male",1991) +#' nLx <- downloadnLx(NULL, "India","male",1971) #' Age_nLx <- names2age(nLx) #' AgeInt_nLx <- age2int(Age_nLx, OAvalue = 1) #' #' Pop_fit <- OPAG(Pop, #' Age_Pop = Age_Pop, -#' AgeInt_Pop = AgeInt_Pop, #' nLx = nLx, #' Age_nLx = Age_nLx, -#' AgeInt_nLx, #' Age_fit = c(60,70), #' AgeInt_fit = c(10,10), #' Redistribute_from = 80) @@ -428,17 +390,15 @@ OPAG_fit_stable_standard <- function(Pop_fit, OPAG <- function(Pop, Age_Pop, - AgeInt_Pop, - nLx, - Age_nLx, - AgeInt_nLx = NULL, + nLx, + Age_nLx, Age_fit = NULL, AgeInt_fit = NULL, Redistribute_from = max(Age_Pop), OAnew = max(Age_nLx), - method = "uniform", - continuous = TRUE){ - + method = "mono" +){ + # ensure OAnew is possible stopifnot(OAnew <= max(Age_nLx)) @@ -450,21 +410,38 @@ OPAG <- function(Pop, if(!identical(as.integer(unique(diff(Age_Pop))), as.integer(unique(diff(Age_nLx))))){ # put a different cat("\nAge_Pop and Age_nLx age intervals are different!\n") } - # what if one age vec is single and the other isn't? we should warn, but continue. - # is_single(Age_Pop) - # is_abridged(Age_Pop) + + # PJ adds this. Note final age group not assigned a width + AgeInt_Pop <- diff(Age_Pop) + # AgeInt_nLx <- diff(Age_Pop) # setup, prelims: # 0) if Age_fit isn't given assume last two 10-year age groups. + if (is.null(Age_fit)){ OA <- max(Age_Pop) Age_fit <- OA - c(20,10) AgeInt_fit <- c(10,10) stopifnot(Age_fit %in% Age_Pop) } - + if (is.null(AgeInt_fit)){ + # assume age intervals are age differences, and repeat last one + AgeInt_fit <- diff(Age_fit) + AgeInt_fit <- c(AgeInt_fit, tail(AgeInt_fit, n=1)) + # if Age_fit includes pop OA then set last fit age int to Inf + if (tail(Age_fit,1) == tail(Age_Pop,1)) { + AgeInt_fit[length(AgeInt_fit)] <- Inf + } + } + if (any(!Age_fit %in% Age_Pop)){ + ind <- Age_fit %in% Age_Pop + Age_fit <- Age_fit[ind] + AgeInt_fit <- AgeInt_fit[ind] + stopifnot(length(Age_fit) > 1) + } + # 1) get Pop_fit - + # TR: note: this calls for a special age utility function I think # earmarking this code chunk to swap it out in the future. Pop_fit <- rep(NA, length(Age_fit)) @@ -472,47 +449,55 @@ OPAG <- function(Pop, ind <- Age_Pop >= Age_fit[i] & Age_Pop < (Age_fit[i] + AgeInt_fit[i]) Pop_fit[i] <- sum(Pop[ind]) } - - # 2) get the standard + + # 2) make sure Lx is single ages + Lx1 <- graduate(nLx, Age_nLx, method = method, constrain = TRUE) + Age_Lx1 <- as.integer(names(Lx1)) + Stab_stand <- OPAG_fit_stable_standard(Pop_fit, Age_fit, AgeInt_fit, - nLx, - Age_nLx, - AgeInt_nLx, - method = method, - continuous = continuous) + Lx1, + Age_Lx1 + ) StPop <- Stab_stand$Standard - + # 3) get total to redistribute: OAG_total <- sum(Pop[Age_Pop >= Redistribute_from]) - + # 4) select standard in those age groups. - StPop_sel <- StPop[Age_nLx >= Redistribute_from] + StPop_sel <- StPop[Age_Lx1 >= Redistribute_from] StPop_sel <- rescale_vector(StPop_sel, scale = 1) - + # 5) redistribute Pop_redistributed <- StPop_sel * OAG_total - + + # 5a) regroup into original pop age grouping + # TR: possibly not the bes tuse of AgeInt_Pop.. + if (tail(AgeInt_Pop, n=2)[-1] == 5) { + Pop_redistributed <- groupAges(Pop_redistributed, N = 5) + } + # 6) graft together Pop_grafted <- c(Pop[Age_Pop < Redistribute_from], Pop_redistributed) Age_grafted <- c(Age_Pop[Age_Pop < Redistribute_from], Age_nLx[Age_nLx >= Redistribute_from]) - + + names(Pop_grafted) <- Age_grafted # 7) potentially group down OAG Pop_out <- groupOAG(Value = Pop_grafted, Age = Age_grafted, OAnew = OAnew) Age_out <- names2age(Pop_out) - + # 8) compose list for output out <- list( - Pop_out = Pop_out, - Age_out = Age_out, - Pop_in = Pop, - Standard = StPop, - r_opt = Stab_stand$r_opt) - + Pop_out = Pop_out, + Age_out = Age_out, + Pop_in = Pop, + Standard = StPop, + r_opt = Stab_stand$r_opt) + out } diff --git a/R/basepop.R b/R/basepop.R index 46d1e27fd..aa5078522 100644 --- a/R/basepop.R +++ b/R/basepop.R @@ -141,8 +141,8 @@ #' * `nLxm` numeric matrix of male `nLx`, abridged ages in rows and (potentially interpolated) time in columns. Potentially downloaded. #' * `Asfr` numeric matrix of age specific fertility in 5-year age groups ages 15-19 until 45-49 in rows, and (potentially interpolated) time in columns. Potentially downloaded. #' * `Exposure_female` numeric matrix of approximated age-specific exposure in 5-year age groups ages 15-19 until 45-49 in rows, and (potentially interpolated) time in columns. -#' * `Bt` births at three time points prior to census corrsponding to the midpoints of the cohorts entering ages 0, 1-4, and 5-9. -#' * `SRB` sex ratio at birth at three time points prior to census corrsponding to the midpoints of the cohorts entering ages 0, 1-4, and 5-9. Potentially downloaded. +#' * `Bt` births at three time points prior to census corresponding to the midpoints of the cohorts entering ages 0, 1-4, and 5-9. +#' * `SRB` sex ratio at birth at three time points prior to census corresponding to the midpoints of the cohorts entering ages 0, 1-4, and 5-9. Potentially downloaded. #' * `Age` age groups of the input population counts. #' # #' `basepop_single` is used, the return value is a numeric vector with diff --git a/R/check_heaping.R b/R/check_heaping.R index 5fa8949d1..9f80e5eae 100644 --- a/R/check_heaping.R +++ b/R/check_heaping.R @@ -504,7 +504,7 @@ check_heaping_spoorenberg <- function(Value, #' @param Agei integer. The age on which the index is centered. #' @param pow either \code{"exp"} (default) or a power such as 2. See details #' -#' @details The index looks down two ages and up two ages, so the data must accommodate that range. The denominator is a mean of the counts in the sourrounding 5 single ages. The kind of mean can be controlled with the \code{pow} argument. By default, this takes the antilog of the arithmetic mean of the natural log of the five denominator counts. That will fail if one of the counts is equal to 0. In such cases, another power, such as 2 or 10 or 100 may be used, which is more robust to 0s. The higher the power, the closer the result will resemble the default output. If \code{pow=="exp"} but a 0 is detected among the denominator ages, then \code{pow} is assigned a value of 1000. \code{pow=1} would imply an arithmetic mean in the denominator. +#' @details The index looks down two ages and up two ages, so the data must accommodate that range. The denominator is a mean of the counts in the surrounding 5 single ages. The kind of mean can be controlled with the \code{pow} argument. By default, this takes the antilog of the arithmetic mean of the natural log of the five denominator counts. That will fail if one of the counts is equal to 0. In such cases, another power, such as 2 or 10 or 100 may be used, which is more robust to 0s. The higher the power, the closer the result will resemble the default output. If \code{pow=="exp"} but a 0 is detected among the denominator ages, then \code{pow} is assigned a value of 1000. \code{pow=1} would imply an arithmetic mean in the denominator. #' @return The value of the index. #' #' @references @@ -679,13 +679,13 @@ heapify <- function(Value, #' Detect if heaping is worse on terminal digits 0s than on 5s #' -#' @description Ages ending in 0 often have higher apparent heaping than ages ending in 5. In this case, data in 5-year age bins might show a sawtooth pattern. If heaping ocurrs in roughly the same amount on 0s and 5s, then it may be sufficient to group data into 5-year age groups and then graduate back to single ages. However, if heaping is worse on 0s, then this procedure tends to produce a wavy pattern in count data, with 10-year periodicity. In this case it is recommended to use one of the methods of \code{smooth_age_5()} as an intermediate step before graduation. +#' @description Ages ending in 0 often have higher apparent heaping than ages ending in 5. In this case, data in 5-year age bins might show a sawtooth pattern. If heaping occurs in roughly the same amount on 0s and 5s, then it may be sufficient to group data into 5-year age groups and then graduate back to single ages. However, if heaping is worse on 0s, then this procedure tends to produce a wavy pattern in count data, with 10-year periodicity. In this case it is recommended to use one of the methods of \code{smooth_age_5()} as an intermediate step before graduation. #' #' @details Data is grouped to 5-year age bins. The ratio of each value to the average of its neighboring values is calculated. If 0s have stronger attraction than 5s then we expect these ratios to be >1 for 0s and <1 for 5s. Ratios are compared within each 10-year age group in the evaluated age range. If in the evaluated range there are at most two exceptions to this rule (0s>5s), then the ratio of the mean of these ratios is returned, and it is recommended to use a smoother method. Higher values suggest use of a more aggressive method. This approach is only slightly different from that of Feeney, as implemented in the \code{smooth_age_5_zigzag_inner()} functions. This is not a general measure of roughness, but rather an indicator of this particular pattern of age attraction. #' @export #' @inheritParams heapify #' @param ageMin integer evenly divisible by 10. Lower bound of evaluated age range, default 40. -#' @param ageMax integer evently divisibly by 5. Upper bound of evaluated age range, defaults to highest age evenly divisible by 10. +#' @param ageMax integer evenly divisible by 5. Upper bound of evaluated age range, defaults to highest age evenly divisible by 10. #' @return numeric, ratio of 0s to 5s. If > 1 then the pattern is present. #' @references #' \insertRef{feeney1979}{DemoTools} @@ -762,7 +762,7 @@ check_heaping_sawtooth <- } #' Evaluate roughness of data in 5-year age groups -#' @description For a given age-structured vector of counts, how rough is data after grouping to 5-year age bins? Data may require smoothing even if there is no detectable sawtooth pattern. It is best to use the value in this method together with visual evidence to gauage whether use of \code{smooth_age_5()} is recommended. +#' @description For a given age-structured vector of counts, how rough is data after grouping to 5-year age bins? Data may require smoothing even if there is no detectable sawtooth pattern. It is best to use the value in this method together with visual evidence to gauge whether use of \code{smooth_age_5()} is recommended. #' @details First we group data to 5-year age bins. Then we take first differences (d1) of these within the evaluated age range. Then we smooth first differences (d1s) using a generic smoother (\code{ogive()}). Roughness is defined as the mean of the absolute differences between \code{mean(abs(d1 - d1s) / abs(d1s))}. Higher values indicate rougher data, and may suggest more aggressive smoothing. Just eyeballing, one could consider smoothing if the returned value is greater than ca 0.2, and values greater than 0.5 already highly recommend it (pending visual verification). #' @export #' @inheritParams check_heaping_sawtooth diff --git a/R/data.R b/R/data.R index 6717eb73e..5bfbf3126 100644 --- a/R/data.R +++ b/R/data.R @@ -21,9 +21,9 @@ -#' Indian male population 1991 +#' Indian male population 1971 #' -#' Indian male population 1991 +#' Indian male population 1971 #' @docType data #' @format #' A numeric vector of length 101 @@ -200,7 +200,7 @@ #' A data frame with: #' \describe{ #' \item{Date}{Reference time for the rates estimate.} -#' \item{Age}{Inferior age for abridged groups. Carefull: last age 100 is not an OAG} +#' \item{Age}{Inferior age for abridged groups. Careful: last age 100 is not an OAG} #' \item{Sex}{Male \code{m} and female \code{m}.} #' \item{nMx}{Mortality rates.} #' } @@ -230,7 +230,7 @@ #' A matrix of dimensions 21 x 21 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "pop_m_mat_five" #' Population matrix for females five year age groups between 1950 and 2050 @@ -242,7 +242,7 @@ #' A matrix of dimensions 21 x 21 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "pop_f_mat_five" #' Survival rates matrix for males five year age groups between 1950 and 2045 @@ -254,7 +254,7 @@ #' A matrix of dimensions 21 x 20 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "sr_m_mat_five" #' Survival rates matrix for females five year age groups between 1950 and 2045 @@ -266,7 +266,7 @@ #' A matrix of dimensions 21 x 20 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "sr_f_mat_five" #' Age-specific fertility rates for age groups 15 to 45 between 1950 and 2045 @@ -278,7 +278,7 @@ #' A matrix of dimensions 7 x 20 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "asfr_mat_five" #' Sex ratio at birth between 1950 and 2045 @@ -289,7 +289,7 @@ #' A vector of length 20 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "srb_vec_five" #' Ages between 0 and 100 abridged in five year age groups @@ -301,7 +301,7 @@ #' A vector of length 21 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "ages_five" #' Ages between 15 and 45 in five year age groups @@ -313,7 +313,7 @@ #' A vector of length 7 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "ages_asfr_five" #' Population matrix for males single ages between 1999 and 2019 @@ -325,7 +325,7 @@ #' A matrix of dimensions 101 x 21 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "pop_m_mat_single" #' Population matrix for females single ages between 1999 and 2019 @@ -337,7 +337,7 @@ #' A matrix of dimensions 101 x 21 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "pop_f_mat_single" #' Survival rates matrix for males single ages between 1999 and 2019 @@ -350,7 +350,7 @@ #' A matrix of dimensions 101 x 20 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "sr_m_mat_single" #' Survival rates matrix for females single ages between 1999 and 2019 @@ -363,7 +363,7 @@ #' A matrix of dimensions 101 x 20 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "sr_f_mat_single" #' Age-specific fertility rates for single ages 15 to 49 between 1999 and 2018 @@ -376,7 +376,7 @@ #' A matrix of dimensions 35 x 20 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "asfr_mat_single" #' Sex ratio at birth between 1999 and 2019 @@ -388,7 +388,7 @@ #' A vector of length 20 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "srb_vec_single" #' Single ages between 0 and 100 @@ -399,7 +399,7 @@ #' A vector of length 101 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "ages_single" #' Single ages between 15 and 49 @@ -410,7 +410,7 @@ #' A vector of length 36 #' #' @source -#' Migration residual PAS spreadhseet +#' Migration residual PAS spreadsheet "ages_asfr_single" #' Parameters for considered migration profiles diff --git a/R/extra_mortality.R b/R/extra_mortality.R index 6e8ed4d4d..9f374178e 100644 --- a/R/extra_mortality.R +++ b/R/extra_mortality.R @@ -26,12 +26,13 @@ #' @param opt.method character. Default `"LF2"`, see `MortalityLaws::MortalityLaw` for a description of choices. #' @param ... Other arguments to be passed on to the #' \code{\link[MortalityLaws]{MortalityLaw}} function. +#' @details If fitting fails to converge, then we refit assuming Gompertz mortality with explicit starting parameters of `parS = c(A = 0.005, B = 0.13)` and a warning is issued. #' @seealso #' \code{\link[MortalityLaws]{MortalityLaw}} #' \code{\link[MortalityLaws]{predict.MortalityLaw}} #' @return An object of class \code{lt_rule_m_extrapolate} with the following components: #' \item{input}{List with arguments provided in input. Saved for convenience.} -#' \item{call}{An unevaluated function call, that is, an unevaluated expressionwhich consists of the named function applied to the given arguments.} +#' \item{call}{An unevaluated function call, that is, an unevaluated expression that consists of the named function applied to the given arguments.} #' \item{fitted.model}{An object of class \code{\link[MortalityLaws]{MortalityLaw}}. Here one can find fitted values, residuals, goodness of fit measures etc.} #' \item{values}{A vector or matrix containing the complete mortality data, that is the modified input data following the extrapolation procedure.} #' @@ -53,7 +54,8 @@ #' f2 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "kannisto_makeham") #' f3 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "gompertz") #' f4 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "ggompertz") -#' f5 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "makeham") +#' # makeham falls back to gompertz for this data +#' suppressWarnings(f5 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "makeham")) #' f6 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "beard") #' f7 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "beard_makeham") #' f8 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "quadratic") @@ -125,17 +127,7 @@ #' lty = c(NA, NA, 1:8), pch = c(16, 16, rep(NA, 8)), #' col = c(1, 4, 2:9), lwd = 2, pt.cex = 2) #'} -#' # ---------------------------------------------- -#' # Example 3 - Extrapolate mortality for multiple years at once -#' -#' # Create some data -#' mx_matrix <- matrix(rep(mx1, 3), ncol = 3) %*% diag(c(1, 1.05, 1.1)) -#' dimnames(mx_matrix) <- list(age = x1, year = c("year1", "year2", "year3")) -#' -#' F1 <- lt_rule_m_extrapolate(mx_matrix, x = x1, x_fit, x_extr, law = "kannisto") -#' F1 -#' ls(F1) -#' coef(F1) + #' @author Marius D. Pascariu #' @export lt_rule_m_extrapolate <- function(mx, @@ -145,7 +137,10 @@ lt_rule_m_extrapolate <- function(mx, law = "kannisto", opt.method = "LF2", ...) { - + dm <- dim(mx) + if (length(dm) == 1 | (length(mx) == 2 & any(dm == 1))){ + mx <- c(mx) + } all_the_laws_we_care_about <- c("kannisto", "kannisto_makeham", "makeham", @@ -171,10 +166,36 @@ lt_rule_m_extrapolate <- function(mx, opt.method = opt.method, ... ) - + + if (!is.null(M$opt.diagnosis)){ + if( M$opt.diagnosis$convergence != 0){ + warning("Extrapolation failed to converge\nFalling back to Gompertz with starting parameters:\n parS = c(A = 0.005, B = 0.13))", + immediate. = TRUE) + + parS <- c(A = 0.005, B = 0.13) + law <- "gompertz" + M <- MortalityLaw( + x = x, + mx = mx, + fit.this.x = x_fit, + law = law, + parS = parS, + ...) + } + } + + # TR: this will fail if a matrix is given where we only x_extr for 1 age. + chop <- FALSE + if (length(x_extr) == 1){ + chop <- TRUE + x_extr <- c(x_extr, x_extr + 1) + } pv <- predict(object = M, x = x_extr) - + if (chop){ + pv <- pv[1] + x_extr <- x_extr[1] + } # which ages are not to be replaced with fitted values? L <- !(x %in% x_extr) diff --git a/R/graduate.R b/R/graduate.R index b5da87b99..cc95772c0 100644 --- a/R/graduate.R +++ b/R/graduate.R @@ -11,7 +11,7 @@ #' #' @return Numeric vector of counts for single year age groups. #' -#' @details Assumes that the population is uniformly distributed across each age interval, and that initial age intervals are integers greater than or equal to 1. If \code{AgeInt} is given, its final value is used as the interval for the final age group. If \code{AgeInt} is missing, then \code{Age} must be given, and the open age group is by default preserved \code{OAvalue} rather than split. To instead split the final age group into, e.g., a 5-year age class, either give \code{AgeInt}, *or* give \code{Age}, \code{OAG = TRUE}, and \code{OAvalue = 5}. +#' @details Assumes that the population is uniformly distributed across each age interval, and that initial age intervals are integers greater than or equal to 1. If \code{AgeInt} is given, its final value is used as the interval for the final age group. If \code{AgeInt} is missing, then \code{Age} must be given, and the open age group is by default preserved \code{OAvalue} rather than split. To instead split the final age group into, e.g., a 5-year age class, either give \code{AgeInt}, *or* give \code{Age}, \code{OAG = TRUE}, and \code{OAvalue = 5}. `Age` be any age range, it does not need to start at 0. #' #' @export #' @examples @@ -52,7 +52,7 @@ graduate_uniform <- #' @description This method is used to interpolate counts based on the Sprague formula. It is based on the first stage of the Sprague R script prepared by Thomas Buettner and Patrick Gerland, itself based on the description in Siegel and Swanson, 2004, p. 727. #' #' @inheritParams graduate -#' @details Ages should refer to lower age bounds, ending in the open age group in the last row (not a closed terminal age). Dimension labelling is necessary. There must be at least six age groups (including the open group). One year of data will work as well, as long as it's given as or coercible to a single-column matrix. This method may produce negative values, most likely in the youngest or oldest ages. This case is dealt with in the \code{graduate()} wrapper function but not in this function. +#' @details Ages should refer to lower age bounds, ending in the open age group in the last row (not a closed terminal age). Dimension labeling is necessary. There must be at least six age groups (including the open group). One year of data will work as well, as long as it's given as or coercible to a single-column matrix. This method may produce negative values, most likely in the youngest or oldest ages. This case is dealt with in the \code{graduate()} wrapper function but not in this function. #' #' If the highest age does not end in a 0 or 5, and \code{OAG == TRUE}, then the open age will be grouped down to the next highest age ending in 0 or 5. If the highest age does not end in a 0 or 5, and \code{OAG == FALSE}, then results extend to single ages covering the entire 5-year age group. #' @@ -203,6 +203,8 @@ graduate_sprague_expand <- function( # get the split coefficients # block for ages 0-9 + + # TR: 5-5-2021, this assumes ages start at 0... g1g2 <- matrix( c( 0.3616, -0.2768, 0.1488, -0.0336, 0.0000, 0.2640, -0.0960, 0.0400, @@ -387,7 +389,7 @@ graduate_grabill_expand <- function(Value, Age, OAG = TRUE) { #' Sprague estimated single-age population counts for the first and final ten ages. Open age groups are preserved, as are annual totals. #' #' @inheritParams graduate -#' @details Dimension labelling is necessary. There must be at least six age groups (including the open group). One year of data will work as well, as long as it's given as a single-column matrix. Data may be given in either single or grouped ages. If the highest age does not end in a 0 or 5, and \code{OAG == TRUE}, then the open age will be grouped down to the next highest age ending in 0 or 5. If the highest age does not end in a 0 or 5, and \code{OAG == FALSE}, then results extend to single ages covering the entire 5-year age group. +#' @details Dimension labeling is necessary. There must be at least six age groups (including the open group). One year of data will work as well, as long as it's given as a single-column matrix. Data may be given in either single or grouped ages. If the highest age does not end in a 0 or 5, and \code{OAG == TRUE}, then the open age will be grouped down to the next highest age ending in 0 or 5. If the highest age does not end in a 0 or 5, and \code{OAG == FALSE}, then results extend to single ages covering the entire 5-year age group. #' #' @return numeric vector in single ages. #' @@ -716,7 +718,7 @@ graduate_beers_expand <- function(Value, #' @inheritParams graduate #' @param method character. Valid values are `"ord"` or `"mod"`. Default `"ord"`. #' @param johnson logical. Whether or not to adjust young ages according to the \href{https://www.census.gov/data/software/dapps.html}{Demographic Analysis & Population Projection System Software} method. Default `FALSE.` -#' @details Ages should refer to lower age bounds. `Value` must be labelled with ages unless `Age` is given separately. There must be at least six 5-year age groups (including the open group, 5 otherwise). If you want the `johnson` adjustment then `Value` must contain a single-year estimate of the population count in age 0. That means `Value` must come either as standard abridged or single age data. +#' @details Ages should refer to lower age bounds. `Value` must be labeled with ages unless `Age` is given separately. There must be at least six 5-year age groups (including the open group, 5 otherwise). If you want the `johnson` adjustment then `Value` must contain a single-year estimate of the population count in age 0. That means `Value` must come either as standard abridged or single age data. #' #' `method` option `"ord"` conserves sums in 5-year age groups, whereas `"mod"` does some smoothing between 5-year age groups too, and is not constrained. #' @@ -893,6 +895,8 @@ graduate_beers_johnson <- function(Age0, pop5, pop1) { #' @details The PCLM method can also be used to graduate rates using an offset if both numerators and denominators are available. In this case \code{Value} is the event count and \code{offset} is person years of exposure. The denominator must match the length of \code{Value} or else the length of the final single age result \code{length(min(Age):OAnew)}. This method can be used to redistribute counts in the open age group if \code{OAnew} gives sufficient space. Likewise, it can give a rate extrapolation beyond the open age. #' #' If there are 0s in `Value`, these are replaced with a small value prior to fitting. If negatives result from the pclm fit, we retry after multiplying `Value` by 10, 100, or 1000, as sometimes a temporary rescale for fitting can help performance. +#' +#' `Age` be any age range, it does not need to start at 0. #' #' @inheritParams graduate #' @param ... further arguments passed to \code{ungroup::pclm()} @@ -1004,7 +1008,7 @@ graduate_pclm <- function(Value, Age, AgeInt, OAnew = max(Age), OAG = TRUE, ...) #' Graduate age groups using a monotonic spline. #' @description Take the cumulative sum of \code{Value} and then run a monotonic spline through it. The first differences split back single-age estimates of \code{Value}. Optionally keep the open age group untouched. #' -#' @details The \code{"hyman"} method of \code{stats::splinefun()} is used to fit the spline because 1) it passes exactly through the points, 2) it is monotonic and therefore guarantees positive counts, and 3) it seems to be a bit less wiggly (lower average first differences of split counts). Single-age data is returned as-is. If you want to use this function as a smoother you first need to group to non-single ages. +#' @details The \code{"hyman"} method of \code{stats::splinefun()} is used to fit the spline because 1) it passes exactly through the points, 2) it is monotonic and therefore guarantees positive counts, and 3) it seems to be a bit less wiggly (lower average first differences of split counts). Single-age data is returned as-is. If you want to use this function as a smoother you first need to group to non-single ages. `Age` be any age range, it does not need to start at 0. #' @inheritParams graduate #' @return Numeric. vector of single smoothed age counts. #' @importFrom stats splinefun @@ -1069,9 +1073,9 @@ graduate_mono <- function( # if the final age is Open, then we should remove it and then # stick it back on - AgePred <- c(min(Age), cumsum(AgeInt)) + AgePred <- c(min(Age), cumsum(AgeInt) + min(Age)) y <- c(0, cumsum(Value)) - AgeS <- min(Age):sum(AgeInt) + AgeS <- min(Age):(sum(AgeInt)+ min(Age)) # TR: changed from monoH.FC to hyman 3.3.2021 y1 <- splinefun(y ~ AgePred, method = "hyman")(AgeS) out <- diff(y1) @@ -1119,7 +1123,7 @@ graduate_mono <- function( #' # one may wish to instead rescale results colSums() of #' # popg at age pivotAge and higher. #' sum(grabill.closed.out) - sum(popvec) -#' # also works on an age-labelled vector of data +#' # also works on an age-labeled vector of data #' closed.vec <- graduate_mono_closeout(popvec, Age = a5, OAG = TRUE) #' # let's compare this one with sprague() @@ -1218,7 +1222,7 @@ graduate_mono_closeout <- #' #' \code{OAnew} cannot be higher than \code{max(Age)+4} for \code{"sprague"} or \code{"beers"} methods. For \code{"uniform","mono","pclm"} it can be higher than this, and in each case the open age group is completely redistributed within this range, meaning it's not really open anymore. #' -#' For all methods, negative values are detected in output. If present, we deal with these in the following way: we take the geometric mean between the given output (with negative imputed with 0s) and the output of \code{graduate_mono()}, which is guaranteed non-negative. This only affects age groups where negatives were produced in the first pass. In our experience this only arises when using sprague, beers, or grabill methods, whereas all others are guarateed non-negative. +#' For all methods, negative values are detected in output. If present, we deal with these in the following way: we take the geometric mean between the given output (with negative imputed with 0s) and the output of \code{graduate_mono()}, which is guaranteed non-negative. This only affects age groups where negatives were produced in the first pass. In our experience this only arises when using Sprague, Beers, or Grabill methods, whereas all others are guaranteed non-negative. #' #' For any case where input data are in single ages, constraining results to sum to values in the original age groups will simply return the original input data, which is clearly not your intent. This might arise when using graduation as an implicit two-step smoother (group + graduate). In this case, separate the steps, first group using \code{groupAges()} then use \code{graduate(..., constrain = TRUE)}. #' @@ -1420,7 +1424,7 @@ graduate <- function(Value, } n <- length(out) - a1 <- min(Age):(n - 1) + a1 <- min(Age):(min(Age) + n - 1) # detect negatives. Have default option to replace. # Favor quick over perfect, since this only can arise diff --git a/R/interp_coh.R b/R/interp_coh.R index b23df2a76..78fd02c4f 100644 --- a/R/interp_coh.R +++ b/R/interp_coh.R @@ -27,34 +27,34 @@ shift_census_ages_to_cohorts <- function(pop, date, censusYearOpt = "frac", OAG = TRUE){ - - + + stopifnot(is_single(age)) - + date <- dec.date(date) yr <- floor(date) f1 <- date - yr - + if (OAG){ N <- length(pop) pop <- pop[-N] age <- age[-N] } - + if (is.na(censusYearOpt)){ censusYearOpt <- "NA" } - + upper_part_of_cohort <- pop * f1 lower_part_of_cohort <- pop * (1 - f1) - + shift <- ceiling(f1) pop_out <- shift.vector(lower_part_of_cohort,shift) + upper_part_of_cohort - + cohorts <- yr - age - 1 + shift - + age_out <- round(f1) + age - + if (censusYearOpt == "drop"){ pop_out <- pop_out[-1] age_out <- age_out[-1] @@ -68,7 +68,7 @@ shift_census_ages_to_cohorts <- function(pop, if (censusYearOpt == "NA"){ pop_out[1] <- NA_real_ } - + list(cohort_size = pop_out, birth_year = cohorts, age = age_out, @@ -76,8 +76,8 @@ shift_census_ages_to_cohorts <- function(pop, f1 = f1) } -#' component-free intercensal cohort interpolation -#' @description Cohorts between two censuses are interpolated flexibly using linear, exponential, or power rules. The lower and upper intercensal triangles are filled using within-age interpolation. This function is experimental and still in development. +#' Cohort component intercensal interpolation +#' @description Cohorts between two censuses are interpolated using a cohort component approach. #' @seealso interp #' @param c1 numeric vector. The first (left) census in single age groups #' @param c2 numeric vector. The second (right) census in single age groups @@ -96,6 +96,7 @@ shift_census_ages_to_cohorts <- function(pop, #' @param midyear logical. `FALSE` means all Jan 1 dates between `date1` and `date2` are returned. `TRUE` means all July 1 intercensal dates are returned. #' @param verbose logical. Shall we send informative messages to the console? #' @param ... optional arguments passed to +#' @details The basic approach is to i) align the censuses to single-year cohorts by blending adjacent ages assuming that the birthdays in each age group are uniformly distributed through the year ii) decrement the first census forward within cohorts using period-cohort survival probabilities calculated from (supplied or downloaded) `l(x)` values, iii) redistribute the residual at the time of the second census uniformly over time within cohorts. These steps are always done on Jan 1 reference dates. If `midyear = TRUE`, then we do within-age band arithmetic interpolation to July 1 reference dates. #' @export #' @importFrom data.table := as.data.table melt data.table dcast between #' @examples @@ -113,29 +114,29 @@ shift_census_ages_to_cohorts <- function(pop, #' ) #' } interp_coh <- function( - c1, - c2, - date1, - date2, - age1 = 1:length(c1) - 1, - age2 = 1:length(c2) - 1, - dates_out = NULL, - lxMat = NULL, - age_lx = NULL, - dates_lx = NULL, - births = NULL, - years_births = NULL, - location = NULL, - sex = "both", - midyear = FALSE, - verbose = TRUE, - ... - ) { - + c1, + c2, + date1, + date2, + age1 = 1:length(c1) - 1, + age2 = 1:length(c2) - 1, + dates_out = NULL, + lxMat = NULL, + age_lx = NULL, + dates_lx = NULL, + births = NULL, + years_births = NULL, + location = NULL, + sex = "both", + midyear = FALSE, + verbose = TRUE, + ... +) { + # convert the dates into decimal numbers date1 <- dec.date(date1) date2 <- dec.date(date2) - + res_list <- rup( c1 = c1, c2 = c2, @@ -155,78 +156,78 @@ interp_coh <- function( verbose = verbose, ... = ... ) - + pop_jan1 <- res_list$pop_jan1 dates_out <- res_list$dates_out - + . <- NULL age <- NULL discount <- NULL pop_jan1_pre <- NULL resid <- NULL year <- NULL - + # add "cumulative" residual to the RUP (pop_jan1_pre) - pop_jan1[, `:=`(pop_jan1 = pop_jan1_pre + resid * discount)] + pop_jan1[, `:=`(pop_jan1 = pop_jan1 + resid * discount)] pop_jan1 <- pop_jan1[!is.na(cohort)] - + # TR: to get residualmigbeta prelim result, one takes the cumulative # resid (resid * discount), then decumulates it (within cohorts!), # then sum over age. boo ya Lexis - + PopAP <- pop_jan1 %>% .[, list(age, year, pop_jan1)] %>% data.table::dcast(age ~ year, value.var = "pop_jan1") %>% .[order(age)] - - + + matinterp <- PopAP[age <= max(age1), -1] %>% as.matrix() rownames(matinterp) <- age1 - + # Handle NAs perhaps c1 needs OPAG beforehand?) ind <- is.na(matinterp) if (any(ind) & verbose){ cat("\n",sum(ind),"NA detected in output.\nThese have been imputed with 0s.\nThis could happen in the highest ages,\nand you may consider extending the open ages of the census inputs?\n") matinterp[ind] <- 0 } - + # Handle negatives (small pops, or large negative residuals relative to pop size) ind <- matinterp < 0 if (any(ind) & verbose){ cat("\n",sum(ind),"negatives detected in output.\nThese have been imputed with 0s.\n") matinterp[ind] <- 0 } - + yrsIn <- as.numeric(colnames(matinterp)) if (all(yrsIn > date1)){ matinterp <- cbind(c1, matinterp) yrsIn <- c(date1, yrsIn) } - + if (all(yrsIn < date2)){ matinterp <- cbind(matinterp, c2[1:length(c2)]) yrsIn <- c(yrsIn, date2) } - + colnames(matinterp) <- yrsIn # now we either return Jan1 dates or July 1 dates. - + out <- interp( matinterp, datesIn = yrsIn, datesOut = as.numeric(dates_out), rule = 1 ) - + if (any(out < 0)) { if (verbose) { cat("\nSome of the interpolated values resulted to be negative, replacing with zeroes\n") #nolintr } - + out[out < 0] <- 0 } - + out } @@ -629,6 +630,8 @@ lt_a2s_chunk <- function(chunk, OAnew, ...){ # dplyr::bind_cols() %>% # as.matrix()) +# ZZZ FLAG for possible depreation + interp_coh_lxMat_pxt <- function(lxMat, dates_lx, age_lx, @@ -641,23 +644,23 @@ interp_coh_lxMat_pxt <- function(lxMat, # fixed. date1 <- dec.date(date1) date2 <- dec.date(date2) - + year1 <- floor(date1) + 1 year2 <- floor(date2) - + year_seq <- year1:year2 - + dates_out <- c(dec.date(date1), year_seq) - + # get ndx andnLx from lt_abridged() - + a1 <- 0:OAnew qx1 <- matrix(ncol = ncol(lxMat), nrow = length(a1), dimnames = list(a1, dates_lx)) for (i in 1:ncol(lxMat)){ - + if (is_abridged(age_lx)){ # LTA <- lt_abridged(Age = age_lx, # lx = lxMat[, i], @@ -671,46 +674,52 @@ interp_coh_lxMat_pxt <- function(lxMat, qx1[, i] <- LT1$nqx } else { qx <- lt_id_l_q(lxMat[, i]) - + LT1 <- lt_single_qx(nqx = qx, Age=1:length(qx)-1, OAnew = OAnew, ...) - - + + qx1[, i] <- LT1$nqx } - + } - + # We do linear interpolation of the logit-transformed qx. logit_qx <- log(qx1 / (1 - qx1)) - + logit_qx_interp <- interp( popmat = logit_qx, datesIn = dates_lx, datesOut = dates_out, - rule = 2) + rule = 2, + negatives = TRUE) # transform back QX <- exp(logit_qx_interp) / (1 + exp(logit_qx_interp)) - QX[nrow(QX), ] <- 1 - - + f1 <- diff(dates_out)[1] f2 <- date2 - floor(date2) - + + # get Sx (keep PX name) + PX <- apply(QX,2,function(q){lt_single_qx(nqx = q, + Age=a1, + OAnew = OAnew, + ...)$Sx}) + rownames(PX) <- rownames(QX) # assume linear px change within age class - PX <- 1 - QX + # PX <- 1 - QX + # IW: PX^f1 is not bad. Other option is PX=(1-QX*f). One assumes linear on d_x, the other constant mu_x. PX[,1] <- PX[, 1] ^f1 PX[,ncol(PX)] <- PX[, ncol(PX)] ^f2 - - + + PX } - +### ZZZ FLAG for redux and/or deprecation / name change transform_pxt <- function(lxMat, location, sex, @@ -721,20 +730,22 @@ transform_pxt <- function(lxMat, age_lx, age1, ...) { - + # get the lexis surface of survival probabilities if (is.null(lxMat)){ - pxt <- suppressMessages( + # ZZZ Note this already returns Sx. that was an easy fix. + pxt <- suppressMessages( interp_coh_download_mortality(location = location, sex = sex, date1 = date1, date2 = date2, - OAnew = max(age1) + 1, + OAnew = max(age1), verbose = verbose) ) } else { - + + ### FLAG This all needs to change. if (is.null(dates_lx)){ # if lx dates not given we assume dates evenly distributed from date1 to date2? dates_lx <- seq(date1,date2,length.out = ncol(lxMat)) @@ -742,10 +753,10 @@ transform_pxt <- function(lxMat, cat("lxMat specified, but not dates_lx\nAssuming:",paste(dates_lx,collapse=", "),"\n") } } - + available_dates <- data.table::between(dates_lx, date1, date2) if (!all(available_dates)) stop("All `dates_lx` must be within the range of `date1` and `date2`") - + # if the shortest distance from dates_lx to date1 or date2 is greater than 7 # warn dates_df <- expand.grid(dates_lx = dates_lx, dates = c(date1, date2)) @@ -761,20 +772,20 @@ transform_pxt <- function(lxMat, ") is greater than 7 years. Be wary." ) } - - ic_period <- date2 - date1 - lx_mm <- range(dates_lx) - overlap <- min(c(lx_mm[2], date2)) - c(max(lx_mm[1], date1)) - extrap_low <- lx_mm[1] - min(lx_mm[1],date1) + + ic_period <- date2 - date1 + lx_mm <- range(dates_lx) + overlap <- min(c(lx_mm[2], date2)) - c(max(lx_mm[1], date1)) + extrap_low <- lx_mm[1] - min(lx_mm[1],date1) extrap_high <- max(lx_mm[2],date2) - lx_mm[2] t1 <- overlap / ic_period < .25 t2 <- extrap_low > 6 t3 <- extrap_high > 6 if (any(c(t1, t2, t3))) cat("\nRange between `date1` and `date2` must overlap with `lx_dates` for at least 25% of the range or 6 years.\n") - + if (is.null(age_lx)){ if (nrow(lxMat) < 26){ - + N <- nrow(lxMat) age_lx <- c(0,1,seq(5,5*(N-2),by=5)) } else { @@ -784,7 +795,7 @@ transform_pxt <- function(lxMat, cat("lxMat specified, but Age_lx missing\nAssuming:",paste(age_lx,collapse=", "),"\n") } } - + # ensure lx fills timepoints. # would like to pass ... here for the lifetable part pxt <- interp_coh_lxMat_pxt( @@ -793,11 +804,11 @@ transform_pxt <- function(lxMat, age_lx = age_lx, date1 = date1, date2 = date2, - OAnew = max(age1) + 1, + OAnew = max(age1), control = list(deg = 3, lambda = 100), ...) } - + pxt } @@ -807,12 +818,12 @@ check_args <- function(lxMat, births, location, age1, age2, c1, c2, verbose) { stopifnot(length(age2) == length(c2)) stopifnot(is_single(age1)) stopifnot(is_single(age2)) - + if (length(age1) != length(age2) & verbose){ cat("\nFYI: age ranges are different for c1 and c2\nWe'll still get intercensal estimates,\nbut returned data will be chopped off after age", max(age1), "\n") } - - + + # If lxMat or births are missing -- message requiring location and sex if (is.null(lxMat) & is.null(location)) { stop("lxMat not specified, please specify location and sex\n") @@ -820,22 +831,22 @@ check_args <- function(lxMat, births, location, age1, age2, c1, c2, verbose) { if (is.null(births) & is.null(location)) { stop("births not specified, please specify location and sex\n") } - + if (!is.null(lxMat) && ncol(lxMat) == 1) { stop("lxMat should have at least two or more dates as columns. lxMat contains only one column") #nolintr } - + if (any(c1 < 0)) stop("No negative values allowed in `c1`") if (any(c2 < 0)) stop("No negative values allowed in `c2`") if (any(lxMat < 0)) stop("No negative values allowed in `lxMat`") - + } # If dates_out not given, then we resolve using the midyear argument. # If FALSE (default) we return intermediate Jan 1, not including c1 and c2 # If TRUE we return intermediate July 1 (.5) dates, not including c1 and c2 transform_datesout <- function(dates_out, date1, date2, midyear) { - + if (is.null(dates_out)){ if (! midyear){ # jan 1 dates @@ -854,28 +865,29 @@ transform_datesout <- function(dates_out, date1, date2, midyear) { dates_out <- dates_out[dates_out_lgl] } } - + dates_out } +### ZZZ FLAG for redux or deprecation reshape_pxt <- function( - pxt, - births, - c1, - c2, - age1, - age2, - date1, - date2, - f1, - f2, - yrs_births - ) { - + pxt, + births, + c1, + c2, + age1, + age2, + date1, + date2, + f1, + f2, + yrs_births +) { + # Since we're using data.table, we need to create these empty # variables to avoid having R CMD checks with no visible binding # for global variable. - + age <- NULL year <- NULL px <- NULL @@ -893,146 +905,150 @@ reshape_pxt <- function( discount <- NULL .N <- NULL . <- NULL - + px_triangles <- pxt %>% data.table::as.data.table(keep.rownames = "age") %>% data.table::melt( - id.vars = "age", - variable.name = "year", - value.name = "px", - variable.factor = FALSE - ) - + id.vars = "age", + variable.name = "year", + value.name = "px", + variable.factor = FALSE + ) + # No need for assignment: data.table assigns without creating a copy px_triangles[, `:=`(age = as.numeric(age), year = as.numeric(year), lower = magrittr::raise_to_power(px, 0.5), upper = magrittr::raise_to_power(px, 1 - 0.5))] - + px_triangles <- px_triangles[, list(age, year, lower, upper)] %>% data.table::melt( - id.vars = c("age", "year"), - measure.vars = c("lower", "upper"), - variable.name = "triangle", - value.name = "value", - variable.factor = FALSE - ) - + id.vars = c("age", "year"), + measure.vars = c("lower", "upper"), + variable.name = "triangle", + value.name = "value", + variable.factor = FALSE + ) + px_triangles[, `:=`(adj = ifelse(triangle == "upper", 1, 0))] px_triangles[, `:=`(cohort = magrittr::subtract(year, age) %>% magrittr::subtract(adj) %>% floor())] - + # cohort changes over the whole period # px_cum1 <- px_triangles[, list(n_triangles = .N, coh_p = prod(value)), # keyby = list(cohort)] - + # adjust the census population vectors c1c <- shift_census_ages_to_cohorts(c1, age1, date1, censusYearOpt = "frac") c2c <- shift_census_ages_to_cohorts(c2, age2, date2, censusYearOpt = "frac") - + # correction for the first year age 0 -- only take first for the remaining of # the year births[1] <- births[1] * (1 - f1) - + # correction for the last year age 0 n_yrs <- length(births) births[n_yrs] <- births[n_yrs] * f2 - + cohort_dt <- data.table::data.table( - cohort = yrs_births, - pop = births - ) - + cohort = yrs_births, + pop = births + ) + input <- data.table::data.table(cohort = c1c$birth_year, pop = c1c$cohort_size) %>% .[order(cohort)] %>% rbind(cohort_dt) %>% .[, list(pop = sum(pop)), keyby = list(cohort)] - + # population c2 observed pop_c2 <- data.frame( cohort = c2c$birth_year, pop_c2_obs = c2c$cohort_size ) - + pop_jan1_pre <- px_triangles %>% .[, list(n_triangles = .N, coh_p = prod(value)), keyby = list(year, cohort)] %>% .[order(cohort, year)] - + pop_jan1_pre[, `:=`(coh_lx = cumprod(coh_p)), keyby = list(cohort)] - + pop_jan1_pre <- pop_jan1_pre[input, on = "cohort"] - + pop_jan1_pre[, `:=`( - pop_jan1_pre = pop * coh_lx, + pop_jan1 = pop * coh_lx, age = floor(year) - cohort, year = floor(year) + 1 )] - + pop_jan1_pre[, `:=`(year = ifelse(year == max(year), year + f2 - 1, year))] - # calculate the discrepancy (migration) -- to be disrtibuted uniformly in # cohorts resid <- pop_jan1_pre %>% .[year == max(year)] %>% .[pop_c2, on = "cohort"] - - resid[, `:=`(resid = pop_c2_obs - pop_jan1_pre)] + + resid[, `:=`(resid = pop_c2_obs - pop_jan1)] # Only used in the process for diagnostics # resid[, `:=`(rel_resid = resid / pop_c2_obs)] resid <- resid[, list(cohort, resid)] - + # This should just be one value per cohort. - + # determine uniform error discounts: resid_discounts <- stats::approx( - x = c(date1, date2), - y = c(0, 1), - xout = yrs_births - ) %>% + x = c(date1, date2), + y = c(0, 1), + xout = yrs_births + ) %>% data.table::as.data.table() %>% .[, list(year = x, discount = y)] - + # output pop_jan1 <- pop_jan1_pre %>% merge(resid, by = "cohort", all = TRUE) %>% merge(resid_discounts, by = "year", all = TRUE) - + # for the residual discount, account for boundaries pop_jan1[, `:=`( resid = ifelse(is.na(resid), 0, resid), discount = ifelse(year == max(year), 1, discount) )] - - + + pop_jan1 } +### ZZZ FLAG for redux +### after the under-the hood changes are redone to back out Sx, +### the top level args could expand it optionally let the user +### also provide nLxMat rup <- function( - c1, - c2, - date1, - date2, - age1, - age2, - dates_out, - lxMat, - age_lx, - dates_lx, - births, - years_births, - location, - sex, - midyear, - verbose, - ... - ) { + c1, + c2, + date1, + date2, + age1, + age2, + dates_out, + lxMat, + age_lx, + dates_lx, + births, + years_births, + location, + sex, + midyear, + verbose, + ... +) { + check_args( lxMat = lxMat, births = births, @@ -1043,11 +1059,11 @@ rup <- function( c2 = c2, verbose = verbose ) - + if (is.na(date1) | is.na(date2)){ stop("\nCensus dates didn't parse\n") } - + # TR: resolve dates_out # if some dates were given, let's coerce to numeric and ensure valid if (!is.null(dates_out)){ @@ -1059,15 +1075,15 @@ rup <- function( if (length(dates_out) == 0){ stop("\nno valid dates to interpolate to\n") } - + # if we still have valid dates, then check we're not extrapolating dates_out_keep <- data.table::between(dates_out, date1, date2, incbounds = FALSE) - + dates_out_for_real <- dates_out[dates_out_keep] - + # warn about any dates lost due to extrap request: if (length(dates_out_for_real) != length(dates_out) & verbose){ cat("\nFollowing dates requested, but not returned\nbecause they'd require extrapolation:\n",paste(dates_out[!dates_out_keep],collapse = ", "),"\n") @@ -1076,22 +1092,23 @@ rup <- function( stop("\nuh oh! This method is strictly for cohort component interpolation\nYour requested dates_out didn't have anything between date1 and date2\n") } } - + # If dates_out not given, then we resolve using the midyear argument. # If FALSE (default) we return intermediate Jan 1, not including c1 and c2 # If TRUE we return intermediate July 1 (.5) dates, not including c1 and c2 dates_out <- transform_datesout(dates_out, date1, date2, midyear) - + DD <- date2 - date1 if (DD >= 15 & verbose){ cat("\nFYI, there are",DD,"years between c1 and c2\nBe wary.\n") } - + # let's store the proportions separately f1 <- date1 %>% magrittr::subtract(date1 %>% floor) f2 <- date2 %>% magrittr::subtract(date2 %>% floor) - + # And download if needed + # ZZZ FLAG: this step may pxt <- transform_pxt( lxMat = lxMat, location = location, @@ -1104,7 +1121,7 @@ rup <- function( age1 = age1, ... = ... ) - + yrs_births <- seq(floor(date1), floor(date2), 1) # TR: if right-side is jan 1 then we can cut it off of pxt. if (f2 == 0){ @@ -1112,7 +1129,7 @@ rup <- function( yrs_births <- yrs_births[-length(yrs_births)] f2 <- 1 } - + # Download wpp births if needed births <- fetch_wpp_births( @@ -1122,23 +1139,26 @@ rup <- function( sex = sex, verbose = verbose ) - + # check length of births, also filter using provided dates if necessary if (!is.null(years_births)){ stopifnot(length(births) == length(years_births)) - + years_births <- floor(years_births) yrs_keep <- data.table::between(years_births, min(yrs_births), max(yrs_births), incbounds = TRUE) - + births <- births[yrs_keep] } - + # now that births should be available we can do this check. stopifnot(length(births) == length(yrs_births)) - + + + # ZZZ FLAG this may or may not stay same. pxt could just be different values, + # and perhaps this would work. However, edge conditions need exa pop_jan1 <- reshape_pxt( pxt = pxt, births = births, @@ -1152,7 +1172,7 @@ rup <- function( f2 = f2, yrs_births = yrs_births ) - + list( pop_jan1 = pop_jan1, dates_out = dates_out diff --git a/R/interp_lc_lim.R b/R/interp_lc_lim.R index 281cbf434..da6899462 100644 --- a/R/interp_lc_lim.R +++ b/R/interp_lc_lim.R @@ -1,10 +1,10 @@ #' Lee-Carter method with limited data. #' -#' @description Given a data frame with dates, sex and mortality data by age (rates, conditionated probabilities of death +#' @description Given a data frame with dates, sex and mortality data by age (rates, conditioned probabilities of death #' or survival function), this function interpolate/extrapolate life tables #' using the method for limited data suggested by Li et. al (2004) (at least three observed years). #' -#' @details Based on spreedsheet "Li_2018_Limited_Lee-Carter-v4.xlsm" from UN. +#' @details Based on spreadsheet "Li_2018_Limited_Lee-Carter-v4.xlsm" from UN. #' Useful for abridged or single ages, and allows output in both formats also. #' One option is the use of non-divergent method for sex coherency (Li & Lee, 2005). #' The other is the possibility of fitting `"k"` to replicate `"e_0"` at some given dates. @@ -13,7 +13,7 @@ #' #' @param input data.frame with cols: Date, Sex, Age, nMx (opt), nqx (opt), lx (opt) #' @param dates_out numeric. Vector of decimal years to interpolate or extrapolate. -#' @param Single logical. Wheter or not the lifetable output is by single ages. +#' @param Single logical. Whether or not the lifetable output is by single ages. #' @param dates_e0 numeric. Vector of decimal years where `"e_0"` should be fitted when apply method. #' @param e0_Males numeric. Vector of life expectancy by year to be fitted. Same length than `"dates_e0"`. #' @param e0_Females numeric. Vector of life expectancy by year to be fitted. Same length than `"dates_e0"`. @@ -287,13 +287,22 @@ interp_lc_lim <- function(input = NULL, e0_Males), dates_e0, dates_out, - extrap = TRUE)[1, ] + extrap = TRUE) e0f <- interp(rbind(e0_Females, e0_Females), dates_e0, dates_out, - extrap = TRUE)[1, ] + extrap = TRUE) + + # IW: issue with dimension in case the interpolation is for 1 date only + if(ndates_out==1){ + e0m = e0m[1] + e0f = e0f[1] + }else{ + e0m = e0m[1,] + e0f = e0f[1,] + } # avoid divergence: same bx but not kt. if (prev_divergence){ @@ -304,7 +313,7 @@ interp_lc_lim <- function(input = NULL, ktm_star = ktf_star = c() for (j in 1:ndates_out){ ktm_star[j] <- optimize(f = interp_lc_lim_kt_min, - interval = c(-20, 20), + interval = c(-50, 50), ax = axm, bx = bxm, age = Age, @@ -312,7 +321,7 @@ interp_lc_lim <- function(input = NULL, e0_target = e0m[j], ...)$minimum ktf_star[j] <- optimize(f = interp_lc_lim_kt_min, # TR: add ... - interval = c(-20, 20), + interval = c(-50, 50), ax = axf, bx = bxf, age = Age, @@ -414,13 +423,13 @@ interp_lc_lim_abk_m <- function(k,ax,bx){ } # estimate LC for limited data -#' Estimate LC with limited data params +#' Estimate LC with limited data parameters #' @description Estimate LC with limited data from a matrix of rates (age by dates). -#' @details SVD for ax and bx. Fit a simmple linear model for k and interp/extrapolate for objective dates. -#' @param M numeric. Matrix with many rows as ages and columns as dates_in. +#' @details SVD for `ax` and `bx.` Fit a simple linear model for `k` and interpolate/extrapolate for objective dates. +#' @param M numeric. Matrix with many rows as ages and columns as `dates_in`. #' @param dates_in numeric. Vector of dates with input rates. #' @param dates_out numeric. Vector of dates for estimate a set of rates. -#' @param SVD logical. Use Singular Value Decomposition for estimate b and k or Maximum Likelihood Estimation. Default `FALSE` for Maximum Likelihood Estimation. +#' @param SVD logical. Use Singular Value Decomposition for estimate `b` and `k` or Maximum Likelihood Estimation. Default `FALSE` for Maximum Likelihood Estimation. #' @references #' \insertRef{Li2004}{DemoTools} #' @export @@ -452,7 +461,7 @@ interp_lc_lim_estimate <- function(M, dates_in, dates_out, SVD = F){ # smooth rule previous to solve ambiguous #' Smooth and apply lt_ambiguous #' @description Considering different mortality input for each sex/year data, -#' smooth olders with makeham or kannisto in case no law was specified, +#' smooth older ages with makeham or kannisto in case no law was specified, #' and return a data.frame with standard LT. #' @details Makeham is chosen if last age is less than 90. Else Kannisto. #' @param input data.frame. with cols: Date, Sex, Age, nMx (opt), nqx (opt), lx (opt) diff --git a/R/interp_lc_lim_group.R b/R/interp_lc_lim_group.R index 502ab21e9..efbdc6790 100644 --- a/R/interp_lc_lim_group.R +++ b/R/interp_lc_lim_group.R @@ -9,11 +9,11 @@ # text/messages/warnings. Specially the case when no `id` is given #' -#' @description Given a data frame with groups (country, region, sex), dates, sex and mortality data by age (rates, conditionated probabilities of death +#' @description Given a data frame with groups (country, region, sex), dates, sex and mortality data by age (rates, conditioned probabilities of death #' or survival function), this function interpolate/extrapolate life tables #' using the method for limited data suggested by Li et. al (2004) (at least three observed years). #' -#' @details Based on spreedsheet "Li_2018_Limited_Lee-Carter-v4.xlsm" from UN. +#' @details Based on spreadsheet "Li_2018_Limited_Lee-Carter-v4.xlsm" from UN. #' Useful for abridged or single ages, and allows output in both formats also. #' One option is the use of non-divergent method for sex coherency (Li & Lee, 2005). #' The other is the possibility of fitting `"k"` to replicate `"e_0"` at some given dates. @@ -23,9 +23,9 @@ #' @note Draft Version #' #' @param input data.frame. Columns: id, Date, Sex, Age, nMx (opt), nqx (opt), lx (opt). -#' The first column (id) cn be a numeric index or charcter vector identifying each group. +#' The first column (id) can be a numeric index or character vector identifying each group. #' @param dates_out numeric. Vector of decimal years to interpolate or extrapolate. -#' @param Single logical. Wheter or not the lifetable output is by single ages. +#' @param Single logical. Whether or not the lifetable output is by single ages. #' @param input_e0 data.frame with cols: id, Date, Sex and `"e_0"`. This should be fitted when apply method. #' @param prev_divergence logical. Whether or not prevent divergence and sex crossover between groups. Default `FALSE.` #' @param weights list. For `prev_divergence` option. A double for each element of a list with names as `id` columns. Should sum up to 1. Default: same weight for each group. diff --git a/R/log_quad_augm.R b/R/log_quad_augm.R new file mode 100644 index 000000000..fcbb921e7 --- /dev/null +++ b/R/log_quad_augm.R @@ -0,0 +1,176 @@ + +# author: IW -------------------------------------------------------------- + +#' HMD pattern for adult ages. +#' @description Adjust rates in oldest ages using HMD pattern, based on log-quad method. +#' @details One possible scenario when mortality data on last ages is not reliable, is to use a mortality pattern with some known index in previous ages. +#' This function gives a HMD pattern based on 5q0 and 45q15, using log-quad model. Additionally, a value on mortality between 60 and 75 can be included to make a better adjustment in level. +#' @param nMx numeric. Vector of mortality rates in abridged age classes. +#' @param Age integer. Single ages (abridged not allowed). +#' @param Sex character. Either male \code{"m"}, female \code{"f"}, or both \code{"b"}. +#' @param q0_5 numeric. Probability of death from born to age 5. By default implicit values in `nMx` should be entered. +#' @param q15_45 numeric. Probability of death from age 15 to age 60. By default implicit values in `nMx` should be entered. +#' @param fitted_logquad Optional, defaults to \code{NULL}. An object of class +#' \code{wilmoth}. If full HMD is not enough, one +#' can fit a Log-Quadratic (\url{https://github.com/mpascariu/MortalityEstimate}) model +#' based on any other collection of life tables; +#' @param q60_15 numeric. Probability of death from age 60 to age 75. When external information on those ages level is available, +#' can be included to increase parameter `ax` from log-quad model in last ages (Li, 2003). +#' @param Age_transition integer. Form which age should transition to HMD pattern starts. +#' @param window_transition integer. Number of ages to the left and to the right of `Age_transition` to do a log-linear transition in rates. +#' @param plot_comparison Show or not a plot with the result. +#' @param ... Other arguments to be passed on to the \code{lt_single} function. +#' @export +#' @return life table as in \code{lt_single} function. +#' @examples +#' # Mortality rates from UN Chilean with e0=70. Wat would be the rates based on HMD pattern? +#' # In this case making a transition of 10 years at age 80, and returning an OAG=100. +#' \dontrun{ +#' lt <- DemoToolsData::modelLTx1 +#' lt <- lt[lt$family == "Chilean" & lt$sex == "female" & lt$e0 == 70,] +#' chilean70_adjHMD <- HMD_old_logquad(nMx = lt$mx1, +#' Age = lt$age, +#' Sex = "f", +#' q0_5 = 1 - lt$lx1[lt$age==5]/lt$lx1[lt$age==0], +#' q15_45 = 1 - lt$lx1[lt$age==60]/lt$lx1[lt$age==15], +#' Age_transition = 80, +#' window_transition = 10, +#' plot_comparison = TRUE, +#' OAnew = 100) +#' # We know (as an example) that q60_15 is .5 higher than what HMD pattern would be. +#' chilean70_adjHMD_augm <- HMD_old_logquad(nMx = lt$mx1, +#' Age = lt$age, +#' Sex = "f", +#' q0_5 = 1 - lt$lx1[lt$age==5]/lt$lx1[lt$age==0], +#' q15_45 = 1 - lt$lx1[lt$age==60]/lt$lx1[lt$age==15], +#' q60_15 = (1 - lt$lx1[lt$age==75]/lt$lx1[lt$age==60]) * 1.5, +#' Age_transition = 80, window_transition = 10, +#' OAnew = 100, plot_comparison = TRUE) +#' } + +HMD_old_logquad <- function(nMx, Age = NULL, + Sex = "b", + q0_5 = NULL, q15_45 = NULL, + q60_15 = NULL, + Age_transition = 80, + window_transition = 3, + plot_comparison = FALSE, + fitted_logquad = NULL, + ...){ + + # check if age is not complete + if(!is_single(Age)) stop("Need rates by single age.") + if(is.null(Age)) Age <- 0:length(nMx) + + # check if an optional fitted_logquad is specified + if(is.null(fitted_logquad)){ + if(Sex == "b"){ + fitted_logquad <- DemoTools::fitted_logquad_b + } + if(Sex == "f"){ + fitted_logquad <- DemoTools::fitted_logquad_f + } + if(Sex == "m"){ + fitted_logquad <- DemoTools::fitted_logquad_m + } + } + + # load the log-quad model already fitted in the package (different from MortalityEstimate) and estimate with input data + logquad_model <- lt_model_lq(Sex = Sex, q0_5 = q0_5, q15_45 = q15_45, fitted_logquad = fitted_logquad) + mx_logquad_5q0_45q15 <- logquad_model$lt + + # augmented method if was asked + if(!is.null(q60_15)){ + mx_logquad_5q0_45q15 <- tryCatch({ + logquad_augmented(coeffs = fitted_logquad$coefficients, + k = logquad_model$values$k, + Age = logquad_model$lt$Age, + q0_5 = q0_5, q60_15 = q60_15, Sex = Sex)}, + error = function(e) { + warning("Augmented log-quad was not possible. Revise q60_15. Returned basic log-quad.") + mx_logquad_5q0_45q15}) + } + + # make it single + mx_logquad_5q0_45q15 <- lt_abridged2single(nMx = mx_logquad_5q0_45q15$nMx, + Age = mx_logquad_5q0_45q15$Age, + lx = mx_logquad_5q0_45q15$lx, + Sex = Sex, + ...) + + # smooth transition with a given length window + Ages <- 0:max(mx_logquad_5q0_45q15$Age) + Age_smooth <- (Age_transition-window_transition):(Age_transition+window_transition) + Age_not_smooth <- Ages[!Ages %in% Age_smooth] + nMx_to_smooth_transition <- data.frame(Age = Age_not_smooth, + nMx = c(nMx[Agemax(Age_smooth)])) + nMx_interpolated <- exp(stats::approx(x = nMx_to_smooth_transition$Age, + y = log(nMx_to_smooth_transition$nMx), + xout = Age_smooth)$y) + smooth_transtition_nMx <- dplyr::bind_rows( + nMx_to_smooth_transition, + data.frame(Age = Age_smooth, nMx = nMx_interpolated)) %>% + dplyr::arrange(Age) + + # plot diagnostic + if(plot_comparison){ + Type <- NULL + df1 <- data.frame(Age = Age, nMx = nMx) + df1$Type <- "Input" + df2 <- data.frame(Age = smooth_transtition_nMx$Age, nMx = smooth_transtition_nMx$nMx) + df2$Type <- "Adjusted" + df3 <- data.frame(Age = Age_smooth, nMx = nMx_interpolated) + df3$Type <- "Transition" + rbind(df1, df2, df3) %>% + ggplot2::ggplot(ggplot2::aes(x = Age, y = nMx, color = Type)) + + ggplot2::geom_line() + + ggplot2::geom_vline(xintercept = Age_transition, linetype = "dashed", color = "grey") + + ggplot2::scale_y_log10() + + ggplot2::theme_bw() + } + + # rebuild lt and finish, with input OAnew if was not defined as additional input argument + lt_extra_arguments <- list(...) + if(!("OAnew" %in% names(lt_extra_arguments))){OAnew <- max(Age)} else {OAnew <- lt_extra_arguments$OAnew} + mx_logquad_5q0_45q15 <- lt_single_mx(nMx = smooth_transtition_nMx$nMx, + Age = smooth_transtition_nMx$Age, + Sex = Sex, + OAnew = OAnew) + return(mx_logquad_5q0_45q15) +} + +#' Augmented logquad +#' @description Adjust rates in oldest ages that comes from a HMD model, using an external estimate of 15q60 (Li, 2014). As an example see\code{\link[DemoTools]{HMD_old_logquad}}. +#' @details Parameter \code{a(x)} is augmented based on an external estimate of 15q60. +#' @param coeffs data.frame. Columns \code{a(x)}, \code{b(x)}, \code{c(x)} and \code{v(x)} from fitted logquad model. See \code{fitted_logquad_b}. +#' @param k numeric. Adult mortality related value from log-quad estimatation based on one or two input parameters. See \code{lt_model_lq}. +#' @param Sex character. Either male \code{"m"}, female \code{"f"}, or both \code{"b"}. +#' @param Age integer. Abridged lower bound ages. Same length than rows in `coeffs`. +#' @param q0_5 numeric. Probability of death from born to age 5. +#' @param q60_15 numeric. Probability of death from age 60 to age 75. +#' @param ... Other arguments to be passed on to the \code{lt_abridged} function. +#' @export +#' @return life table as in \code{lt_abridged} function. +#' @references See [Li (2014)](https://www.un.org/development/desa/pd/content/estimating-life-tables-developing-countries). + +logquad_augmented <- function(coeffs, k, q0_5, q60_15, Sex = "b", Age, ...){ + + # arrange parameters and get rates from the model + params <- c(1, log(q0_5), log(q0_5)^2, k) + mx_logquad <- exp(rowSums(as.matrix(coeffs) %*% diag(params))) + + # adjust ax with the ratio between input value and implicit value from model + q60_15_hat <- q60_15 + age_q60_15 <- which(Age == 60) + q60_15 <- 1 - exp(-5*(sum(mx_logquad[c(age_q60_15, age_q60_15+1, age_q60_15+2)]))) + coeffs_hat <- coeffs + coeffs_hat$ax[age_q60_15:nrow(coeffs_hat)] <- coeffs_hat$ax[age_q60_15:nrow(coeffs_hat)] + log(log(1-q60_15_hat)/log(1-q60_15)) + + # new rates with changed ax + mx_logquad_augm <- exp(rowSums(as.matrix(coeffs_hat) %*% diag(params))) + lt_logquad_augm <- lt_abridged(nMx = mx_logquad_augm, Age = Age, Sex = Sex, ...) + + # output + return(lt_logquad_augm) +} \ No newline at end of file diff --git a/R/lt_abridged.R b/R/lt_abridged.R index 6630faf74..4ff6c3c81 100644 --- a/R/lt_abridged.R +++ b/R/lt_abridged.R @@ -20,7 +20,7 @@ #' is aligned with the other columns in all 5-year age groups, but note the #' first two values have a slightly different age-interval interpretation: #' In Age 0, the interpretation is survival from birth until interval 0-4. -#' In Age 1, it is survival from 0-4 into 5-9. Therafter the age groups align. +#' In Age 1, it is survival from 0-4 into 5-9. Thereafter the age groups align. #' This column is required for population projections. #' #' @param Deaths numeric. Vector of death counts in abridged age classes. @@ -36,12 +36,13 @@ #' @param region character. North, East, South, or West: code{"n"}, code{"e"}, code{"s"}, code{"w"}. Default code{"w"}. #' @param IMR numeric. Infant mortality rate \ifelse{html}{\out{q0}}{\eqn{q_0}}, in case available and \code{nqx} is not specified. Default \code{NA}. #' @param mod logical. If \code{"un"} specified for \code{axmethod}, whether or not to use Nan Li's modification for ages 5-14. Default \code{TRUE}. -#' @param SRB the sex ratio at birth (boys / girls), detault 1.05 +#' @param SRB the sex ratio at birth (boys / girls), default 1.05 #' @param OAnew integer. Desired open age group (5-year ages only). Default \code{max(Age)}. If higher then rates are extrapolated. #' @param OAG logical. Whether or not the last element of \code{nMx} (or \code{nqx} or \code{lx}) is an open age group. Default \code{TRUE}. #' @param extrapLaw character. If extrapolating, which parametric mortality law should be invoked? Options include #' \code{"Kannisto", "Kannisto_Makeham", "Makeham", "Gompertz", "GGompertz", "Beard", "Beard_Makeham", "Quadratic"}. Default \code{"Kannisto"} if the highest age is at least 90, otherwise `"makeham"`. See details. #' @inheritParams lt_a_closeout +#' @importFrom dplyr case_when #' @export #' @return Lifetable in data.frame with columns #' \itemize{ @@ -183,9 +184,30 @@ lt_abridged <- function(Deaths = NULL, extrapFrom = max(Age), extrapFit = NULL, ...) { + + # some handy name coercion + a0rule <- case_when(a0rule == "Andreev-Kingkade" ~ "ak", + a0rule == "Coale-Demeny" ~ "cd", + TRUE ~ a0rule) + axmethod <- case_when(axmethod == "UN (Greville)" ~ "un", + axmethod == "PASEX" ~ "pas", + TRUE ~ axmethod) + Sex <- substr(Sex, 1, 1) |> + tolower() + Sex <- ifelse(Sex == "t", "b", Sex) + + region <- substr(region, 1, 1) |> + tolower() + if (!is.null(extrapLaw)){ + extrapLaw <- tolower(extrapLaw) + } + + + # now we check args axmethod <- match.arg(axmethod, choices = c("pas","un")) Sex <- match.arg(Sex, choices = c("m","f","b")) a0rule <- match.arg(a0rule, choices = c("ak","cd")) + if (!is.null(extrapLaw)){ extrapLaw <- tolower(extrapLaw) extrapLaw <- match.arg(extrapLaw, choices = c("kannisto", @@ -318,27 +340,30 @@ lt_abridged <- function(Deaths = NULL, momega <- nMx[length(nMx)] } # -------------------------------- - # begin extrapolation: - # TR: 13 Oct 2018. always extrapolate to 130 no matter what, - # then truncate to OAnew in all cases. This will ensure more robust closeouts - # and an e(x) that doesn't depend on OAnew. 130 is used similarly by HMD. - x_extr <- seq(extrapFrom, 130, by = 5) - - Mxnew <- lt_rule_m_extrapolate( - x = Age, - mx = nMx, - x_fit = extrapFit, - x_extr = x_extr, - law = extrapLaw, - ...) - - nMxext <- Mxnew$values - Age2 <- names2age(nMxext) - - keepi <- Age2 < extrapFrom - nMxext[keepi] <- nMx[Age < extrapFrom] - nMx <- nMxext - Age <- Age2 + + if (max(Age) < 130){ + # begin extrapolation: + # TR: 13 Oct 2018. always extrapolate to 130 no matter what, + # then truncate to OAnew in all cases. This will ensure more robust closeouts + # and an e(x) that doesn't depend on OAnew. 130 is used similarly by HMD. + x_extr <- seq(extrapFrom, 130, by = 5) + + Mxnew <- lt_rule_m_extrapolate( + x = Age, + mx = nMx, + x_fit = extrapFit, + x_extr = x_extr, + law = extrapLaw, + ...) + + nMxext <- Mxnew$values + Age2 <- names2age(nMxext) + + keepi <- Age2 < extrapFrom + nMxext[keepi] <- nMx[Age < extrapFrom] + nMx <- nMxext + Age <- Age2 + } AgeInt <- age2int( Age, OAG = TRUE, @@ -416,7 +441,9 @@ lt_abridged <- function(Deaths = NULL, nMx[N] <- lx[N] / Tx[N] } - Sx <- lt_id_Ll_S(nLx, lx, AgeInt, N = 5) + Sx <- c(lt_id_Ll_S(nLx, lx, Age, AgeInt, N = 5), 0.0) + names(Sx) <- NULL + # output is an unrounded, unsmoothed lifetable out <- data.frame( Age = Age, diff --git a/R/lt_id.R b/R/lt_id.R index 400645dc3..7c6ed00f6 100644 --- a/R/lt_id.R +++ b/R/lt_id.R @@ -118,15 +118,16 @@ lt_id_l_d <- function(lx) { #' @title Derive lifetable death probabilities from survivorship. #' @description This lifetable identity is the same no matter what kind of lifetable is required. #' You can find it in any demography textbook. -#' @details The vector returned is the same length as \code{lx} and it sums to the lifetable radix. -#' If the radix is one then this is the discrete deaths distribution. +#' @details The vector returned is the same length as \code{lx}. #' #' @param lx numeric. Vector of age-specific lifetable survivorship. #' @references #' \insertRef{preston2000demography}{DemoTools} -#' @return ndx vector of lifetable deaths. +#' @return `qx` values of age-specific mortality rates. The last value is always 1.0 #' @export lt_id_l_q <- function(lx) { + # TR note if there are trailing 0s in lx then + # this can be NaN dx <- lt_id_l_d(lx) dx / lx } @@ -271,27 +272,47 @@ lt_id_ma_q <- function(nMx, nax, AgeInt, closeout = TRUE, IMR) { } #' @title Calculate survivor ratios -#' @description An extra lifetable column for use in projections, which require uniform time steps both both age and period. Intervals are either single age (\code{N=1}) or five-year ages (\code{N=5}). Input vectors are assumed to come from either single or standard abridged ages. +#' @description An extra lifetable column for use in projections, which require uniform time steps both both age and period. Intervals are either single age or five-year ages. Input vectors are assumed to come from either single or standard abridged ages. Note that the ages of the output Sx are the ages the population would be after the N-year projection. #' @details This function does not account for \code{nLx} having been pre-binned into uniform 5-year age widths, which will throw an error. Just leave them in abridged ages instead. Note that in the case of abridged ages, the interpretation for the first and second value don't follow the original abridged age intervals: the first value in the probability of surviving from birth into ages 0-4 in the first five years, and the second value is the probability of surviving from 0-4 to 5-9. This represents a slight misalignment with the rest of the lifetable, user beware. -#' @inheritParams lt_abridged #' @param nLx numeric vector of lifetable exposure. +#' @param lx numeric vector of lifetable survivors from same lifetable than \code{nLx}. Infered radix from nLx in case is \code{NULL}. +#' @param Age integer vector of starting ages. +#' @param AgeInt integer vector of age intervals. #' @param N integer, the age width for survivor ratios, either 5 or 1. Default 5. #' @export -lt_id_Ll_S <- function(nLx, lx, AgeInt, N = c(5, 1)) { + +lt_id_Ll_S <- function(nLx, lx = NULL, Age, AgeInt = NULL, N = 5) { + # number ages n <- length(nLx) - stopifnot(length(lx) == n) + # same length of vectors + stopifnot(length(nLx) == length(Age)) # either we're in 1 or 5 year age groups stopifnot(length(N) == 1 & N %in% c(5, 1)) - ## compute Sx (missing from the LTbr computation - Sx <- rep(NA, n) + # infer radix in case lx is not given + radix <- lx[1] + if(is.null(lx)){ + radix <- ifelse(nLx[1]>1, 10^nchar(trunc(nLx[1])), 1) + } + # validate nLx. Some zero in ages>100 could happen. YP should be non-zero in ages<80, even in historical pops (?) + # IW: relax this condition on YP of non-negative YP because of huge heterogeneity on input lt + stopifnot(all(nLx>=0, # nLx[Age<60]>0, + nLx[-n] < (radix*N))) + + ## compute Sx (missing from the LTbr computation) # first age group is survival from births to the second age group if (N == 5) { - # double check because assuming abridged nLx is given... + Sx <- rep(NA, n-1) + # infer AgeInt in case is not given + if(is.null(AgeInt)){ + AgeInt <- inferAgeIntAbr(Age) + } stopifnot(length(AgeInt) == n) ageintcompare <- inferAgeIntAbr(vec = nLx) - stopifnot(all(ageintcompare[-n] == AgeInt[-n])) + if (Age[1] == 0){ + stopifnot(all(ageintcompare[-n] == AgeInt[-n])) + } # birth until 0-4 - Sx[1] <- (nLx[1] + nLx[2]) / ((AgeInt[1] + AgeInt[2]) * lx[1]) + Sx[1] <- (nLx[1] + nLx[2]) / ((AgeInt[1] + AgeInt[2]) * radix) # second age group is survival age 0-4 to age 5-9 Sx[2] <- nLx[3] / (nLx[1] + nLx[2]) # middle age groups @@ -299,18 +320,20 @@ lt_id_Ll_S <- function(nLx, lx, AgeInt, N = c(5, 1)) { Sx[mind] <- nLx[mind + 1] / nLx[mind] # penultimate age group Sx[n - 1] <- nLx[n] / (nLx[n - 1] + nLx[n]) - # closeout - Sx[n] <- 0.0 + # names of ages at arrive + names(Sx) <- seq(0,Age[length(Age)],5) } if (N == 1) { - LLXX <- c(lx[1], nLx) + Sx <- rep(NA, n) + LLXX <- c(radix, nLx) mind <- 1:(n - 1) Sx[mind] <- LLXX[mind + 1] / LLXX[mind] # closeout Sx[n] <- nLx[n] / (nLx[n - 1] + nLx[n]) + # names of ages at arrive + names(Sx) <- 0:(n-1) } - - - Sx } + + diff --git a/R/lt_model_lq.R b/R/lt_model_lq.R index ee601dea1..fa063b710 100644 --- a/R/lt_model_lq.R +++ b/R/lt_model_lq.R @@ -12,7 +12,7 @@ #' Estimate Wilmoth Model Life Table #' -#' Construct model life tables based on the Log-Quadratic (wilmoth) estimates +#' Construct model life tables based on the Log-Quadratic (Wilmoth) estimates #' with various choices of 2 input parameters: #' \code{q0_5, q0_1, q15_45, q15_35} and \code{e0}. There are 8 possible #' combinations (see examples below). @@ -20,7 +20,7 @@ #' @details Due to limitations of the R language the notation for probability #' of dying \code{nqx} is written \code{qx_n}, where \code{x} and \code{n} are #' integers. For example \code{45q15} is represented as \code{q45_15}. -#' @note This function is ported from \code{MortalityEstimate::wilmothLT} experimental package by Marius Pascariu. The package is no longe maintained. The latest version can be found here: \url{https://github.com/mpascariu/MortalityEstimate} +#' @note This function is ported from \code{MortalityEstimate::wilmothLT} experimental package by Marius Pascariu. The package is no longer maintained. The latest version can be found here: \url{https://github.com/mpascariu/MortalityEstimate} #' @param Sex Choose the sex of the population. This choice defines the use #' of a corresponding Log-Quadratic (\code{wilmoth}) #' model fitted for the whole Human Mortality Database (as of Dec 2019, diff --git a/R/lt_regroup_age.R b/R/lt_regroup_age.R index 516d0cde7..b2e557011 100644 --- a/R/lt_regroup_age.R +++ b/R/lt_regroup_age.R @@ -53,7 +53,8 @@ lt_single2abridged <- function(lx, nAx[N] <- ex[N] nMx <- ndx/nLx Tx <- lt_id_L_T(nLx) - Sx <- lt_id_Ll_S(nLx, lx, AgeInt, N = 5) + Sx <- c(lt_id_Ll_S(nLx, lx, Age5, AgeInt, N = 5),0.0) + names(Sx) <- NULL out <- data.frame( Age = Age5, @@ -80,7 +81,7 @@ lt_single2abridged <- function(lx, #' @description Computes single year of age life table by graduating the mortality schedule of an abridged life table, using the `ungroup::pclm()` to ungroup binned count data. Returns complete single-age lifetable. #' @details Similar to `lt_abridged()` details, forthcoming. #' @inheritParams lt_abridged -#' @param ... optional arguments passed to `pclm()`. For example, if you pass an expicit `lambda` parameter via the `control` argument, you can speed up estimation +#' @param ... optional arguments passed to `pclm()`. For example, if you pass an explicit `lambda` parameter via the `control` argument, you can speed up estimation #' @return Single-year lifetable in data.frame with columns #' \itemize{ #' \item{Age}{integer. Lower bound of single year age class}, @@ -98,6 +99,7 @@ lt_single2abridged <- function(lx, #' #' @export #' @importFrom ungroup pclm +#' @importFrom dplyr case_when #' @examples #' Mx <- c(.23669,.04672,.00982,.00511,.00697,.01036,.01169, #' .01332,.01528,.01757,.02092,.02517,.03225,.04241,.06056, @@ -150,6 +152,23 @@ lt_abridged2single <- function( NN <- length(Age) #stopifnot(length(nMx) == NN) + # some handy name coercion + a0rule <- case_when(a0rule == "Andreev-Kingkade" ~ "ak", + a0rule == "Coale-Demeny" ~ "cd", + TRUE ~ a0rule) + axmethod <- case_when(axmethod == "UN (Greville)" ~ "un", + axmethod == "PASEX" ~ "pas", + TRUE ~ axmethod) + Sex <- substr(Sex, 1, 1) |> + tolower() + Sex <- ifelse(Sex == "t", "b", Sex) + + region <- substr(region, 1, 1) |> + tolower() + if (!is.null(extrapLaw)){ + extrapLaw <- tolower(extrapLaw) + } + if (!is.null(extrapLaw)){ extrapLaw <- tolower(extrapLaw) extrapLaw <- match.arg(extrapLaw, choices = c("kannisto", @@ -218,9 +237,9 @@ lt_abridged2single <- function( # redefine Age and extrapFit for single year ages and new maxage a1 <- 1:length(M) - 1 - extrapFit <- a1[a1 >= min(extrapFit, (max(Age)-20)) & a1 <= max(Age)] + extrapFit <- a1[a1 >= min(extrapFit, (max(a1)-20)) & a1 <= max(Age)] # always refit from 110 even if extrapFrom > 110 - extrapFrom <- min(max(Age), 110) + extrapFrom <- min(max(a1), 110) # compute life table columns from single year mx LT <- lt_single_mx(nMx = M, @@ -242,7 +261,7 @@ lt_abridged2single <- function( } -#' calculate an abidged or single age lifetable from abridged or sinlge age data +#' calculate an abridged or single age lifetable from abridged or single age data #' @description This is a wrapper around the other lifetable utilities. We start with either `nMx`, `nqx`, or `lx` in single or abridged ages, and returns a full lifetable in either single or abridged ages. All optional arguments of `lt_abridged()` or `lt_single*()` can be passed in, for instance the `nax` assumptions or the extrapolation arguments. #' #' @param nMx_or_nqx_or_lx numeric vector of either `nMx`, `nqx`, or `lx` @@ -311,7 +330,7 @@ lt_ambiguous <- function(nMx_or_nqx_or_lx = NULL, out <- lt_single_qx(nqx = xx, Age = Age, Sex = Sex, ...) } if (type == "q" & !Single){ - out <- lt_single_qx(qx = xx, Age = Age, Sex = Sex, ...) + out <- lt_single_qx(nqx = xx, Age = Age, Sex = Sex, ...) out <- lt_single2abridged(lx = out$lx,nLx = out$nLx, ex = out$ex) } } diff --git a/R/lt_rule.R b/R/lt_rule.R index 828e81541..b16b56df2 100644 --- a/R/lt_rule.R +++ b/R/lt_rule.R @@ -88,8 +88,8 @@ #' segments(1, M1_4, 5, M1_4) #' text(1, c(M0, M1_4, M0_4), c("M0", "M1_4", "M0_4"), pos = 3) #' } -lt_rule_4m0_D0 <- function(D04, M04, P04, Sex = c("m", "f")) { - +lt_rule_4m0_D0 <- function(D04, M04, P04, Sex = "m") { + stopifnot(Sex %in% c("m","f")) if (missing(M04)) { M5 <- D04 / P04 } else { @@ -225,8 +225,8 @@ lt_rule_4m0_D0 <- function(D04, M04, P04, Sex = c("m", "f")) { #' text(1, c(M0, M1_4, M0_4), c("M0", "M1_4", "M0_4"), pos = 3) #' } -lt_rule_4m0_m0 <- function(M04, D04, P04, Sex = c("m", "f")) { - +lt_rule_4m0_m0 <- function(M04, D04, P04, Sex ="m") { + stopifnot(Sex %in% c("m","f")) if (missing(M04)) { M5 <- D04 / P04 } else { @@ -282,7 +282,7 @@ lt_rule_4m0_m0 <- function(M04, D04, P04, Sex = c("m", "f")) { #' @title estimates a0 using the Andreev-Kingkade rule of thumb starting with IMR #' -#' @description \code{AKq02a0} Andreev Kingkade a0 method. This version has a 3-part segemented linear model, based on cutpoints in q0. Code ported from HMDLifeTables. +#' @description \code{AKq02a0} Andreev Kingkade a0 method. This version has a 3-part segmented linear model, based on cut points in q0. Code ported from HMDLifeTables. #' #' @param q0 a value or vector of values of q0, the death probability in the first year of life. #' @param Sex either "m" or "f" @@ -328,9 +328,9 @@ lt_rule_ak_m0_a0 <- function(M0, Sex ){ #' #' @description This function wraps the two approximations for a0 based on either q0 (IMR) or m0. #' -#' @param M0 a value or vector of values of m0, the death probability in the first year of life. -#' @param q0 a value or vector of values of m0, the death risk in the first year of life. -#' @param Sex either "m" or "f" +#' @param M0 a value or vector of values of `1m0``, the death risk in the first year of life. +#' @param q0 a value or vector of values of `1q0``, the death probability in the first year of life, sometimes approximated with IMR. +#' @param Sex either `"m"` or `"f"` #' #' @return a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values. #' @@ -343,7 +343,7 @@ lt_rule_1a0_ak <- function(M0 = NULL, q0 = NULL, Sex){ a0 <- lt_rule_ak_q0_a0(q0,Sex) } if (is.null(q0) & !is.null(M0)){ - a0 <- lt_rule_ak_q0_a0(M0,Sex) + a0 <- lt_rule_ak_m0_a0(M0,Sex) } a0 } @@ -351,14 +351,14 @@ lt_rule_1a0_ak <- function(M0 = NULL, q0 = NULL, Sex){ #' @title calculate a0 in different ways #' -#' @description This function wraps the Coale-Demeny and Andreev-Kingkade approximations for a0, which can come from M0, qo, or IMR. +#' @description This function wraps the Coale-Demeny and Andreev-Kingkade approximations for `a0`, which can come from `M0`, `q0`, or `IMR.` #' @details If sex is given as both, \code{"b"}, then we calculate the male and female results separately, then weight them together using SRB. This is bad in theory, but the leverage is trivial, and it's better than using male or female coefs for the total population. #' #' @inheritParams lt_rule_1a0_cd #' @param rule character. Either \code{"ak"} (Andreev-Kingkade) or \code{"cd"} (Coale-Demeny). #' @param Sex character, either \code{"m"}, \code{"f"}, or \code{"b"} #' @param q0 a value or vector of values of m0, the death risk in the first year of life. -#' @param SRB the sex ratio at birth (boys / girls), detault 1.05 +#' @param SRB the sex ratio at birth (boys / girls), default 1.05 #' @details Neither Coale-Demeny nor Andreev-Kingkade have explicit a0 rules for both-sexes combined. There's not a good way to arrive at a both-sex a0 estimate without increasing data requirements (you'd need data from each sex, which are not always available). It's more convenient to blend sex-specific a0 estimates based on something. Here we use SRB to do this, for no other reason than it has an easy well-known default value. This is bad because it assumes no sex differences in infant mortality, but this choice has a trivial impact on results. #' @return a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values. #' diff --git a/R/lt_single.R b/R/lt_single.R index 7abec996f..60cbaac5a 100644 --- a/R/lt_single.R +++ b/R/lt_single.R @@ -19,6 +19,7 @@ #' \item{ex}{numeric. Age-specific remaining life expectancy.} #' } #' @export +#' @importFrom dplyr case_when lt_single_mx <- function(nMx, Age = 1:length(nMx) - 1, radix = 1e5, @@ -36,6 +37,21 @@ lt_single_mx <- function(nMx, ...) { stopifnot(extrapFrom <= max(Age)) + + # some handy name coercion + a0rule <- case_when(a0rule == "Andreev-Kingkade" ~ "ak", + a0rule == "Coale-Demeny" ~ "cd", + TRUE ~ a0rule) + Sex <- substr(Sex, 1, 1) |> + tolower() + Sex <- ifelse(Sex == "t", "b", Sex) + + region <- substr(region, 1, 1) |> + tolower() + if (!is.null(extrapLaw)){ + extrapLaw <- tolower(extrapLaw) + } + Sex <- match.arg(Sex, choices = c("m","f","b")) a0rule <- match.arg(a0rule, choices = c("ak","cd")) if (!is.null(extrapLaw)){ @@ -74,37 +90,40 @@ lt_single_mx <- function(nMx, } # -------------------------- # Now all vectors may end up being longer - x_extr <- seq(extrapFrom, 130, by = 1) - Mxnew <- lt_rule_m_extrapolate( - x = Age, - mx = nMx, - x_fit = extrapFit, - x_extr = x_extr, - law = extrapLaw, - ...) - - nMxext <- Mxnew$values - Age2 <- names2age(nMxext) + if (max(Age) < 130){ + x_extr <- seq(extrapFrom, 130, by = 1) + Mxnew <- lt_rule_m_extrapolate( + x = Age, + mx = nMx, + x_fit = extrapFit, + x_extr = x_extr, + law = extrapLaw, + ...) - keepi <- Age2 < extrapFrom - nMxext[keepi] <- nMx[Age < extrapFrom] - - # overwrite some variables: - nMx <- nMxext - Age <- Age2 + nMxext <- Mxnew$values + Age2 <- names2age(nMxext) + + keepi <- Age2 < extrapFrom + nMxext[keepi] <- nMx[Age < extrapFrom] + + # overwrite some variables: + nMx <- nMxext + Age <- Age2 + } N <- length(Age) AgeInt <- rep(1, N) # get ax: nAx <- rep(.5, N) - nAx[1] <- lt_rule_1a0( - rule = a0rule, - M0 = nMx[1], - IMR = IMR, - Sex = Sex, - region = region, - SRB = SRB) - + if (Age[1] == 0){ + nAx[1] <- lt_rule_1a0( + rule = a0rule, + M0 = nMx[1], + IMR = IMR, + Sex = Sex, + region = region, + SRB = SRB) + } # get qx (if pathological qx > 1, ax replaced, assumed constant hazard) qx <- lt_id_ma_q( nMx = nMx, @@ -146,7 +165,7 @@ lt_single_mx <- function(nMx, AgeInt[N] <- NA # Survival ratios computed only after nLx is closed out - Sx <- lt_id_Ll_S(nLx, lx, AgeInt = AgeInt, N = 1) + Sx <- lt_id_Ll_S(nLx, lx, Age, AgeInt = AgeInt, N = 1) if (OAG) { if (OAnew == OA) { diff --git a/R/lt_single_qx.R b/R/lt_single_qx.R index a164e8667..988fd4886 100644 --- a/R/lt_single_qx.R +++ b/R/lt_single_qx.R @@ -15,7 +15,7 @@ #' \item{lx}{numeric. Lifetable survivorship} #' \item{ndx}{numeric. Lifetable deaths distribution.} #' \item{nLx}{numeric. Lifetable exposure.} -#' \item{Sx}{numeric. Survivor ratios in uniform 5-year age groups.} +#' \item{Sx}{numeric. Survivor ratios in uniform single-year age groups.} #' \item{Tx}{numeric. Lifetable total years left to live above age x.} #' \item{ex}{numeric. Age-specific remaining life expectancy.} #' } @@ -81,13 +81,14 @@ lt_single_qx <- function(nqx, # compute ax: nAx <- rep(.5, N) - nAx[1] <- lt_rule_1a0(rule = a0rule, - q0 = nqx[1], - IMR = IMR, - Sex = Sex, - region = region, - SRB = SRB) - + if (Age[1] == 0){ + nAx[1] <- lt_rule_1a0(rule = a0rule, + q0 = nqx[1], + IMR = IMR, + Sex = Sex, + region = region, + SRB = SRB) + } # compute 1mx from 1qx and 1ax nMx <- lt_id_qa_m(nqx = nqx, nax = nAx, diff --git a/R/mig_beta.R b/R/mig_beta.R index 51aaf2d7a..16fdd4e0c 100644 --- a/R/mig_beta.R +++ b/R/mig_beta.R @@ -1,3 +1,8 @@ +# [ ] Jan 1 thing not ideal. +# [ ] C2 and last year can produce big negative in age 0. +# maybe cut it off and not return estimate for partial year +# [ ] use Lx ratios to project + #' Estimate intercensal migration by comparing census population, by age and #' sex, to the results of a RUP projection. #' @@ -8,6 +13,7 @@ #' the cohort parallelogram assuming uniform distribution assuming it is all #' migration. It finalizes by summing the estimate by age groups across the entire #' intercensal period to have a total migration during the entire period. +#' Alternatively, a child adjustment and an old age adjustment can be applied. #' #' @param c1 numeric vector. The first (left) census in single age groups #' @param c2 numeric vector. The second (right) census in single age groups @@ -25,6 +31,33 @@ #' @param sex character string, either `"male"`, `"female"`, or `"both"` #' @param midyear logical. `FALSE` means all Jan 1 dates between `date1` and `date2` are returned. `TRUE` means all July 1 intercensal dates are returned. #' @param verbose logical. Shall we send informative messages to the console? +#' +#' @param child_adjust The method with which to adjust the youngest age groups. +#' If \code{"none"}, no adjustment is applied (default). If +#' child-woman ratio (\code{"cwr"}) is chosen, the first cohorts reflecting the +#' difference between \code{date2 - date1} are adjusted (plus age 0). If +#' child constant ratio (\code{"constant"}) is chosen, the first 15 age groups +#' are adjusted. +#' +#' @param childage_max The maximum age from which to apply \code{child_adjust}. +#' By default, set to \code{NULL}, which gets translated into all the cohorts +#' between \code{date2} and \code{date1}. If \code{date2} is 2010 and +#' \code{date1} is 2002, the first 8 cohorts are adjusted. Otherwise, the user +#' can supply an integer. +#' +#' @param cwr_factor A numeric between 0 and 1 to which adjust the CWR method +#' for the young ages from \code{child_adjust}. \strong{This is only used +#' when \code{child_adjust} is \code{"cwr"}}. +#' +#' @param oldage_adjust The type of adjustment to apply to ages at and above +#' \code{oldage_min}. \code{'beers'} applies a beers graduation method +#' while \code{'mav'} applies a moving average with cascading on the tails. +#' For more information see \code{?mav} and \code{?graduation_beers}. +#' +#' @param oldage_min The minimum age from which to apply \code{oldage_adjust}. +#' By default, set to 65, so any adjustment from \code{oldage_adjust} will be +#' applied for 65+. +#' #' @param ... optional arguments passed to \code{lt_single_qx} #' @export #' @@ -36,7 +69,8 @@ #' @examples #' #' \dontrun{ -#' mig_beta( +#' +#' mig_beta( #' location = "Russian Federation", #' sex = "male", #' c1 = pop1m_rus2002, @@ -64,13 +98,24 @@ mig_beta <- function( sex = "both", midyear = FALSE, verbose = TRUE, - ... - ) { + child_adjust = c("none", "cwr", "constant"), + childage_max = NULL, + cwr_factor = 0.3, + oldage_adjust = c("none", "beers", "mav"), + oldage_min = 65, + ...) { + child_adjust <- match.arg(child_adjust) + oldage_adjust <- match.arg(oldage_adjust) # convert the dates into decimal numbers date1 <- dec.date(date1) date2 <- dec.date(date2) + # If null, assume, the cohorts between censuses date2 and dates2 + if (is.null(childage_max)) { + childage_max <- as.integer(ceiling(date2) - floor(date1)) + } + res_list <- rup( c1 = c1, c2 = c2, @@ -112,67 +157,79 @@ mig_beta <- function( # and the decum_resid on the values. mat_resid <- data.table::dcast( - pop_jan1[, list(year, age, decum_resid)], - age ~ year, - value.var = "decum_resid" - ) + pop_jan1[, list(year, age, decum_resid)], + age ~ year, + value.var = "decum_resid" + ) + mig_vec <- rowSums(mat_resid[,-1], na.rm = TRUE) # Sum over all ages to get a total decum_resid over all years for each age. - mig <- stats::setNames(rowSums(mat_resid, na.rm = TRUE), mat_resid$age) - mig -} + mig <- stats::setNames(mig_vec, mat_resid$age) + # Child adjustment + mig <- + switch( + child_adjust, + "none" = mig, + "cwr" = mig_beta_cwr(mig, c1, c2, date1, date2, n_cohs = childage_max, cwr_factor = cwr_factor), + "constant" = mig_beta_constant_child(mig, c1, c2, ageMax = childage_max) + ) + # Old age adjustment + mig_oldage <- + switch( + oldage_adjust, + "none" = mig, + "beers" = graduate_beers(mig, as.integer(names(mig)), AgeInt = 1), + "mav" = mav(mig, names(mig), tails = TRUE) + ) + # Only apply the old age adjustment on ages above oldage_min + ages_oldages <- as.integer(names(mig_oldage)) + mig[ages_oldages >= oldage_min] <- mig_oldage[ages_oldages >= oldage_min] + + mig +} -mig_beta_cwr <- function(mig, - c1_females, - c2_females, - date1, - date2, - maternal_window = 30, - maternal_min = 15){ +mig_beta_cwr <- function(mig, + c1_females, + c2_females, + date1, + date2, + maternal_window = 30, + maternal_min = 15, + n_cohs = NULL, + cwr_factor = 0.3) { + age <- names2age(mig) - + # conservative guess at how many child ages to cover: - n_cohs <- as.integer(ceiling(date2) - floor(date1)) - + if (is.null(n_cohs)) n_cohs <- as.integer(ceiling(date2) - floor(date1)) + mig_out <- mig - for (i in 1:n_cohs){ + for (i in 1:n_cohs) { # index maternal ages - a_min <- i + maternal_min - a_max <- min(i + maternal_min + maternal_window, 49) - mat_ind <- a_min:a_max - cwr_i <- (c1_females[i] / sum(c1_females[mat_ind]) + c2_females[i] / sum(c2_females[mat_ind])) / 2 + a_min <- i + maternal_min + a_max <- min(i + maternal_min + maternal_window, 49) + mat_ind <- a_min:a_max + cwr_i <- (c1_females[i] / sum(c1_females[mat_ind]) + c2_females[i] / sum(c2_females[mat_ind])) / 2 # proportional to maternal neg mig. - mig_out[i] <- cwr_i * sum(mig[mat_ind]) + mig_out[i] <- cwr_factor * cwr_i * sum(mig[mat_ind]) } - + mig_out } +# rough stb at constant child adjustment +mig_beta_constant_child <- function(mig, c1, c2, ageMax = 14) { + age <- names2age(mig) + + denom <- (c1 + c2) / 2 -# TR: prep for constant child. Need denom for rates though, -# but ideally without re-calculating an intermediate object -# that was already needed. Maybe be we can get exposures -# from RUP. Hmm. -# mig_beta_constant_child <- function(mig, -# c1, -# c2, -# date1, -# date2, -# maternal_window = 30, -# maternal_min = 15){ -# age <- names2age(mig) -# -# # conservative guess at how many child ages to cover: -# n_cohs <- as.integer(ceiling(date2) - floor(date1)) -# -# mig_out <- mig -# -# -# -# mig_out -# } -# + ind <- age <= ageMax + mig_rate_const <- sum(mig[ind]) / sum(denom[ind]) + mig[ind] <- denom[ind] * mig_rate_const + + mig +} diff --git a/R/mig_rc.R b/R/mig_rc.R index 8ec34743c..28ee1c223 100644 --- a/R/mig_rc.R +++ b/R/mig_rc.R @@ -14,7 +14,7 @@ #' @export #' @details In the full 13 parameter model, the migration rate at age x, \eqn{m(x)} is defined as -#' \deqn{m(x) = a1*exp(-1*alpha1*x) + a2*exp(-1*alpha2*(x - mu2) - exp(-1*lambda2*(x - mu2))) + a3*exp(-1*alpha3*(x - 3) - exp(-1*lambda3*(x - mu3))) + a4*exp(lambda4*x) + c} +#' \deqn{m(x) = a1*exp(-1*alpha1*x) + a2*exp(-1*alpha2*(x - mu2) - exp(-1*lambda2*(x - mu2))) + a3*exp(-1*alpha3*(x - mu3) - exp(-1*lambda3*(x - mu3))) + a4*exp(lambda4*x) + c} #' #' The first, second, third and fourth pieces of the equation represent pre-working age, working age, retirement and post-retirement age patterns, respectively. #' Models with less parameters gradually remove terms at the older ages. Parameters in each family are: diff --git a/R/mig_resid.R b/R/mig_resid.R index 17984aed1..9f387864b 100644 --- a/R/mig_resid.R +++ b/R/mig_resid.R @@ -371,7 +371,7 @@ mig_resid_stock <- function(pop_m_mat, is.numeric(ages_asfr) ) -# <<<<<<< HEAD + # # Check in dimensions are ok - still working on this # if(ncol(asfr_mat) == ncol(pop_f_mat) -1 & nrow(sr_f_mat) == nrow(pop_f_mat) -1){ # print("matrix dimensions are correct") @@ -389,22 +389,23 @@ mig_resid_stock <- function(pop_m_mat, # asfr_mat # sr_f_mat # } -# ======= -# >>>>>>> 362ae9857574b05c519c8de40548461d6b9070dd + # Migration net of only survivors - net_mig_m <- migresid_net_surv(pop_m_mat, sr_m_mat) - net_mig_f <- migresid_net_surv(pop_f_mat, sr_f_mat) + net_mig_m <- migresid_net_surv(pop_mat = pop_m_mat, + sr_mat = sr_m_mat) + net_mig_f <- migresid_net_surv(pop_mat = pop_f_mat, + sr_mat = sr_f_mat) # fertility_index <- which(ages %in% ages_asfr) # Returns all births for all years age_interval <- unique(diff(ages)) all_births <- migresid_births( - pop_f_mat, - asfr_mat, + pop_f_mat = pop_f_mat, + asfr_mat = asfr_mat, # fertility_index, - age_interval + age_interval = age_interval ) # With all_births already calculated, separate between @@ -414,17 +415,17 @@ mig_resid_stock <- function(pop_m_mat, births_f <- all_births * (1 / (1 + srb_vec[byrs])) net_mig_m <- migresid_net_surv_first_ageg( - net_mig_m, - pop_m_mat, - births_m, - sr_m_mat + net_mig = net_mig_m, + pop_mat = pop_m_mat, + births = births_m, + sr_mat = sr_m_mat ) net_mig_f <- migresid_net_surv_first_ageg( - net_mig_f, - pop_f_mat, - births_f, - sr_f_mat + net_mig = net_mig_f, + pop_mat = pop_f_mat, + births = births_f, + sr_mat = sr_f_mat ) # First year is empty, so we exclude @@ -490,12 +491,12 @@ mig_resid_cohort <- function(pop_m_mat, # Adjust last age group in the bounds mig_bounds <- migresid_bounds_last_ageg( - net_mig_m, - net_mig_f, - mig_upper_m, - mig_lower_m, - mig_upper_f, - mig_lower_f + net_mig_m = net_mig_m, + net_mig_f = net_mig_f, + mig_upper_m = mig_upper_m, + mig_lower_m = mig_lower_m, + mig_upper_f = mig_upper_f, + mig_lower_f = mig_lower_f ) mig_upper_m <- mig_bounds$mig_upper_m @@ -540,13 +541,6 @@ mig_resid_time <- function(pop_m_mat, asfr_mat <- args_list$asfr_mat srb_vec <- args_list$srb_vec - # TR: add chunk (maybe a new function?) that - # checks dimensions; names dimensions if necessary (and warns if so) - # and trims dimensions if necessary (warning user if needed). - # warning does mean warning() it just means cat("\nwatch out!\n") - # Not important, but theese could be silenced using a new 'verbose' arg - - # Estimate stock method mig_res <- mig_resid_stock( @@ -604,7 +598,9 @@ migresid_net_surv <- function(pop_mat, sr_mat) { # is treated by migresid_net_surv_first_ageg. res[nrow(res), ] <- NA res <- rbind(matrix(NA, nrow = 1, ncol = ncol(res)), res) - res <- migresid_net_surv_last_ageg(res, pop_mat, sr_mat) + res <- migresid_net_surv_last_ageg(net_mig = res, + pop_mat = pop_mat, + sr_mat = sr_mat) rownames(res) <- rownames(pop_mat) colnames(res) <- colnames(pop_mat)[-p] res diff --git a/R/mig_un_fam.R b/R/mig_un_fam.R index a5f516c19..81a2c8e8b 100644 --- a/R/mig_un_fam.R +++ b/R/mig_un_fam.R @@ -1,9 +1,9 @@ #' Net migration by age for an UN family #' @description Given a total net migration, #' calculate the net migration age schedule based on the Rogers and Castro formula for UN families. -#' @param NM numeric. Total net migration to distribuite between ages and sex. +#' @param NM numeric. Total net migration to distribute between ages and sex. #' @param family character. Could be "Family", "Female Labor", "Male Labor". -#' @param Single logical. Results by simple age. Default `FALSE`. +#' @param Single logical. Results by simple age. Default `TRUE`. #' Typically from pre-working age and working age parts of in Roger-Castro formula. #' @param OAnew The age from which to group all ages into an open ended age group. #' By default it is set to 100, so it groups all ages up to 120, which is the diff --git a/R/nAx.R b/R/nAx.R index d7c0e5f7d..729fcd1d2 100644 --- a/R/nAx.R +++ b/R/nAx.R @@ -788,9 +788,14 @@ lt_a_un <- function(nMx, Sex = Sex, region = region, mod = mod, - # no need to redo extrap in here + # we need to redo extrap in here because otherwise extrapFit + # might be length < 2 and raise problems with MortalityLaws + extrapLaw = extrapLaw, + extrapFrom = extrapFrom, + extrapFit = extrapFit, ... ) + qxnew <- lt_id_ma_q( nMx = mxi, diff --git a/R/smooth_age_5.R b/R/smooth_age_5.R index 45c8a1392..bc73a2246 100644 --- a/R/smooth_age_5.R +++ b/R/smooth_age_5.R @@ -63,11 +63,11 @@ smooth_age_5_cf <- function(Value, out <- Value5 * NA # cut back down (depending) and name interleaf <- c(rbind(vodds, vevens)) - + if (start_on == 5){ interleaf <- interleaf[-1] } - + n <- min(c(length(interleaf), N)) out[1:n] <- interleaf[1:n] @@ -115,7 +115,7 @@ smooth_age_5_kkn <- function(Value, # would need to move this up to ensure? # or in case of 85+ would we want to keep 80-84, 85+ as-is? - Value10 <- groupAges(Value5, Age = Age, N = 10, shiftdown = start_on) + Value10 <- groupAges(Value5, Age = Age5, N = 10, shiftdown = start_on) # what OAG is a strange digit? Then take OAG after grouping. if (OAG) { @@ -133,21 +133,21 @@ smooth_age_5_kkn <- function(Value, vodds <- Value10 / 2 + (v10R - v10L) / 16 # constrained in 10-year age groups vevens <- Value10 - vodds - + # if (start_on == 5){ # # this is the KNN operation # vevens <- Value10 / 2 + (v10R - v10L) / 16 # # constrained in 10-year age groups # vodds <- Value10 - vevens # } - # + # # stagger odd even 5s interleaf <- c(rbind(vodds, vevens)) - + if (start_on == 5){ interleaf <- interleaf[-1] } - + # produce results vector out <- Value5 * NA n <- min(c(length(interleaf), N)) @@ -442,14 +442,24 @@ smooth_age_5_zigzag <- function(Value, } #' Smooth in 5-year age groups using a moving average +#' #' @description Smooth data in 5-year age groups. #' @details This function calls \code{smooth_age_5_zigzag_inner()}, but prepares data in a way consistent with other methods called by \code{smooth_age_5()}. It is probably preferable to call \code{zigzag()} from the top level, or else call this method from \code{agesmth()} for more control over tail imputations. +#' #' @param Value numeric vector of (presumably) counts in 5-year age groups. #' @param Age integer vector of age group lower bounds. #' @param OAG logical. Whether or not the top age group is open. Default \code{TRUE}. #' @param n integer. The width of the moving average. Default 3 intervals (x-5 to x+9). +#' @param tails logical. If tails is \code{FALSE}, both tails are left untouched. +#' Otherwise, the tails are filled out using a cascade method. +#' #' @return numeric vector of smoothed counts in 5-year age groups. -#' @details This function calls \code{mav()}, which itself relies on the more general \code{ma()}. We lose the lowest and highest ages with this method, unless \code{n=1}, in which case data is returned in the original 5-year age groups. The total population count is not constrained to sum to the orignal total. +#' +#' @details If tails is set to \code{FALSE}, this function calls \code{mav()}, which itself relies on the more general \code{ma()}. We lose the lowest and highest ages with this method, unless \code{n=1}, in which case data is returned in the original 5-year age groups. The total population count is not constrained to sum to the original total. +#' +#' If tails is \code{TRUE}, the same results are expected but the tails are +#' filled in using a cascading method. +#' #' @examples #' Age <- c(0,1,seq(5,90,by=5)) #' # defaults @@ -466,11 +476,11 @@ smooth_age_5_zigzag <- function(Value, #' legend("topright", col = cols, lty = 1, lwd = lwds, legend = paste("n =",1:5)) #' } #' @export - smooth_age_5_mav <- function(Value, Age, OAG = TRUE, - n = 3) { + n = 3, + tails = FALSE) { Value <- groupAges(Value, Age = Age, N = 5) Age <- as.integer(names(Value)) @@ -479,7 +489,8 @@ smooth_age_5_mav <- function(Value, Value = Value, Age = Age, OAG = OAG, - n = n + n = n, + tails = tails ) Smoothed @@ -615,10 +626,10 @@ smooth_age_5_feeney <- function(Value, #' Smooth populations in 5-year age groups using various methods #' #' @description Smooth population counts in 5-year age groups using the Carrier-Farrag, -#' Karup-King-Newton, Arriaga, United Nations, Stong, or Zigzag methods. Allows for imputation +#' Karup-King-Newton, Arriaga, United Nations, Strong, MAV or Zigzag methods. Allows for imputation #' of values in the youngest and oldest age groups for the Carrier-Farrag, Karup-King-Newton, #' and United Nations methods. - +#' #' @details The Carrier-Farrag, Karup-King-Newton (KKN), and Arriaga methods do not modify the totals #' in each 10-year age group, whereas the United Nations, Strong, Zigzag, and moving average (MAV) methods do. The age intervals #' of input data could be any integer structure (single, abridged, 5-year, other), but @@ -627,8 +638,8 @@ smooth_age_5_feeney <- function(Value, #' #' The Carrier-Farrag, Karup-King-Newton, and United Nations methods do not produce estimates #' for the first and final 10-year age groups. By default, these are imputed with the original 5-year age group totals, but -#' you can also specify to impute with \code{NA}, or the results of the Arriaga or -#' Strong methods. If the terminal digit of the open age group is 5, then the terminal 10-year +#' you can also specify to impute with \code{NA}, or the results of the Arriaga, +#' Strong and Cascade methods. If the terminal digit of the open age group is 5, then the terminal 10-year #' age group shifts down, so imputations may affect more ages in this case. Imputation can follow #' different methods for young and old ages. #' @@ -640,7 +651,7 @@ smooth_age_5_feeney <- function(Value, #' #' @param Value numeric vector of counts in single, abridged, or 5-year age groups. #' @param Age integer vector of ages corresponding to the lower integer bound of the counts. -#' @param method character string. Options include \code{"Carrier-Farrag"},\code{"Arriaga"},\code{"KKN"},\code{"United Nations"}, \code{"Strong"}, and \code{"Zigzag"}. See details. Default \code{"Carrier-Farrag"}. +#' @param method character string. Options include \code{"Carrier-Farrag"},\code{"Arriaga"},\code{"KKN"},\code{"United Nations"}, \code{"Strong"}, \code{MAV} and \code{"Zigzag"}. See details. Default \code{"Carrier-Farrag"}. #' @param OAG logical. Whether or not the top age group is open. Default \code{TRUE}. #' @param ageMin integer. The lowest age included included in intermediate adjustment. Default 10. Only relevant for Strong method. #' @param ageMax integer. The highest age class included in intermediate adjustment. Default 65. Only relevant for Strong method. @@ -756,7 +767,7 @@ smooth_age_5 <- function(Value, ageMin = 10, ageMax = 65, n = 3, - young.tail = c("Original", "Arriaga", "Strong", NA), + young.tail = c("Original", "Arriaga", "Strong", "Cascade", NA), old.tail = young.tail) { method <- match.arg(method, c("Carrier-Farrag", @@ -766,8 +777,10 @@ smooth_age_5 <- function(Value, "Strong", "Zigzag", "MAV")) - young.tail <- match.arg(young.tail, c("Original", "Arriaga", "Strong", NA)) - old.tail <- match.arg(old.tail, c("Original", "Arriaga", "Strong", NA)) + + tail_methods <- c("Original", "Arriaga", "Strong", "Cascade", NA) + young.tail <- match.arg(young.tail, tail_methods) + old.tail <- match.arg(old.tail, tail_methods) method <- simplify.text(method) young.tail <- simplify.text(young.tail) @@ -858,6 +871,8 @@ smooth_age_5 <- function(Value, original <- groupAges(Value, Age = Age, N = 5) arriaga <- smooth_age_5_arriaga(Value, Age = Age, OAG = OAG) strong <- smooth_age_5_strong(Value, Age = Age, OAG = OAG) + mav_tails <- smooth_age_5_mav(Value, Age = Age, OAG = OAG, tails = TRUE) + # are the final entries NAs? if (nrle$values[length(nrle$values)] == 1 & !is.na(old.tail)) { nrle$values[1] <- 0 @@ -877,7 +892,10 @@ smooth_age_5 <- function(Value, stopifnot(length(strong) == length(out)) out[old.ind] <- strong[old.ind] } - + if (old.tail == "cascade") { + stopifnot(length(mav_tails) == length(out)) + out[old.ind] <- mav_tails[old.ind] + } } nrle <- rle(as.integer(nas)) # take care of young tail @@ -900,6 +918,11 @@ smooth_age_5 <- function(Value, stopifnot(length(strong) == length(out)) out[young.ind] <- strong[young.ind] } + if (young.tail == "cascade") { + stopifnot(length(mav_tails) == length(out)) + out[young.ind] <- mav_tails[young.ind] + } + } } # end tail operations diff --git a/R/utils.R b/R/utils.R index 655601b1b..a47457e2d 100644 --- a/R/utils.R +++ b/R/utils.R @@ -196,7 +196,9 @@ getModelLifeTable <- function(ModelName, Sex) { avg_adj <- function(x) { n <- length(x) - (shift.vector(x,-1, NA) + shift.vector(x, 1, NA)) / 2 + out <- (shift.vector(x,-1, NA) + shift.vector(x, 1, NA)) / 2 + names(out) <- names(x) + out } #' convert strings to concatenation of lower case alphabet diff --git a/R/utilsAge.R b/R/utilsAge.R index 57da8c721..1c6082b01 100644 --- a/R/utilsAge.R +++ b/R/utilsAge.R @@ -58,7 +58,7 @@ AGEN <- #' @param shiftdown integer. Move the grouping down by one or more single ages. Optional argument. #' #' @details If you shift the groupings, then the first age groups may have a negative lower bound -#' (for example of -5). These counts would be discarded for the osculatory version of Sprague smoothing, +#' (for example of -5). These counts would be discarded for the oscillatory version of Sprague smoothing, #' for example, but they are preserved in this function. The important thing to know is that if you shift #' the groups, the first and last groups won't be N years wide. For example if \code{shiftdown} is 1, #' the first age group is 4-ages wide. @@ -272,7 +272,7 @@ age2int <- function(Age, OAG = TRUE, OAvalue = NA) { #' @return Vector of counts in N-year age groups. #' #' @details If you shift the groupings, then the first age groups may have a negative lower bound -#' (for example of -5). These counts would be discarded for the osculatory version of Sprague smoothing, +#' (for example of -5). These counts would be discarded for the oscillatory version of Sprague smoothing, #' for example, but they are preserved in this function. The important thing to know is that if you shift #' the groups, the first and last groups will not be N years wide. For example if \code{shiftdown} is 1, the first age group is 4-ages wide. The ages themselves are not returned, #' but they are the name attribute of the output count vector. Note this will also correctly group abridged ages @@ -324,6 +324,7 @@ groupOAG <- function(Value, Age, OAnew) { N <- length(Value[Age <= OAnew]) Value[N] <- sum(Value[Age >= OAnew]) Value <- Value[1:N] + names(Value) <- Age[1:N] Value } @@ -331,7 +332,7 @@ groupOAG <- function(Value, Age, OAnew) { #' check for coherence within Age and between Age and AgeInt #' #' @description A few checks are carried out to test if \code{Age} is internally consistent, that \code{OAG} is consistent with \code{AgeInt}, and that \code{Age} and \code{AgeInt} are consistent with one another. For \code{Age} to be internally consistent, we cannot have redundant values, and values must be sequential. -#' @details If \code{OAG} is \code{TRUE} then \code{AgeInt} must be coded as \code{NA}. If \code{Age} is not sorted then we sort both \code{Age} and \code{AgeInt}, assuming that they are in matched order. This isn't incoherence per se, but a message is returned to the console. +#' @details If \code{OAG} is \code{TRUE} then \code{AgeInt} must be coded as \code{NA}. If \code{Age} is not sorted then we sort both \code{Age} and \code{AgeInt}, assuming that they are in matched order. This isn't incoherence in itself, but a message is returned to the console. #' #' @param Age integer vector of single ages (lower bound) #' @param AgeInt integer vector. Age interval widths @@ -724,7 +725,7 @@ rescaleAgeGroups <- function(Value1, } #' force a (count) vector to abridged ages -#' @description This is a robustness utility, in place to avoid annoying hang-ups in \code{LTAbr()}. If data are given in non-standard ages, they are forced to standard abrdiged ages on the fly. Really this should happen prior to calling \code{lt_abridged()} +#' @description This is a robustness utility, in place to avoid annoying hang-ups in \code{LTAbr()}. If data are given in non-standard ages, they are forced to standard abridged ages on the fly. Really this should happen prior to calling \code{lt_abridged()} #' @details This should be able to group up and group down as needed. \code{graduate_mono()} is used below the hood. \code{pclm()} or \code{graduate_uniform()} out to be flexible enough to do the same. #' @inheritParams graduate_uniform #' @seealso graduate_mono_closeout, lt_abridged @@ -734,14 +735,16 @@ rescaleAgeGroups <- function(Value1, #' Age <- c(0,1,3,seq(5,100,5)) #' AgeInt <- c(1,2,2,rep(5,19),1) #' Value <- tapply(V1,rep(Age,times=AgeInt), sum) -#' +#' #' is_abridged(Age) -#' age_abridge_force(Value, AgeInt, Age) -age_abridge_force <- function(Value, AgeInt, Age) { +#' age_abridge_force(Value, Age) +age_abridge_force <- function(Value, Age) { + v1 <- graduate_mono_closeout( Value, Age = Age) - a1 <- min(Age):(length(v1) - 1) + #a1 <- min(Age):(length(v1) - 1) + a1 <- (1:length(v1) - 1 + min(Age)) |> as.integer() AgeAbr <- calcAgeAbr(a1) vabr <- tapply(v1, AgeAbr, sum) vabr diff --git a/R/utils_downloads.R b/R/utils_downloads.R index af435bda2..2627408fc 100644 --- a/R/utils_downloads.R +++ b/R/utils_downloads.R @@ -3,7 +3,7 @@ # and potentially others. #' Extract Lx estimates from WPP2019. Mainly an util function for other ones. -#' @description We extract `Lx` from `wpp2019`, interpolated to exact dates. Different methods availables. +#' @description We extract `Lx` from `wpp2019`, interpolated to exact dates. Different methods available. #' A vector of countries can handle, but with an unique sex. Row names are not indicative of countries. #' @param nLx numeric. either `NULL` or a numeric vector of lifetable exposure. If it's the second then we just pass it back. #' @param location vector. UN Pop Div `LocName` or `LocID` @@ -11,7 +11,7 @@ #' @param nLxDatesIn numeric. Vector of three decimal dates produced by (or passed through) `basepop_five()` #' @param method character. Could be `"linear"`, `"exponential"`, or `"power"` #' -#' @return numeric matrix of `nLx` with `length(nLxDatesIn)` and abrdiged ages in rows. +#' @return numeric matrix of `nLx` with `length(nLxDatesIn)` and abridged ages in rows. #' @export #' @importFrom stats setNames #' @importFrom stats reshape @@ -58,7 +58,7 @@ downloadnLx <- function(nLx, location, gender, nLxDatesIn, method="linear") { } if (!any(fertestr::is_LocID(location))) { location_code <- fertestr::get_location_code(location) - }else { + } else { location_code <- as.integer(location) } @@ -121,7 +121,7 @@ downloadnLx <- function(nLx, location, gender, nLxDatesIn, method="linear") { } #' Extract ASFR estimates from WPP2019. Mainly an util function for other ones. -#' @description We extract `ASFRx` from `wpp2019`, interpolated to exact dates. Different methods availables. +#' @description We extract `ASFRx` from `wpp2019`, interpolated to exact dates. Different methods available. #' A vector of countries can handle, but with an unique sex. Row names are not indicative of countries. #' @param Asfrmat numeric. #' @param location vector. UN Pop Div `LocName` or `LocID` @@ -262,7 +262,7 @@ downloadSRB <- function(SRB, location, DatesOut, verbose = TRUE){ fetch_wpp_births <- function(births, yrs_births, location, sex, verbose) { # fetch WPP births if not provided by user - if (is.null(births)) { + if (is.null(births) | length(births) == 0) { # load WPP births requireNamespace("DemoToolsData", quietly = TRUE) @@ -316,7 +316,8 @@ interp_coh_download_mortality <- function(location, sex, date1, date2, OAnew = 1 }) %>% lapply(lt_a2s_chunk, OAnew = OAnew) %>% lapply(function(X){ - 1 - X$nqx + #1 - X$nqx + lt_id_Ll_S(X$nLx, X$lx, X$Age, X$AgeInt, N = 1) }) %>% do.call("cbind",.) diff --git a/README.md b/README.md index 2e0ec7c56..2c972ce7d 100644 --- a/README.md +++ b/README.md @@ -5,19 +5,19 @@ [![R build status](https://github.com/timriffe/DemoTools/workflows/R-CMD-check/badge.svg)](https://github.com/timriffe/DemoTools/actions) [![codecov](https://codecov.io/gh/timriffe/DemoTools/branch/master/graph/badge.svg)](https://codecov.io/gh/timriffe/DemoTools) -[![](https://img.shields.io/badge/devel%20version-01.13.39-yellow.svg)](https://github.com/timriffe/DemoTools) +[![](https://img.shields.io/badge/devel%20version-01.13.79-yellow.svg)](https://github.com/timriffe/DemoTools) [![issues](https://img.shields.io/github/issues-raw/timriffe/DemoTools.svg)](https://github.com/timriffe/DemoTools/issues) [![lifecycle](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) # Tools for aggregate demographic analysis -Date: 2021-03-23 +Date: 2023-12-20 `DemoTools` is an R package that contains simple functions often used in demographic analysis. It is in active development. This project is commissioned by the [UN Population Division](http://www.un.org/en/development/desa/population/) and financed by the [Bill and Melinda Gates Foundation](https://www.gatesfoundation.org/) as part of the [Making Family Planning Count](http://www.un.org/en/development/desa/population/projects/making-family-planning-count/index.shtml) project. Work is also done in collaboration with Sean Fennell, [José Manuel Aburto](https://github.com/jmaburto), [Ilya Kashnitsky](https://ikashnitsky.github.io/), [Marius Pascariu](https://github.com/mpascariu), [Jorge Cimentada](https://github.com/cimentadaj), [Monica Alexander](https://www.monicaalexander.com/), and with minor contributions from [several more](https://github.com/timriffe/DemoTools/graphs/contributors) (thank you!). This work is licensed under the Creative Commons Attribution-ShareAlike 3.0 IGO ([CC BY-SA 3.0 IGO](https://creativecommons.org/licenses/by-sa/3.0/igo/)). -The idea behind `DemoTools` is to provide a common set of functions that can be easily used by analysts and scientists working on demographic analysis and modelling. +The idea behind `DemoTools` is to provide a common set of functions that can be easily used by analysts and scientists working on demographic analysis and modeling. If you detect a bug or have a suggestion please notify us using the [Issues](https://github.com/timriffe/DemoTools/issues) tab on github. Even better if you fix it and make a pull request! See [CONTRIBUTING.md](https://github.com/timriffe/DemoTools/blob/master/CONTRIBUTING.md) for more tips on reporting bugs or offering patches. @@ -29,12 +29,11 @@ If you are getting started with `DemoTools` we recommend taking a look at the tu You can load the ```DemoTools``` package in R like so: ```r -# install.packages("devtools") +# install.packages("remotes") -library(devtools) # requires the development version of rstan, sorry! install.packages("rstan", repos = c("https://mc-stan.org/r-packages/", getOption("repos"))) -install_github("timriffe/DemoTools") +remotes::install_github("timriffe/DemoTools") ``` ## Citation diff --git a/_pkgdown.yml b/_pkgdown.yml index 0c43c45ef..b110f0e37 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -10,8 +10,8 @@ home: title: DemoTools description: Do you love demography and data? If so, you might enjoy using this package. links: - - text: UN Deparment of Economic and Social Affairs Population - href: https://www.un.org/en/development/desa/population/index.asp + - text: United Nations Population Division + href: https://www.un.org/development/desa/pd/ - text: Browse source code href: https://github.com/timriffe/DemoTools/ @@ -47,6 +47,12 @@ reference: desc: Functions to construct a lifetable contents: - '`lt_abridged`' + - '`lt_single_mx`' + - '`lt_single_qx`' + - '`lt_single2abridged`' + - '`lt_abridged2single`' + - '`lt_ambiguous`' + - '`lt_smooth_ambiguous`' - title: "Interpolation" desc: Functions to interpolate counts contents: @@ -54,6 +60,7 @@ reference: - '`interp_coh`' - title: "Extrapolation" desc: Functions to interpolate/extrapolate rates or counts + contents: - '`interp_lc_lim`' - '`lt_rule_m_extrapolate`' - '`OPAG`' diff --git a/data/asfr_mat_five.rda b/data/asfr_mat_five.rda index 78b67259b..3aa59a764 100644 Binary files a/data/asfr_mat_five.rda and b/data/asfr_mat_five.rda differ diff --git a/data/pop_f_mat_five.rda b/data/pop_f_mat_five.rda index aea76202a..115355c24 100644 Binary files a/data/pop_f_mat_five.rda and b/data/pop_f_mat_five.rda differ diff --git a/dev/.gitignore b/dev/.gitignore index 2d6e1e81a..a21fc36c9 100644 --- a/dev/.gitignore +++ b/dev/.gitignore @@ -4,3 +4,5 @@ transitivitytests.R scratch.R junk.R +testLT.R +lx2.RData diff --git a/dev/FunctionInventory.csv b/dev/FunctionInventory.csv new file mode 100644 index 000000000..72f6628a5 --- /dev/null +++ b/dev/FunctionInventory.csv @@ -0,0 +1,171 @@ +Script,Function +LIFIT.R,ADM +utilsAge.R,age_abridge_force +utilsAge.R,age2ageN +utilsAge.R,age2int +utilsAge.R,AGEN +AGESEX.R,ageRatioScore +AGESEX.R,ageSexAccuracy +AGESEX.R,ageSexAccuracyDasGupta +OGIVE.R,agesmth1 +basepop.R,ArgsCheck +utils.R,avg_adj +basepop.R,basepop_five +GRPOPYB.R,birthCohorts +utilsAge.R,calcAgeAbr +utilsAge.R,calcAgeN +interp_coh.R,check_args +check_heaping.R,check_heaping_bachi +check_heaping.R,check_heaping_coale_li +check_heaping.R,check_heaping_jdanov +check_heaping.R,check_heaping_kannisto +check_heaping.R,check_heaping_myers +check_heaping.R,check_heaping_noumbissi +check_heaping.R,check_heaping_roughness +check_heaping.R,check_heaping_sawtooth +check_heaping.R,check_heaping_spoorenberg +check_heaping.R,check_heaping_whipple +utils.R,dec.date +utils_downloads.R,downloadAsfr +utils_downloads.R,downloadnLx +utils_downloads.R,downloadSRB +utils_downloads.R,fetch_wpp_births +lt_model_lq.R,find_my_case +utils_downloads.R,get_LocID +utils_downloads.R,get_LocName +utils.R,getModelLifeTable +graduate.R,graduate +graduate.R,graduate_beers +graduate.R,graduate_beers_expand +graduate.R,graduate_beers_johnson +graduate.R,graduate_grabill +graduate.R,graduate_grabill_expand +graduate.R,graduate_mono +graduate.R,graduate_mono_closeout +graduate.R,graduate_pclm +graduate.R,graduate_sprague +graduate.R,graduate_sprague_expand +graduate.R,graduate_uniform +utilsAge.R,groupAges +utilsAge.R,groupOAG +check_heaping.R,heapify +IRDID.R,ID +utilsAge.R,inferAgeIntAbr +utilsAge.R,int2age +utilsAge.R,int2ageN +AGEINT.R,interp +interp_coh.R,interp_coh +utils_downloads.R,interp_coh_download_mortality +interp_coh.R,interp_coh_lxMat_pxt +interp_lc_lim.R,interp_lc_lim +interp_lc_lim.R,interp_lc_lim_abk_m +interp_lc_lim.R,interp_lc_lim_estimate +interp_lc_lim_group.R,interp_lc_lim_group +interp_lc_lim.R,interp_lc_lim_kt_min +AGEINT.R,interpolatePop +IRDID.R,IRD +utilsAge.R,is_abridged +utilsAge.R,is_age_coherent +utilsAge.R,is_age_redundant +utilsAge.R,is_age_sequential +utils_downloads.R,is_Loc_available +utilsAge.R,is_single +utils_downloads.R,loc_message +OGIVE.R,loess_smth1 +nAx.R,lt_a_closeout +nAx.R,lt_a_pas +nAx.R,lt_a_un +interp_coh.R,lt_a2s_chunk +lt_abridged.R,lt_abridged +lt_regroup_age.R,lt_abridged2single +lt_regroup_age.R,lt_ambiguous +lt_id.R,lt_id_d_l +lt_id.R,lt_id_d_q +lt_id.R,lt_id_l_d +lt_id.R,lt_id_l_q +lt_id.R,lt_id_L_T +lt_id.R,lt_id_lda_L +lt_id.R,lt_id_Ll_S +lt_id.R,lt_id_ma_q +nAx.R,lt_id_morq_a +nAx.R,lt_id_morq_a_greville +lt_id.R,lt_id_q_l +lt_id.R,lt_id_qa_m +lt_id.R,lt_id_qm_a +basepop.R,lt_infer_radix_from_1L0 +lt_model_lq.R,lt_model_lq +lt_rule.R,lt_rule_1a0 +lt_rule.R,lt_rule_1a0_ak +nAx.R,lt_rule_1a0_cd +nAx.R,lt_rule_4a1_cd +lt_rule.R,lt_rule_4m0_D0 +lt_rule.R,lt_rule_4m0_m0 +lt_rule.R,lt_rule_ak_m0_a0 +lt_rule.R,lt_rule_ak_q0_a0 +extra_mortality.R,lt_rule_m_extrapolate +lt_single.R,lt_single_mx +lt_single_qx.R,lt_single_qx +lt_regroup_age.R,lt_single2abridged +interp_lc_lim.R,lt_smooth_ambiguous +lt_model_lq.R,lthat.logquad +utils.R,ma +MAV.R,mav +MAV.R,mav_tails +utilsAge.R,maxA2abridged +mig_beta.R,mig_beta +mig_beta.R,mig_beta_constant_child +mig_beta.R,mig_beta_cwr +mig_rc.R,mig_calculate_rc +mig_rc.R,mig_estimate_rc +mig_resid.R,mig_resid +mig_resid.R,mig_resid_cohort +mig_resid.R,mig_resid_dim_checker +mig_resid.R,mig_resid_stock +mig_resid.R,mig_resid_time +mig_un_fam.R,mig_un_fam +mig_resid.R,migresid_births +mig_resid.R,migresid_bounds +mig_resid.R,migresid_bounds_last_ageg +mig_resid.R,migresid_net_surv +mig_resid.R,migresid_net_surv_first_ageg +mig_resid.R,migresid_net_surv_last_ageg +utilsAge.R,names2age +OPAG.R,OPAG +OPAG.R,OPAG_fit_stable_standard +OPAG.R,OPAG_nLx_warp_r +OPAG.R,OPAG_r_min +OPAG.R,OPAG_simple +OGIVE.R,poly_smth1 +utils.R,ratx +LIFIT.R,RDM +utils.R,rescale_vector +utilsAge.R,rescaleAgeGroups +interp_coh.R,reshape_pxt +utils.R,rlog +interp_coh.R,rup +AGESEX.R,sexRatioScore +interp_coh.R,shift_census_ages_to_cohorts +utils.R,shift.vector +utils.R,simplify.text +utils.R,single2abridged +smooth_age_5.R,smooth_age_5 +smooth_age_5.R,smooth_age_5_arriaga +smooth_age_5.R,smooth_age_5_cf +smooth_age_5.R,smooth_age_5_feeney +smooth_age_5.R,smooth_age_5_kkn +smooth_age_5.R,smooth_age_5_mav +smooth_age_5.R,smooth_age_5_strong +smooth_age_5.R,smooth_age_5_un +smooth_age_5.R,smooth_age_5_zigzag +ZIGZAG.R,smooth_age_5_zigzag_inner +ZIGZAG.R,smooth_age_5_zigzag_min +ZIGZAG.R,smooth_age_5_zigzag_p +SPENCER.R,spencer +splitOscillate.R,splitOscillate +CENSUR.R,surv10 +CENSUR.R,surv5 +CENSUR.R,survN +CENSUR.R,survRatioError +interp_coh.R,transform_datesout +interp_coh.R,transform_pxt +ZELNIK.R,zelnik diff --git a/dev/arriaga_tidy.R b/dev/arriaga_tidy.R new file mode 100644 index 000000000..0ae5e9263 --- /dev/null +++ b/dev/arriaga_tidy.R @@ -0,0 +1,164 @@ +Ages <- seq(0, 80, by = 5) +AMales <- smooth_age_5_arriaga(Value = pop5m_pasex, Age = Ages, OAG = TRUE) +# PAS spreadsheet result: +Atest <- c(662761, 495126, 345744, 287629, 285919, 261018, 237469, 203277, + 161733, 126960, 88586, 67496, 54587, 41257, 28790, 17189, 34729) +all(round(AMales) - Atest == 0, na.rm = TRUE) +plot(Ages, pop5m_pasex) +lines(as.integer(names(AMales)),AMales) +# Before: +smooth_age_5_arriaga <- function(Value, + Age, + OAG = TRUE) { + + # these values are not used, it's just for lengths, and to make sure we + # end on an even 10. Technically we could even provide data in 10-year + # age groups and it'd still not break. + Value1 <- graduate_uniform(Value = Value, Age = Age, OAG = OAG) + Value5 <- + groupAges(Value1, Age = as.integer(names(Value1)), N = 5) + N <- length(Value5) + Age5 <- as.integer(names(Value5)) + + # would need to move this up to ensure? + # or in case of 85+ would we want to keep 80-84, 85+ as-is? + Value10 <- groupAges(Value, Age = Age, N = 10) + + # what OAG is a strange digit? Then take OAG after grouping. + if (OAG) { + OAGvalue <- Value5[length(Value5)] + Value10[length(Value10)] <- NA + Value5[length(Value5)] <- NA + } + + # again get staggered vectors + Value10LL <- shift.vector(Value10,-2, fill = NA) + Value10L <- shift.vector(Value10,-1, fill = NA) + Value10R <- shift.vector(Value10, 1, fill = NA) + Value10RR <- shift.vector(Value10, 2, fill = NA) + + # alternating calc, with differences at tails + vevens <- (-Value10R + 11 * Value10 + 2 * Value10L) / 24 + # tails different + vevens[1] <- + (8 * Value10[1] + 5 * Value10L[1] - Value10LL[1]) / 24 + lastind <- which(is.na(vevens))[1] + vevens[lastind] <- + Value10[lastind] - (8 * Value10[lastind] + 5 * Value10R[lastind] - Value10RR[lastind]) / 24 + # odds are complement + vodds <- Value10 - vevens + + # prepare output + interleaf <- c(rbind(vodds, vevens)) + # produce results vector + out <- Value5 * NA + n <- min(c(length(interleaf), N)) + out[1:n] <- interleaf[1:n] + + # if OA ends in 5, then we can save penultimate value too + na.i <- is.na(out) + out[na.i] <- Value5[na.i] + if (OAG) { + out[N] <- OAGvalue + } + + out +} +library(tidyverse) +data <- tibble(Age = Ages, Pop = pop5m_pasex) + +age2single <- function(Age, OAG = TRUE, AgeInt = NULL){ + maxA <- ifelse(OAG, max(Age), max(Age) + AgeInt[which.max(Age)]) + min(Age):maxA +} + +library(collapse) +library(data.table) +library(tidyfast) +smooth_age_5_arriaga_tidy <- function(data, variable, OAG = TRUE){ + + if (OAG){ + OAvalue <- data |> + fsubset(Age == max(Age)) |> + pull(!!sym(variable)) + } + + data <- + data |> + rename(V = !!variable) + V <- data$V + A <- data$Age + V1 <- graduate_uniform(Value = V, + Age = Age, + OAG = OAG) + A1 <- age2single(A, OAG = OAG) + # force to single + # reframe( + # V = graduate_uniform(Value = V, + # Age = Age, + # OAG = OAG), + # Age = age2single(Age)) + data.frame(V = V1, Age = A1) |> + # data |> + # group to 5 (innocuous if 5-year data given) + fmutate(Age = Age - Age %% 5, + V = fifelse(Age == max(Age) & OAG, NA_real_, V)) |> + fgroup_by(Age) |> + fsummarize(V = sum(V)) |> + fungroup() |> + fmutate(Age10 = Age - Age %% 10) |> + fmutate(V10 = sum(V), .by = Age10) |> + fmutate(V10LL = flag(V10,-4), + V10L = flag(V10,-2), + V10R = flag(V10, 2), + V10RR = flag(V10,4), + vevens = dt_case_when(Age10 == min(Age10) ~ (8 * V10 + 5 * V10L - V10LL) / 24, + Age10 == (max(Age10) - 10) ~ V10 - (8 * V10 + 5 * V10R - V10RR) / 24, + TRUE ~ (-V10R + 11 * V10 + 2 * V10L) / 24), + vodds = V10 - vevens, + V5out = dt_case_when( + Age == max(Age)~ OAvalue, + Age %% 10 == 0 ~ vodds, + Age %% 5 == 0 ~ vevens, + TRUE ~ V), + V5out = fifelse(is.na(V5out), V, V5out)) |> + fselect(Age, Age10, V5out) |> + rename(!!variable := V5out) + + } +# ---------------- # +# test equivalency # +# ---------------- # +# for OAG divisible by 10 +smooth_age_5_arriaga_tidy(tibble(Age = Ages, + Pop = pop5m_pasex), + variable = "Pop", + OAG = TRUE) + mutate(V5orig = smooth_age_5_arriaga(pop5m_pasex, Ages, TRUE)) + + +# for OAG divisible by 5 +Age75 <- seq(0,75,by=5) +v75 <- pop5m_pasex +v75[16] <- sum(pop5m_pasex[16:17]) +v75 <- v75[-17] + +data <- tibble(Age = Age75, + Pop = v75) +smooth_age_5_arriaga_tidy(data, + variable = "Pop", + OAG = TRUE) |> + mutate(V5orig = smooth_age_5_arriaga(v75, Age75, TRUE)) + +# ----------------------- # +# Compare execution speed # +# ----------------------- # +library(rbenchmark) +benchmark(smooth_age_5_arriaga_tidy(data, + variable = "Pop", + OAG = TRUE)) # .522 | 0.402 if we remove reframe() +benchmark(smooth_age_5_arriaga(v75, Age75, TRUE)) # .054, 10 times faster... + +# Conclusion: +# Possibly data.table could do the same a tic faster than collapse/tidyfast, +# but only marginally do, whereas base-powered DemoTools arithmetic is 10x faster diff --git a/dev/build.R b/dev/build.R index 9545ed4bb..d6e841d2e 100644 --- a/dev/build.R +++ b/dev/build.R @@ -18,10 +18,12 @@ shhh <- function(expr){ library(devtools) library(TimUtils) +library(magrittr) +library(git2r) #install.packages("backports") #install.packages("roxygen2") #install_github("hadley/devtools") -#install_github("timriffe/TimUtils/TimUtils") +#install_github("timriffe/TimUtils") # do this whenever new functions are added to /R, or whenever roxygen is updated devtools::document() @@ -30,15 +32,28 @@ devtools::document() devtools::build_vignettes() # devtools::install_github("r-lib/pkgdown") +# usethis::proj_activate(here::here()) pkgdown::build_site() -versionIncrement( +TimUtils::versionIncrement( major = FALSE, # only for releases mid = FALSE, # major functionality added minor = TRUE, # whenever documentation renewed, any patch, tweak, or fix maxdigits = c(2,2,3),# maybe 4 required? README = TRUE) # update README dev version badge +# add line to immediately commit and tag. + +# D <- readLines("DESCRIPTION") +# vs <- D[grepl(D,pattern = "Version: ")] %>% gsub(pattern = "Version: ", replacement = "") %>% +# paste0("v",.) +# commit(message = vs,all=TRUE) +# tag(name =vs,message = vs) +# push(refspec = vs) + +# https://raw.githubusercontent.com/timriffe/DemoTools/59a0f4e50b7696c185a3c9d4e582426f88aac84f/DESCRIPTION + + # run this to get access to already-written functions shhh(load_all()) @@ -47,6 +62,9 @@ shhh(load_all()) Sys.setenv('_R_CHECK_SYSTEM_CLOCK_' = 0) devtools::check(force_suggests = TRUE) + +source("version_lookup.R") +update_lookup() #build(pkg = "/home/tim/git/DemoTools", path = "/home/tim/Desktop") #?devtools::build #devtools::use_testthat("/home/tim/git/DemoTools") diff --git a/dev/build_tests.R b/dev/build_tests.R index b7d9d1ed4..63d848227 100644 --- a/dev/build_tests.R +++ b/dev/build_tests.R @@ -55,4 +55,41 @@ str(d2) names(eliminable) <- names(d2) N - eliminable -unlist(d2) %>% unique() \ No newline at end of file +unlist(d2) %>% unique() + +# what file is each function in? + +A <- lsf.str("package:DemoTools") +devtools::load_all() +attr(attr(inferAgeIntAbr,"srcref"),"srcfile") + + +get_file <- function(filename_no_quotes){ + attr(attr(filename_no_quotes,"srcref"),"srcfile") +} +get_file(inferAgeIntAbr) +lapply(ls("package:DemoTools"),function(x) eval() %>% is.function) +is.function(DemoTools::`:=`) + +# list of functions by script + +scripts <- dir("R") +files <- list() + +for (i in scripts){ +test.env <- new.env() +sys.source(paste0("R/",i), envir = test.env) +A <- lsf.str(envir=test.env) +funs <- lapply(A,"[[",1) %>% unlist() +files[[i]] <- funs +rm(A) +rm(test.env) +} +library(tidyverse) + +lengths <- lapply(files, length) %>% unlist() +DF <- tibble(Script = rep(names(lengths), times = lengths), + Function = unlist(files)) +DF %>% + arrange(Function) %>% + write_csv("dev/FunctionInventory.csv") diff --git a/dev/ediev.R b/dev/ediev.R new file mode 100644 index 000000000..0c5d693fc --- /dev/null +++ b/dev/ediev.R @@ -0,0 +1,217 @@ +smooth_age_zigzag_dd <- function( + Value, #population counts by single year of age + Age, #age index for Value: should be a continuous range of integer values (we will not check that) + OAG = TRUE, #is the last age group an open interval? + ret_ggPlot = TRUE, #if TRUE, we return a list(data=ValueOut, ggplot=myPlot) with a plot for checking results + #if FALSE, we return the smoothed series ValueOut + legendPlot = NULL #legend for the ggPlot +) +{ + #Extension of Feeney (2013) "Removing Zig-Zag from Age Data" for single age data + #in the spirit of Dalkhat M. Ediev (2021) "A model of age heaping with applications to population graduation that retains informative demographic variation" + #Main differences with Feeney's algorithm is that, apart for working with single age data + #to suppose that age heaping can start 2 years before (and symmetrically 2 years after) + #and not only 1 year before and after. + #For example for age 40, we suppose that heaping occurs symmetrically, in the same proportion, at + #ages 39 and 41, as Feeney supposed for five-years age groups with adjacent age groups, + #but in the same spirit we suppose that + #heaping can occur also at ages 38 and 42 towards attracting age 40. + #We also suppose that heaping can only occurs for ages multiple of 5 and 10 + if (OAG) { + #if the last age is an open interval, we drop it + AgeRangeOut <- Age[1:(length(Age)-1)] + } + #determine the ages with the variable parameters + #For example we start with ages 3 and 4 for heaping at age 5 and ages 6 and 7 use symmetrical values + #generally speaking heaping can start at ages x-2, x-1 and then symmetrically at ages x+1, x+2 towards attracting age x + #we suppose that the first and the last age we can treat have at least values for 2 ages before and after + #that means that if the first age in Age is 9 or 10, we will skip the first + getAgeRange <- function(AgeRangeOut) { + ageRangeMin <- max(5, trunc((AgeRangeOut[1] + 2) / 5) * 5) + if (ageRangeMin < AgeRangeOut[1] + 2) ageRangeMin <- ageRangeMin + 5 + ageRangeMax <- trunc((AgeRangeOut[length(AgeRangeOut)] - 2) / 5) * 5 + if (ageRangeMax > AgeRangeOut[length(AgeRangeOut)] - 2) ageRangeMax <- ageRangeMax - 5 + return (c(ageRangeMin, ageRangeMax)) + } + ageRange <- getAgeRange (AgeRangeOut) + ageRangeMin <- ageRange[1] + ageRangeMax <- ageRange[2] + #number of ages multiples of 5 + nAges_mult <- trunc ((ageRangeMax - ageRangeMin) / 5) + 1 + if (nAges_mult < 2) stop ("Age range should include at least two consecutive ages multiple of 5") + #initial values for the parameters + #if first attracting age is 5, age 3 has initial proportion of 0.1, age 4 of 0.2 + #these two parameters will get 'optimized' by R optim + #the same parameters are applied, symmetrically, for age 6 (0.2) and age 7 (0.1) + param <- rep (c(0.1, 0.2), nAges_mult) + param_min <- rep(0, nAges_mult * 2) + param_max <- rep(9, nAges_mult * 2) + + computeNewPop <- function(data, par, Age, ageRangeMin, nAges_mult, ret_Props=FALSE) { + if (ret_Props) props <- rep(NA, length(data)) + for (ageIter in (1:nAges_mult)) { + currFirstAge <- ageRangeMin + (ageIter - 1) * 5 - 2 + indexInData <- currFirstAge - Age[1] + 1 + if (ret_Props) { + props[indexInData] <- par[(ageIter - 1) * 2 + 1] + props[indexInData+1] <- par[(ageIter - 1) * 2 + 2] + props[indexInData+3] <- par[(ageIter - 1) * 2 + 2] + props[indexInData+4] <- par[(ageIter - 1) * 2 + 1] + } else { + data[indexInData + 2] <- data[indexInData + 2] - + (data[indexInData] + data[indexInData + 4]) * par[(ageIter - 1) * 2 + 1] - + (data[indexInData + 1] + data[indexInData + 3]) * par[(ageIter - 1) * 2 + 2] + data[indexInData] <- data[indexInData] * (1 + par[(ageIter - 1) * 2 + 1]) + data[indexInData+1] <- data[indexInData+1] * (1 + par[(ageIter - 1) * 2 + 2]) + data[indexInData+3] <- data[indexInData+3] * (1 + par[(ageIter - 1) * 2 + 2]) + data[indexInData+4] <- data[indexInData+4] * (1 + par[(ageIter - 1) * 2 + 1]) + } + } + + if (ret_Props) { + return (props) + } else { + return (data) + } + } + + optim_fun <- function(data, par) { + popIter <- computeNewPop(data, par, Age, ageRangeMin, nAges_mult) + totPop <- sum (popIter) + objective_to_min <- 0 + for (ageIter in (1:nAges_mult)) { + currFirstAge <- ageRangeMin + (ageIter - 1) * 5 - 2 + indexInData <- currFirstAge - Age[1] + 1 + term1 <- ((popIter[indexInData + 2] - (popIter[indexInData + 1] + popIter[indexInData + 3]) / 2)) / totPop + term2 <- ((popIter[indexInData + 2] - (popIter[indexInData] + popIter[indexInData + 4]) / 2)) / totPop + objective_to_min <- objective_to_min + term1^2 + term2^2 + } + return (objective_to_min) + } + + oldOptimValue <- 10^1000 + + for (rep in (1:10)) { + #apply optimization up to 10 times, using parameters obtained in the previous step, just in case... + parResult <- optim(data=Value, par=param, lower=param_min, upper=param_max, fn=optim_fun, method = "L-BFGS-B") + if (parResult$value >= oldOptimValue) break + param <- parResult$par + oldOptimValue <- parResult$value + } + valueOut <- computeNewPop(Value, parResult$par, Age, ageRangeMin, nAges_mult) + + if (ret_ggPlot) { + library2 <- function(pack) { + #pack<-"tcltk2" + if( !(pack %in% installed.packages())) + {install.packages(pack)} + library(pack,character.only = TRUE) + } + library2("ggplot2") + library2("scales") + + getScales <- function(Value, ageRangeMin, ageRangeMax) { + maxCount <- max(Value) * 1.0 + power <- 0 + while (maxCount > 1) { + maxCount <- maxCount / 10.0 + power <- power + 1 + } + maxCount <- ceiling(maxCount * 10) * 10^(power-1) + leftScale <- seq(0, maxCount, maxCount / 4) + rightScale <- seq(0, 1, 0.25) + xScale <- seq(ageRangeMin-5, ageRangeMax+5, 5) + return (list(left=leftScale, right=rightScale, x=xScale)) + } + + df <- data.frame(Age=Age, val=Value) + df$type <- 'Original' + temp <- data.frame(Age=Age, val=valueOut) + temp$type <- 'Corrected' + df <- rbind(df, temp) + props <- computeNewPop(Value, parResult$par, Age, ageRangeMin, nAges_mult, ret_Prop = TRUE) + temp <- data.frame(Age=Age, val=props) + temp$type <- 'Attraction' + #transform into proportions + temp$val <- temp$val / (1 + temp$val) + scale_axes <- getScales (Value, ageRangeMin, ageRangeMax) + maxY_left <- scale_axes$left[length(scale_axes$left)] + #adjust values in preparation for second axis (we will correct the labels later on in scale_y_continuous) + temp$val <- temp$val * maxY_left + df <- rbind(df, temp) + df$type <- factor (df$type, levels=c("Original", "Corrected", "Attraction")) + myLabelNames <- c("Original", "Corrected", "Attraction factors") + + myPlot <- ggplot(df, aes(x=Age, y=val, color=type, alpha=type, size=type)) + geom_line() + myPlot <- myPlot + scale_y_continuous(breaks = scale_axes$left, name="population counts", + sec.axis = sec_axis(as.formula(paste("~./", maxY_left, sep="")), name="attraction proportions", + breaks=scale_axes$right), + labels = scales::label_number()) + myPlot <- myPlot + scale_x_continuous(breaks = scale_axes$x) + myPlot <- myPlot + scale_colour_manual(values=c("red", "blue", "grey40"), labels=myLabelNames) + myPlot <- myPlot + scale_size_manual(values=c(1, 2, 2), labels=myLabelNames) + myPlot <- myPlot + scale_alpha_manual(values=c(1, 1, 0.2), labels=myLabelNames) + myPlot <- myPlot + theme_bw() + theme(legend.key = element_blank(), legend.title=element_blank(), legend.position="bottom") + myPlot <- myPlot + theme_text(1) + myPlot <- myPlot + labs(color="", alpha="", size="") + if (!is.null(legendPlot)) myPlot <- myPlot + ggtitle(legendPlot) + + return (list(data=valueOut, ggplot=myPlot)) + } else { + return (valueOut) + } +} + +#data for Colombia 1973 +col1973 <- structure(list( + Age = 0:99, + Value = c(493538L, 497954L, 580000L, + 610204L, 609266L, 609670L, 599184L, 628352L, 624302L, 549158L, + 624112L, 527810L, 611266L, 541084L, 490342L, 466650L, 435310L, + 423912L, 444468L, 334732L, 345390L, 301686L, 319468L, 312100L, + 267828L, 293902L, 230690L, 230770L, 244100L, 178590L, 278284L, + 136552L, 201120L, 218514L, 159054L, 217800L, 156786L, 162270L, + 203560L, 141258L, 247758L, 94344L, 166104L, 164336L, 116024L, + 182858L, 104268L, 102916L, 136222L, 90550L, 180598L, 68132L, + 109788L, 108058L, 81438L, 116446L, 77230L, 62844L, 73906L, 49812L, + 136486L, 37506L, 61658L, 69702L, 43570L, 72760L, 36930L, 34102L, + 40052L, 24126L, 68924L, 16004L, 32988L, 40868L, 18406L, 31836L, + 14222L, 11824L, 16206L, 6972L, 24294L, 5016L, 7392L, 6906L, 6244L, + 9424L, 3806L, 3286L, 3478L, 1912L, 5260L, 916L, 1540L, 1142L, + 1266L, 1674L, 602L, 468L, 752L, 2360L)), + class = "data.frame", + row.names = c(NA,-100L)) + +#Alexandropol 1897 +Alex1897 <- structure(list( + Age = 0:99, + Value = c(376.7579251, 342.1757925, + 398.3717579, 337.8530259, 314.0778098, 290.3025937, 355.1440922, + 334, 345, 360, 392, 260.0432277, 370, 307.5936599, 290.3025937, + 247.074928, 281.6570605, 268.6887608, 298.9481268, 164.9423631, + 333.5302594, 1401.253602, 1729.783862, 1643.32853, 1669.26513, + 700.9654179, 324.8847262, 273.0115274, 234.1066282, 104.4236311, + 471.8587896, 108.7463977, 186.556196, 151.9740634, 113.0691643, + 389.7262248, 151.9740634, 126.037464, 156.29683, 48.22766571, + 458.8904899, 48.22766571, 108.7463977, 56.87319885, 65.51873199, + 277.3342939, 82.80979827, 52.55043228, 91.45533141, 48.22766571, + 363.7896254, 26.61383285, 52.55043228, 39.58213256, 39.58213256, + 199.5244957, 56.87319885, 39.58213256, 48.22766571, 5, 285.9798271, + 17.96829971, 26.61383285, 17.96829971, 22.29106628, 78.4870317, + 22.29106628, 22.29106628, 26.61383285, 9.322766571, 121.7146974, + 0.677233429, 9.322766571, 5, 5, 30.93659942, 13.64553314, 9.322766571, + 9.322766571, 13.64553314, 35.25936599, 9.322766571, 9.322766571, + 5, 0.677233429, 17.96829971, 0.677233429, 5, 0.677233429, 9.322766571, + 17.96829971, 5, 5, 5, 5, 0.677233429, 0.677233429, 0.677233429, + 0.677233429, 0.677233429)), + class = "data.frame", + row.names = c(NA, -100L)) + +#call the function for Colombia +resCol <- smooth_age_zigzag_dd(col1973$Value, col1973$Age, OAG = TRUE, ret_ggPlot = TRUE, legendPlot="Colombia 1973") +#show the plot for Colombia +resCol$ggplot +#call the function for Alexandropol +resAlex <- smooth_age_zigzag_dd(Alex1897$Value, Alex1897$Age, OAG = TRUE, ret_ggPlot = TRUE, legendPlot="Alexandropol 1897") +#show the plot +resAlex$ggplot diff --git a/docs/404.html b/docs/404.html index b6e9684e8..3df36364e 100644 --- a/docs/404.html +++ b/docs/404.html @@ -79,7 +79,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -87,7 +87,7 @@
  • - +
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    • - + github diff --git a/docs/CODE_OF_CONDUCT.html b/docs/CODE_OF_CONDUCT.html index 03b764994..d89b91b63 100644 --- a/docs/CODE_OF_CONDUCT.html +++ b/docs/CODE_OF_CONDUCT.html @@ -79,7 +79,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -87,7 +87,7 @@
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            • Your pull request will be easiest for us to read if you use a common style: http://r-pkgs.had.co.nz/r.html#style. Please pay particular attention to whitespace.

            • You should always add a bullet point to NEWS.md motivating the change. It should look like “This is what changed (@yourusername, #issuenumber)”. Please don’t add headings like “bug fix” or “new features” - these are added during the release process.

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            • If you’re adding new parameters or a new function, you’ll also need to document them with roxygen2. Make sure to re-run devtools::document() on the code before submitting.

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            • Also run devtools::check() to make sure your function doesn’t imply downstream errors or warnings. More such checking will be taken care of by us.

              • +

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            diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index e62e83b74..449627831 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -79,7 +79,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -87,7 +87,7 @@
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              • - + github diff --git a/docs/articles/Age-heaping_quality_with_Demotools.html b/docs/articles/Age-heaping_quality_with_Demotools.html index ff1b1beef..b53afde38 100644 --- a/docs/articles/Age-heaping_quality_with_Demotools.html +++ b/docs/articles/Age-heaping_quality_with_Demotools.html @@ -38,7 +38,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -46,7 +46,7 @@
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                    +

                    Age-heaping indices with DemoTools

                    Jose Manuel Aburto, Ilya Kashnitsky, Marius Pascariu, Tim Riffe

                    -

                    2021-02-24

                    +

                    2022-01-17

                    Source: vignettes/Age-heaping_quality_with_Demotools.Rmd @@ -137,9 +137,9 @@

                    plot(Age, pop1m_ind, type = 'o')

                    -

                    The figure above has some clear age irregularities. For example, there is a consistent clustering around ages ending in zero and five. In principle, a post-enumeration survey or a sample interview should give more information on the reasons why these irregularities appear in the data (Siegel Jacob and Swanson David 2004). However, not every country has the resources to conduct a survey or post-enumeration process. Therefore, various arithmetic techniques have been developed for measuring heaping on individual ages, terminal digits, and age ranges. These estimate the degree of bias, but they do not correct it (Booth and Gerland 2015). The simplest way to analyze age-heaping is by assuming that the true numbers are rectangularly distributed over some age range that includes centered the age in question (Siegel Jacob and Swanson David 2004).

                    -

                    Several indices of age-heaping exist. DemoTools implements Whipple (Spoorenberg and Dutreuilh 2007), Myers, Bachi (Bachi 1951), Coale-Li (Coale and Li 1991), Noumbissi (Noumbissi 1992), Spoorenberg (Spoorenberg and Dutreuilh 2007), Jdanov (Jdanov 2008), and Kannisto (Kannisto 1999) age-heaping indices. Although the literal interpretation of these indices may vary, they tend to covary strongly when applied to the same data and age-ranges, so for making quick judgements of the degree of heaping over a large collection of data it may not be necessary to apply more than one or two. We also offer two new measures designed to test for irregularities in data in 5-year age bins.

                    -

                    Often the degree of detected heaping suggests that some smoothing procedure is warranted, but the kind of smoothing procedure may be a function of the particular way in which heaping is manifested. For example, if heaping is light, or just a matter of rounding to the nearest digit divisible by 5, then there will be no major difference between heaping on 0s versus heaping on 5s. In this case, grouping to 5-year age bins (see groupAges()) and then graduating in a constrained way (see sprague()) may suffice to remove the distortion while maintaining the broad underlying age pattern. However, if heaping is much worse on 0s than on 5s, the age pattern may still be distorted in a regular and pernicious way even after binning in 5-year age groups. In that case, it is advised to select a smoothing procedure designed for 5-year age groups (see smooth_age_5() and the vignette on smoothing) before graduating, or else some other more agressive smoothing option (see agesmth1()). The present vignette does not offer specific guidelines for such adjustments, but we do offer two new age quality indices that might be useful for deciding whether to resort to agressive smoothing: zero_pref_sawtooth() checks for and rates the mentioned jagged pattern in 5-year age groups. five_year_roughness() gives a total measure of noise for data in 5-year age groups, but does not look for a particular pattern to it. This second measure should not be used in isolation, but together with visual assessment. More details about such adjustments and decisions can be found in a second vignette on smoothing.

                    +

                    The figure above has some clear age irregularities. For example, there is a consistent clustering around ages ending in zero and five. In principle, a post-enumeration survey or a sample interview should give more information on the reasons why these irregularities appear in the data (Siegel Jacob and Swanson David 2004). However, not every country has the resources to conduct a survey or post-enumeration process. Therefore, various arithmetic techniques have been developed for measuring heaping on individual ages, terminal digits, and age ranges. These estimate the degree of bias, but they do not correct it (Booth and others 2015). The simplest way to analyze age-heaping is by assuming that the true numbers are rectangularly distributed over some age range that includes centered the age in question (Siegel Jacob and Swanson David 2004).

                    +

                    Several indices of age-heaping exist. DemoTools implements Whipple (Spoorenberg and Dutreuilh 2007), Myers, Bachi (Bachi 1951), Coale-Li (Coale and Li 1991), Noumbissi (Noumbissi 1992), Spoorenberg (Spoorenberg and Dutreuilh 2007), Jdanov (Jdanov 2008), and Kannisto (Kannisto 1999) age-heaping indices. Although the literal interpretation of these indices may vary, they tend to covary strongly when applied to the same data and age-ranges, so for making quick judgement of the degree of heaping over a large collection of data it may not be necessary to apply more than one or two. We also offer two new measures designed to test for irregularities in data in 5-year age bins.

                    +

                    Often the degree of detected heaping suggests that some smoothing procedure is warranted, but the kind of smoothing procedure may be a function of the particular way in which heaping is manifested. For example, if heaping is light, or just a matter of rounding to the nearest digit divisible by 5, then there will be no major difference between heaping on 0s versus heaping on 5s. In this case, grouping to 5-year age bins (see groupAges()) and then graduating in a constrained way (see sprague()) may suffice to remove the distortion while maintaining the broad underlying age pattern. However, if heaping is much worse on 0s than on 5s, the age pattern may still be distorted in a regular and pernicious way even after binning in 5-year age groups. In that case, it is advised to select a smoothing procedure designed for 5-year age groups (see smooth_age_5() and the vignette on smoothing) before graduating, or else some other more aggressive smoothing option (see agesmth1()). The present vignette does not offer specific guidelines for such adjustments, but we do offer two new age quality indices that might be useful for deciding whether to resort to aggressive smoothing: zero_pref_sawtooth() checks for and rates the mentioned jagged pattern in 5-year age groups. five_year_roughness() gives a total measure of noise for data in 5-year age groups, but does not look for a particular pattern to it. This second measure should not be used in isolation, but together with visual assessment. More details about such adjustments and decisions can be found in a second vignette on smoothing.

                    Whipple Index

                    @@ -188,7 +188,7 @@

                    Noumbissi

                    This method, based on the underlying principles and assumptions of the original Whipple’s index, improves by extending its basic principle to all ten digits. It compares single terminal digit numerators to denominators consisting in 5-year age groups centered on the terminal digit of age in question (Noumbissi 1992). It relies on a weaker assumption of linearity over an age range of five years rather than ten. It is based once more on the underlying principles and assumptions of the original Whipple’s index and introduces the following formulas to measure age heaping:

                    \[\begin{align} -\label{eq:Noumbussi} +\label{eq:Noumbissi} W_0 =5\frac{P_{30}+P_{40}+P_{50}+P_{60}}{_5P_{28}+\, _5P_{38}+\, _5P_{48}+\, _5P_{58}} \\ W_5 =5\frac{P_{25}+P_{35}+P_{45}+P_{55}}{_5P_{23}+\, _5P_{33}+\, _5P_{43}+\, _5P_{53}} \\ \end{align}\]

                    @@ -262,39 +262,39 @@

                    #> [1] 0.8269583

                    Values greater than 0.1 coupled with a sawtooth value greater than 0 already suggest that some smoothing is warranted. If there is no detected sawtooth pattern, then five-year-roughness indices should probably need to be higher and visually confirmed before deciding to smooth. Further guidelines can be found in the vignette on smoothing.

                    ##References

                    -
                    -
                    -

                    Bachi, Roberto. 1951. “The Tendency to Round Off Age Returns: Measurement and Correction.” Bulletin of the International Statistical Institute 33 (4): 195–222.

                    +
                    +
                    +Bachi, Roberto. 1951. “The Tendency to Round Off Age Returns: Measurement and Correction.” Bulletin of the International Statistical Institute 33 (4): 195–222.
                    -
                    -

                    Booth, Heather, and Patrick Gerland. 2015. “Demographic Techniques: Data Adjustment and Correction.” Elsevier.

                    +
                    +Booth, Heather, and others. 2015. “Demographic Techniques, Data Adjustment and Correction.” In International Encyclopedia of the Social and Behavioral Sciences (2015), edited by James E. Wright, 2nd ed. Vol. 6. Oxford: Elsevier.
                    -
                    -

                    Coale, Ansley J, and Shaomin Li. 1991. “The Effect of Age Misreporting in China on the Calculation of Mortality Rates at Very High Ages.” Demography 28 (2). Springer: 293–301.

                    +
                    +Coale, Ansley J, and Shaomin Li. 1991. “The Effect of Age Misreporting in China on the Calculation of Mortality Rates at Very High Ages.” Demography 28 (2): 293–301.
                    -
                    -

                    Jdanov, Dmitri. 2008. “Beyond the Kannisto-Thatcher Database on Old Age Mortality: An Assessment of Data Quality at Advanced Ages.” MPIDR Working Paper WP-2008-013.

                    +
                    +Jdanov, Dmitri. 2008. “Beyond the Kannisto-Thatcher Database on Old Age Mortality: An Assessment of Data Quality at Advanced Ages.” MPIDR Working Paper WP-2008-013.
                    -
                    -

                    Kannisto, Väinö. 1999. “Assessing the Information on Age at Death of Old Persons in National Vital Statistics.” Validation of Exceptional Longevity”, Odense Monographs on Population Aging 6: 235–49.

                    +
                    +Kannisto, Väinö. 1999. “Assessing the Information on Age at Death of Old Persons in National Vital Statistics.” Validation of Exceptional Longevity”, Odense Monographs on Population Aging 6: 235–49.
                    -
                    -

                    Myers, Robert J. 1954. “Accuracy of Age Reporting in the 1950 United States Census.” Journal of the American Statistical Association 49 (268). Taylor & Francis: 826–31.

                    +
                    +Myers, Robert J. 1954. “Accuracy of Age Reporting in the 1950 United States Census.” Journal of the American Statistical Association 49 (268): 826–31.
                    -
                    -

                    Noumbissi, Amadou. 1992. “L’indice de Whipple Modifié: Une Application Aux Données Du Cameroun, de La Suède et de La Belgique.” Population (French Edition). JSTOR, 1038–41.

                    +
                    +Noumbissi, Amadou. 1992. “L’indice de Whipple Modifié: Une Application Aux Données Du Cameroun, de La Suède Et de La Belgique.” Population (French Edition), 1038–41.
                    -
                    -

                    Siegel Jacob, S, and A Swanson David, eds. 2004. The Methods and Materials of Demography. 2nd ed. San Diego, USA: Elsevier Academic Press, California, USA.

                    +
                    +Siegel Jacob, S, and A Swanson David, eds. 2004. The Methods and Materials of Demography. 2nd ed. San Diego, USA: Elsevier Academic Press, California, USA.
                    -
                    -

                    Spoorenberg, Thomas, and Catriona Dutreuilh. 2007. “Quality of Age Reporting: Extension and Application of the Modified Whipple’s Index.” Population 62 (4). INED: 729–41.

                    +
                    +Spoorenberg, Thomas, and Catriona Dutreuilh. 2007. “Quality of Age Reporting: Extension and Application of the Modified Whipple’s Index.” Population 62 (4): 729–41.
                    -
                    -

                    United Nations. 1952. “Accuracy Tests for Census Age Distributions Tabulated in Five-Year and Ten-Year Groups.” Population Bulletin, no. 2: 59–79.

                    +
                    +United Nations. 1952. “Accuracy Tests for Census Age Distributions Tabulated in Five-Year and Ten-Year Groups.” Population Bulletin, no. 2: 59–79.
                    -
                    -

                    ———. 1955. Manual Ii: Methods of Appraisal of Quality of Basic Data for Population Estimates. 23. New York: United Nations Department of International Economic; Social Affairs.

                    +
                    +———. 1955. Manual II: Methods of Appraisal of Quality of Basic Data for Population Estimates. 23. New York: United Nations Department of International Economic; Social Affairs.
                    diff --git a/docs/articles/Age-heaping_quality_with_Demotools_files/figure-html/unnamed-chunk-1-1.png b/docs/articles/Age-heaping_quality_with_Demotools_files/figure-html/unnamed-chunk-1-1.png index 655240dcc..0d5f68a81 100644 Binary files a/docs/articles/Age-heaping_quality_with_Demotools_files/figure-html/unnamed-chunk-1-1.png and b/docs/articles/Age-heaping_quality_with_Demotools_files/figure-html/unnamed-chunk-1-1.png differ diff --git a/docs/articles/Age-heaping_quality_with_Demotools_files/figure-html/unnamed-chunk-9-1.png b/docs/articles/Age-heaping_quality_with_Demotools_files/figure-html/unnamed-chunk-9-1.png index 5e3a1e9ca..e493a4bf0 100644 Binary files a/docs/articles/Age-heaping_quality_with_Demotools_files/figure-html/unnamed-chunk-9-1.png and b/docs/articles/Age-heaping_quality_with_Demotools_files/figure-html/unnamed-chunk-9-1.png differ diff --git a/docs/articles/Age-heaping_quality_with_Demotools_files/header-attrs-2.10/header-attrs.js b/docs/articles/Age-heaping_quality_with_Demotools_files/header-attrs-2.10/header-attrs.js new file mode 100644 index 000000000..dd57d92e0 --- /dev/null +++ b/docs/articles/Age-heaping_quality_with_Demotools_files/header-attrs-2.10/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/Age-heaping_quality_with_Demotools_files/header-attrs-2.8/header-attrs.js b/docs/articles/Age-heaping_quality_with_Demotools_files/header-attrs-2.8/header-attrs.js new file mode 100644 index 000000000..dd57d92e0 --- /dev/null +++ b/docs/articles/Age-heaping_quality_with_Demotools_files/header-attrs-2.8/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/Age-heaping_quality_with_Demotools_files/header-attrs-2.9/header-attrs.js b/docs/articles/Age-heaping_quality_with_Demotools_files/header-attrs-2.9/header-attrs.js new file mode 100644 index 000000000..dd57d92e0 --- /dev/null +++ b/docs/articles/Age-heaping_quality_with_Demotools_files/header-attrs-2.9/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/case_study_1.html b/docs/articles/case_study_1.html index 1abc68d6f..c25123bd5 100644 --- a/docs/articles/case_study_1.html +++ b/docs/articles/case_study_1.html @@ -38,7 +38,7 @@ DemoTools - 01.13.20 + 01.13.76
                    @@ -46,7 +46,7 @@
                    • - +
                      • @@ -87,13 +87,13 @@
                      • - +
                      • - + github @@ -108,13 +108,13 @@ -
                        +

                        DemoTools: A case study

                        Jose Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Jorge Cimentada, Tim Riffe

                        -

                        2021-02-24

                        +

                        2022-01-17

                        Source: vignettes/case_study_1.Rmd @@ -126,7 +126,7 @@

                        2021-02-24

                        Intro

                        -

                        Here the idea is use API to read in death count and pop count data, evaluate them, adjust them, get to single ages, and make a single age lifetable. Or Abrdiged lifetable. Or both and compare. Show different closeout options too.

                        +

                        Here the idea is use API to read in death count and pop count data, evaluate them, adjust them, get to single ages, and make a single age lifetable. Or Abridged lifetable. Or both and compare. Show different closeout options too.

                        diff --git a/docs/articles/case_study_1_files/header-attrs-2.10/header-attrs.js b/docs/articles/case_study_1_files/header-attrs-2.10/header-attrs.js new file mode 100644 index 000000000..dd57d92e0 --- /dev/null +++ b/docs/articles/case_study_1_files/header-attrs-2.10/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/case_study_1_files/header-attrs-2.8/header-attrs.js b/docs/articles/case_study_1_files/header-attrs-2.8/header-attrs.js new file mode 100644 index 000000000..dd57d92e0 --- /dev/null +++ b/docs/articles/case_study_1_files/header-attrs-2.8/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/case_study_1_files/header-attrs-2.9/header-attrs.js b/docs/articles/case_study_1_files/header-attrs-2.9/header-attrs.js new file mode 100644 index 000000000..dd57d92e0 --- /dev/null +++ b/docs/articles/case_study_1_files/header-attrs-2.9/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/graduation_with_demotools.html b/docs/articles/graduation_with_demotools.html index c176fe21b..6a257d280 100644 --- a/docs/articles/graduation_with_demotools.html +++ b/docs/articles/graduation_with_demotools.html @@ -38,7 +38,7 @@ DemoTools - 01.13.20 + 01.13.76
                        @@ -46,7 +46,7 @@
                        • - +
                          • @@ -87,13 +87,13 @@
                          • - +
                          • - + github @@ -108,13 +108,13 @@ -
                            +

                            Graduation with DemoTools

                            José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Jorge Cimentada, Tim Riffe

                            -

                            2021-02-24

                            +

                            2022-01-17

                            Source: vignettes/graduation_with_demotools.Rmd @@ -129,7 +129,7 @@

                            What is graduation?

                            -

                            Graduation is a practice used to derive figures for n-year age groups, for example 5-year age groups from census data, that are corrected for net reporting error (Siegel Jacob and Swanson David 2004). The basic idea is to fit different curves to the original n-year and redistribute them into single-year values. These techniques are designed so that the sum of the interpolated single-year values is consistent with the total for the groups as a whole. Among the major graduation methods are the Sprague (Sprague 1880) and Beers (H. Beers 1945) oscilatory methods, monotone spline and the uniform distribution. More recently, the penalized composite link model (pclm) was proposed to ungroup coarsely aggregated data (Rizzi, Gampe, and Eilers 2015). All of these methodologies are implemented in DemoTools.

                            +

                            Graduation is a practice used to derive figures for n-year age groups, for example 5-year age groups from census data, that are corrected for net reporting error (Siegel Jacob and Swanson David 2004). The basic idea is to fit different curves to the original n-year and redistribute them into single-year values. These techniques are designed so that the sum of the interpolated single-year values is consistent with the total for the groups as a whole. Among the major graduation methods are the Sprague (Sprague 1880) and Beers (H. Beers 1945) oscillatory methods, monotone spline and the uniform distribution. More recently, the penalized composite link model (pclm) was proposed to ungroup coarsely aggregated data (Rizzi, Gampe, and Eilers 2015). All of these methodologies are implemented in DemoTools.

                            @@ -186,14 +186,14 @@

                            #check the totals sum(sprague) -#> [1] 395139.8 +#> [1] 395141.3 sum(Value) #> [1] 395138

                            Beers

                            -

                            Following a similar idea, Beers interpolated two overlapping curves minimizing the squares of the differences within the interpolation range (H. Beers 1945). Specifically, Beers did this by minimizing fifth differences for a six-term formula, refered to as the ‘Ordinary’ Beers method (H. S. Beers 1945). Subsequently, the ordinay formula was modified to relax the requirement that the given value be reproduced and yield smoother interpolated results, refered to as ‘Modified’ Beers method (H. Beers 1945).

                            +

                            Following a similar idea, Beers interpolated two overlapping curves minimizing the squares of the differences within the interpolation range (H. Beers 1945). Specifically, Beers did this by minimizing fifth differences for a six-term formula, refered to as the ‘Ordinary’ Beers method (H. S. Beers 1945). Subsequently, the ordinary formula was modified to relax the requirement that the given value be reproduced and yield smoother interpolated results, referred to as ‘Modified’ Beers method (H. Beers 1945).

                             beers.ordinary <- graduate(Value, Age, method = "beers(ord)")
 beers.modified <- graduate(Value, Age, method = "beers(mod)")
@@ -219,7 +219,7 @@ 
                            
 
                            
 
                            
 Monotone spline
                            
-
                            Graduation by a monotone spline is another option available in DemoTools. This option offers an algorithm to produce interpolants that preserve properties such as monotonicity or convexity that are present in the data (Fritsch and Carlson 1980). These are desirable conditions to not introduce biased details that cannot be ascertained from the data (Hyman 1983). To run this algorithm in DemoTools the option mono should be selected as follows:
                            
+
                            Graduation by a monotone spline is another option available in DemoTools. This option offers an algorithm to produce interpolations that preserve properties such as monotonicity or convexity that are present in the data (Fritsch and Carlson 1980). These are desirable conditions to not introduce biased details that cannot be ascertained from the data (Hyman 1983). To run this algorithm in DemoTools the option mono should be selected as follows:
                            
 
                             mono.grad <- graduate(Value, Age, method = "mono")
 
@@ -338,12 +338,10 @@ 
                            
                     splitfun = graduate_uniform, 
                  recursive = TRUE)
 pop1resc
-#>          0          1          6          8         10         14         18 
-#>  77.564622 307.324828 177.930968 127.943315 271.952999 259.819449   4.988328 
-#>         19         21         24         28         31         34         37 
-#>  99.177502 134.093179 257.662726 112.092588 173.855370 168.577732 119.897178 
-#>         40 
-#> 249.142993
                            
+#>         0         1         6         8        10        14        18        19 
+#>  14.01029 417.19723 111.24706 163.11039 214.19437 380.31624  68.27963 169.87352 
+#>        21        24        28        31        34        37        40 
+#>  83.10931 362.36966 238.36890  61.02290  57.59022 163.75313 332.03078
                            @@ -354,33 +352,33 @@

                            References

                            -
                            -
                            -

                            Beers, Henry S. 1945. “Six-Term Formulas for Routine Actuarial Interpolation.” The Record of the American Institute of Actuaries 34 (69): 59–60.

                            +
                            +
                            +Beers, Henry S. 1945. “Six-Term Formulas for Routine Actuarial Interpolation.” The Record of the American Institute of Actuaries 34 (69): 59–60.
                            -
                            -

                            Beers, HS. 1945. “Modified-Interpolation Formulas That Minimize Fourth Differences.” Record of the American Institute of Actuaries 34 (69): 14–20.

                            +
                            +Beers, HS. 1945. “Modified-Interpolation Formulas That Minimize Fourth Differences.” Record of the American Institute of Actuaries 34 (69): 14–20.
                            -
                            -

                            Brass, William. 1960. “The Graduation of Fertility Distributions by Polynomial Functions.” Population Studies 14 (2). Taylor & Francis: 148–62.

                            +
                            +Brass, William. 1960. “The Graduation of Fertility Distributions by Polynomial Functions.” Population Studies 14 (2): 148–62.
                            -
                            -

                            Fritsch, Frederick N, and Ralph E Carlson. 1980. “Monotone Piecewise Cubic Interpolation.” SIAM Journal on Numerical Analysis 17 (2). SIAM: 238–46.

                            +
                            +Fritsch, Frederick N, and Ralph E Carlson. 1980. “Monotone Piecewise Cubic Interpolation.” SIAM Journal on Numerical Analysis 17 (2): 238–46.
                            -
                            -

                            Hyman, James M. 1983. “Accurate Monotonicity Preserving Cubic Interpolation.” SIAM Journal on Scientific and Statistical Computing 4 (4). SIAM: 645–54.

                            +
                            +Hyman, James M. 1983. “Accurate Monotonicity Preserving Cubic Interpolation.” SIAM Journal on Scientific and Statistical Computing 4 (4): 645–54.
                            -
                            -

                            Pascariu, Marius D, Maciej J Dańko, Jonas Schöley, and Silvia Rizzi. 2018. “Ungroup: An R Package for Efficient Estimation of Smooth Distributions from Coarsely Binned Data.” Age 10: 0.

                            +
                            +Pascariu, Marius D, Maciej J Dańko, Jonas Schöley, and Silvia Rizzi. 2018. “Ungroup: An r Package for Efficient Estimation of Smooth Distributions from Coarsely Binned Data.” Age 10: 0.
                            -
                            -

                            Rizzi, Silvia, Jutta Gampe, and Paul HC Eilers. 2015. “Efficient Estimation of Smooth Distributions from Coarsely Grouped Data.” American Journal of Epidemiology 182 (2). Oxford University Press: 138–47.

                            +
                            +Rizzi, Silvia, Jutta Gampe, and Paul HC Eilers. 2015. “Efficient Estimation of Smooth Distributions from Coarsely Grouped Data.” American Journal of Epidemiology 182 (2): 138–47.
                            -
                            -

                            Siegel Jacob, S, and A Swanson David, eds. 2004. The Methods and Materials of Demography. 2nd ed. San Diego, USA: Elsevier Academic Press, California, USA.

                            +
                            +Siegel Jacob, S, and A Swanson David, eds. 2004. The Methods and Materials of Demography. 2nd ed. San Diego, USA: Elsevier Academic Press, California, USA.
                            -
                            -

                            Sprague, Thomas Bond. 1880. “Explanation of a New Formula for Interpolation.” Journal of the Institute of Actuaries 22 (4). Cambridge University Press: 270–85.

                            +
                            +Sprague, Thomas Bond. 1880. “Explanation of a New Formula for Interpolation.” Journal of the Institute of Actuaries 22 (4): 270–85.
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We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/graduation_with_demotools_files/header-attrs-2.8/header-attrs.js b/docs/articles/graduation_with_demotools_files/header-attrs-2.8/header-attrs.js new file mode 100644 index 000000000..dd57d92e0 --- /dev/null +++ b/docs/articles/graduation_with_demotools_files/header-attrs-2.8/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/graduation_with_demotools_files/header-attrs-2.9/header-attrs.js b/docs/articles/graduation_with_demotools_files/header-attrs-2.9/header-attrs.js new file mode 100644 index 000000000..dd57d92e0 --- /dev/null +++ b/docs/articles/graduation_with_demotools_files/header-attrs-2.9/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/index.html b/docs/articles/index.html index 6ae677515..b3a456073 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -79,7 +79,7 @@ DemoTools - 01.13.20 + 01.13.76
                            @@ -87,7 +87,7 @@
                            • - +
                              • @@ -128,13 +128,13 @@
                              • - +
                              • - + github diff --git a/docs/articles/lifetable_with_demotools_files/crosstalk-1.0.0/js/crosstalk.js.map b/docs/articles/lifetable_with_demotools_files/crosstalk-1.0.0/js/crosstalk.js.map deleted file mode 100644 index 508b24fa0..000000000 --- a/docs/articles/lifetable_with_demotools_files/crosstalk-1.0.0/js/crosstalk.js.map +++ /dev/null @@ -1,37 +0,0 @@ -{ - "version": 3, - "sources": [ - "node_modules/browser-pack/_prelude.js", - "javascript/src/events.js", - "javascript/src/filter.js", - "javascript/src/filterset.js", - "javascript/src/group.js", - "javascript/src/index.js", - "javascript/src/input.js", - "javascript/src/input_checkboxgroup.js", - "javascript/src/input_selectize.js", - "javascript/src/input_slider.js", - "javascript/src/selection.js", - "javascript/src/util.js", - "javascript/src/var.js" - ], - "names": [], - "mappings": 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- "file": "generated.js", - "sourceRoot": "", - "sourcesContent": [ - "(function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require==\"function\"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error(\"Cannot find module '\"+o+\"'\");throw f.code=\"MODULE_NOT_FOUND\",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require==\"function\"&&require;for(var o=0;o {\n this._eventRelay.trigger(\"change\", e, this);\n });\n this._varOnChangeSub = sub;\n }\n }\n\n /**\n * Combine the given `extraInfo` (if any) with the handle's default\n * `_extraInfo` (if any).\n * @private\n */\n _mergeExtraInfo(extraInfo) {\n return util.extend({},\n this._extraInfo ? this._extraInfo : null,\n extraInfo ? extraInfo : null);\n }\n\n /**\n * Close the handle. This clears this handle's contribution to the filter set,\n * and unsubscribes all event listeners.\n */\n close() {\n this._emitter.removeAllListeners();\n this.clear();\n this.setGroup(null);\n }\n\n /**\n * Clear this handle's contribution to the filter set.\n *\n * @param {Object} [extraInfo] - Extra properties to be included on the event\n * object that's passed to listeners (in addition to any options that were\n * passed into the `FilterHandle` constructor).\n */\n clear(extraInfo) {\n if (!this._filterSet)\n return;\n this._filterSet.clear(this._id);\n this._onChange(extraInfo);\n }\n\n /**\n * Set this handle's contribution to the filter set. This array should consist\n * of the keys of the rows that _should_ be displayed; any keys that are not\n * present in the array will be considered _filtered out_. Note that multiple\n * `FilterHandle` instances in the group may each contribute an array of keys,\n * and only those keys that appear in _all_ of the arrays make it through the\n * filter.\n *\n * @param {string[]} keys - Empty array, or array of keys. To clear the\n * filter, don't pass an empty array; instead, use the\n * {@link FilterHandle#clear} method.\n * @param {Object} [extraInfo] - Extra properties to be included on the event\n * object that's passed to listeners (in addition to any options that were\n * passed into the `FilterHandle` constructor).\n */\n set(keys, extraInfo) {\n if (!this._filterSet)\n return;\n this._filterSet.update(this._id, keys);\n this._onChange(extraInfo);\n }\n\n /**\n * @return {string[]|null} - Either: 1) an array of keys that made it through\n * all of the `FilterHandle` instances, or, 2) `null`, which means no filter\n * is being applied (all data should be displayed).\n */\n get filteredKeys() {\n return this._filterSet ? this._filterSet.value : null;\n }\n\n /**\n * Subscribe to events on this `FilterHandle`.\n *\n * @param {string} eventType - Indicates the type of events to listen to.\n * Currently, only `\"change\"` is supported.\n * @param {FilterHandle~listener} listener - The callback function that\n * will be invoked when the event occurs.\n * @return {string} - A token to pass to {@link FilterHandle#off} to cancel\n * this subscription.\n */\n on(eventType, listener) {\n return this._emitter.on(eventType, listener);\n }\n\n /**\n * Cancel event subscriptions created by {@link FilterHandle#on}.\n *\n * @param {string} eventType - The type of event to unsubscribe.\n * @param {string|FilterHandle~listener} listener - Either the callback\n * function previously passed into {@link FilterHandle#on}, or the\n * string that was returned from {@link FilterHandle#on}.\n */\n off(eventType, listener) {\n return this._emitter.off(eventType, listener);\n }\n\n _onChange(extraInfo) {\n if (!this._filterSet)\n return;\n this._filterVar.set(this._filterSet.value, this._mergeExtraInfo(extraInfo));\n }\n\n /**\n * @callback FilterHandle~listener\n * @param {Object} event - An object containing details of the event. For\n * `\"change\"` events, this includes the properties `value` (the new\n * value of the filter set, or `null` if no filter set is active),\n * `oldValue` (the previous value of the filter set), and `sender` (the\n * `FilterHandle` instance that made the change).\n */\n\n /**\n * @event FilterHandle#change\n * @type {object}\n * @property {object} value - The new value of the filter set, or `null`\n * if no filter set is active.\n * @property {object} oldValue - The previous value of the filter set.\n * @property {FilterHandle} sender - The `FilterHandle` instance that\n * changed the value.\n */\n}\n", - "import { diffSortedLists } from \"./util\";\n\nfunction naturalComparator(a, b) {\n if (a === b) {\n return 0;\n } else if (a < b) {\n return -1;\n } else if (a > b) {\n return 1;\n }\n}\n\n/**\n * @private\n */\nexport default class FilterSet {\n constructor() {\n this.reset();\n }\n\n reset() {\n // Key: handle ID, Value: array of selected keys, or null\n this._handles = {};\n // Key: key string, Value: count of handles that include it\n this._keys = {};\n this._value = null;\n this._activeHandles = 0;\n }\n\n get value() {\n return this._value;\n }\n\n update(handleId, keys) {\n if (keys !== null) {\n keys = keys.slice(0); // clone before sorting\n keys.sort(naturalComparator);\n }\n\n let {added, removed} = diffSortedLists(this._handles[handleId], keys);\n this._handles[handleId] = keys;\n\n for (let i = 0; i < added.length; i++) {\n this._keys[added[i]] = (this._keys[added[i]] || 0) + 1;\n }\n for (let i = 0; i < removed.length; i++) {\n this._keys[removed[i]]--;\n }\n\n this._updateValue(keys);\n }\n\n /**\n * @param {string[]} keys Sorted array of strings that indicate\n * a superset of possible keys.\n * @private\n */\n _updateValue(keys = this._allKeys) {\n let handleCount = Object.keys(this._handles).length;\n if (handleCount === 0) {\n this._value = null;\n } else {\n this._value = [];\n for (let i = 0; i < keys.length; i++) {\n let count = this._keys[keys[i]];\n if (count === handleCount) {\n this._value.push(keys[i]);\n }\n }\n }\n }\n\n clear(handleId) {\n if (typeof(this._handles[handleId]) === \"undefined\") {\n return;\n }\n\n let keys = this._handles[handleId];\n if (!keys) {\n keys = [];\n }\n\n for (let i = 0; i < keys.length; i++) {\n this._keys[keys[i]]--;\n }\n delete this._handles[handleId];\n\n this._updateValue();\n }\n\n get _allKeys() {\n let allKeys = Object.keys(this._keys);\n allKeys.sort(naturalComparator);\n return allKeys;\n }\n}\n", - "import Var from \"./var\";\n\n// Use a global so that multiple copies of crosstalk.js can be loaded and still\n// have groups behave as singletons across all copies.\nglobal.__crosstalk_groups = global.__crosstalk_groups || {};\nlet groups = global.__crosstalk_groups;\n\nexport default function group(groupName) {\n if (groupName && typeof(groupName) === \"string\") {\n if (!groups.hasOwnProperty(groupName)) {\n groups[groupName] = new Group(groupName);\n }\n return groups[groupName];\n } else if (typeof(groupName) === \"object\" && groupName._vars && groupName.var) {\n // Appears to already be a group object\n return groupName;\n } else if (Array.isArray(groupName) &&\n groupName.length == 1 &&\n typeof(groupName[0]) === \"string\") {\n return group(groupName[0]);\n } else {\n throw new Error(\"Invalid groupName argument\");\n }\n}\n\nclass Group {\n constructor(name) {\n this.name = name;\n this._vars = {};\n }\n\n var(name) {\n if (!name || typeof(name) !== \"string\") {\n throw new Error(\"Invalid var name\");\n }\n\n if (!this._vars.hasOwnProperty(name))\n this._vars[name] = new Var(this, name);\n return this._vars[name];\n }\n\n has(name) {\n if (!name || typeof(name) !== \"string\") {\n throw new Error(\"Invalid var name\");\n }\n\n return this._vars.hasOwnProperty(name);\n }\n}\n", - "import group from \"./group\";\nimport { SelectionHandle } from \"./selection\";\nimport { FilterHandle } from \"./filter\";\nimport \"./input\";\nimport \"./input_selectize\";\nimport \"./input_checkboxgroup\";\nimport \"./input_slider\";\n\nconst defaultGroup = group(\"default\");\n\nfunction var_(name) {\n return defaultGroup.var(name);\n}\n\nfunction has(name) {\n return defaultGroup.has(name);\n}\n\nif (global.Shiny) {\n global.Shiny.addCustomMessageHandler(\"update-client-value\", function(message) {\n if (typeof(message.group) === \"string\") {\n group(message.group).var(message.name).set(message.value);\n } else {\n var_(message.name).set(message.value);\n }\n });\n}\n\nconst crosstalk = {\n group: group,\n var: var_,\n has: has,\n SelectionHandle: SelectionHandle,\n FilterHandle: FilterHandle\n};\n\n/**\n * @namespace crosstalk\n */\nexport default crosstalk;\nglobal.crosstalk = crosstalk;\n", - "let $ = global.jQuery;\n\nlet bindings = {};\n\nexport function register(reg) {\n bindings[reg.className] = reg;\n if (global.document && global.document.readyState !== \"complete\") {\n $(() => {\n bind();\n });\n } else if (global.document) {\n setTimeout(bind, 100);\n }\n}\n\nfunction bind() {\n Object.keys(bindings).forEach(function(className) {\n let binding = bindings[className];\n $(\".\" + binding.className).not(\".crosstalk-input-bound\").each(function(i, el) {\n bindInstance(binding, el);\n });\n });\n}\n\n// Escape jQuery identifier\nfunction $escape(val) {\n return val.replace(/([!\"#$%&'()*+,.\\/:;<=>?@\\[\\\\\\]^`{|}~])/g, \"\\\\$1\");\n}\n\nfunction bindEl(el) {\n let $el = $(el);\n Object.keys(bindings).forEach(function(className) {\n if ($el.hasClass(className) && !$el.hasClass(\"crosstalk-input-bound\")) {\n let binding = bindings[className];\n bindInstance(binding, el);\n }\n });\n}\n\nfunction bindInstance(binding, el) {\n let jsonEl = $(el).find(\"script[type='application/json'][data-for='\" + $escape(el.id) + \"']\");\n let data = JSON.parse(jsonEl[0].innerText);\n\n let instance = binding.factory(el, data);\n $(el).data(\"crosstalk-instance\", instance);\n $(el).addClass(\"crosstalk-input-bound\");\n}\n\nif (global.Shiny) {\n let inputBinding = new global.Shiny.InputBinding();\n let $ = global.jQuery;\n $.extend(inputBinding, {\n find: function(scope) {\n return $(scope).find(\".crosstalk-input\");\n },\n initialize: function(el) {\n if (!$(el).hasClass(\"crosstalk-input-bound\")) {\n bindEl(el);\n }\n },\n getId: function(el) {\n return el.id;\n },\n getValue: function(el) {\n\n },\n setValue: function(el, value) {\n\n },\n receiveMessage: function(el, data) {\n\n },\n subscribe: function(el, callback) {\n $(el).data(\"crosstalk-instance\").resume();\n },\n unsubscribe: function(el) {\n $(el).data(\"crosstalk-instance\").suspend();\n }\n });\n global.Shiny.inputBindings.register(inputBinding, \"crosstalk.inputBinding\");\n}\n", - "import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n className: \"crosstalk-input-checkboxgroup\",\n\n factory: function(el, data) {\n /*\n * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n * group: \"ct-groupname\"\n */\n let ctHandle = new FilterHandle(data.group);\n\n let lastKnownKeys;\n let $el = $(el);\n $el.on(\"change\", \"input[type='checkbox']\", function() {\n let checked = $el.find(\"input[type='checkbox']:checked\");\n if (checked.length === 0) {\n lastKnownKeys = null;\n ctHandle.clear();\n } else {\n let keys = {};\n checked.each(function() {\n data.map[this.value].forEach(function(key) {\n keys[key] = true;\n });\n });\n let keyArray = Object.keys(keys);\n keyArray.sort();\n lastKnownKeys = keyArray;\n ctHandle.set(keyArray);\n }\n });\n\n return {\n suspend: function() {\n ctHandle.clear();\n },\n resume: function() {\n if (lastKnownKeys)\n ctHandle.set(lastKnownKeys);\n }\n };\n }\n});\n", - "import * as input from \"./input\";\nimport * as util from \"./util\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n className: \"crosstalk-input-select\",\n\n factory: function(el, data) {\n /*\n * items: {value: [...], label: [...]}\n * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n * group: \"ct-groupname\"\n */\n\n let first = [{value: \"\", label: \"(All)\"}];\n let items = util.dataframeToD3(data.items);\n let opts = {\n options: first.concat(items),\n valueField: \"value\",\n labelField: \"label\",\n searchField: \"label\"\n };\n\n let select = $(el).find(\"select\")[0];\n\n let selectize = $(select).selectize(opts)[0].selectize;\n\n let ctHandle = new FilterHandle(data.group);\n\n let lastKnownKeys;\n selectize.on(\"change\", function() {\n if (selectize.items.length === 0) {\n lastKnownKeys = null;\n ctHandle.clear();\n } else {\n let keys = {};\n selectize.items.forEach(function(group) {\n data.map[group].forEach(function(key) {\n keys[key] = true;\n });\n });\n let keyArray = Object.keys(keys);\n keyArray.sort();\n lastKnownKeys = keyArray;\n ctHandle.set(keyArray);\n }\n });\n\n return {\n suspend: function() {\n ctHandle.clear();\n },\n resume: function() {\n if (lastKnownKeys)\n ctHandle.set(lastKnownKeys);\n }\n };\n }\n});\n", - "import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\nlet strftime = global.strftime;\n\ninput.register({\n className: \"crosstalk-input-slider\",\n\n factory: function(el, data) {\n /*\n * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n * group: \"ct-groupname\"\n */\n let ctHandle = new FilterHandle(data.group);\n\n let opts = {};\n let $el = $(el).find(\"input\");\n let dataType = $el.data(\"data-type\");\n let timeFormat = $el.data(\"time-format\");\n let timeFormatter;\n\n // Set up formatting functions\n if (dataType === \"date\") {\n timeFormatter = strftime.utc();\n opts.prettify = function(num) {\n return timeFormatter(timeFormat, new Date(num));\n };\n\n } else if (dataType === \"datetime\") {\n let timezone = $el.data(\"timezone\");\n if (timezone)\n timeFormatter = strftime.timezone(timezone);\n else\n timeFormatter = strftime;\n\n opts.prettify = function(num) {\n return timeFormatter(timeFormat, new Date(num));\n };\n }\n\n $el.ionRangeSlider(opts);\n\n function getValue() {\n let result = $el.data(\"ionRangeSlider\").result;\n\n // Function for converting numeric value from slider to appropriate type.\n let convert;\n let dataType = $el.data(\"data-type\");\n if (dataType === \"date\") {\n convert = function(val) {\n return formatDateUTC(new Date(+val));\n };\n } else if (dataType === \"datetime\") {\n convert = function(val) {\n // Convert ms to s\n return +val / 1000;\n };\n } else {\n convert = function(val) { return +val; };\n }\n\n if ($el.data(\"ionRangeSlider\").options.type === \"double\") {\n return [convert(result.from), convert(result.to)];\n } else {\n return convert(result.from);\n }\n }\n\n let lastKnownKeys = null;\n\n $el.on(\"change.crosstalkSliderInput\", function(event) {\n if (!$el.data(\"updating\") && !$el.data(\"animating\")) {\n let [from, to] = getValue();\n let keys = [];\n for (let i = 0; i < data.values.length; i++) {\n let val = data.values[i];\n if (val >= from && val <= to) {\n keys.push(data.keys[i]);\n }\n }\n keys.sort();\n ctHandle.set(keys);\n lastKnownKeys = keys;\n }\n });\n\n\n // let $el = $(el);\n // $el.on(\"change\", \"input[type=\"checkbox\"]\", function() {\n // let checked = $el.find(\"input[type=\"checkbox\"]:checked\");\n // if (checked.length === 0) {\n // ctHandle.clear();\n // } else {\n // let keys = {};\n // checked.each(function() {\n // data.map[this.value].forEach(function(key) {\n // keys[key] = true;\n // });\n // });\n // let keyArray = Object.keys(keys);\n // keyArray.sort();\n // ctHandle.set(keyArray);\n // }\n // });\n\n return {\n suspend: function() {\n ctHandle.clear();\n },\n resume: function() {\n if (lastKnownKeys)\n ctHandle.set(lastKnownKeys);\n }\n };\n }\n});\n\n\n// Convert a number to a string with leading zeros\nfunction padZeros(n, digits) {\n let str = n.toString();\n while (str.length < digits)\n str = \"0\" + str;\n return str;\n}\n\n// Given a Date object, return a string in yyyy-mm-dd format, using the\n// UTC date. This may be a day off from the date in the local time zone.\nfunction formatDateUTC(date) {\n if (date instanceof Date) {\n return date.getUTCFullYear() + \"-\" +\n padZeros(date.getUTCMonth()+1, 2) + \"-\" +\n padZeros(date.getUTCDate(), 2);\n\n } else {\n return null;\n }\n}\n", - "import Events from \"./events\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\nexport class SelectionHandle {\n\n /**\n * @classdesc\n * Use this class to read and write (and listen for changes to) the selection\n * for a Crosstalk group. This is intended to be used for linked brushing.\n *\n * If two (or more) `SelectionHandle` instances in the same webpage share the\n * same group name, they will share the same state. Setting the selection using\n * one `SelectionHandle` instance will result in the `value` property instantly\n * changing across the others, and `\"change\"` event listeners on all instances\n * (including the one that initiated the sending) will fire.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n * null or undefined (or any other falsy value). This can be changed later\n * via the [SelectionHandle#setGroup](#setGroup) method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n * the event object whenever an event is emitted.\n */\n constructor(group = null, extraInfo = null) {\n this._eventRelay = new Events();\n this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n // Name of the group we're currently tracking, if any. Can change over time.\n this._group = null;\n // The Var we're currently tracking, if any. Can change over time.\n this._var = null;\n // The event handler subscription we currently have on var.on(\"change\").\n this._varOnChangeSub = null;\n\n this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n this.setGroup(group);\n }\n\n /**\n * Changes the Crosstalk group membership of this SelectionHandle. The group\n * being switched away from (if any) will not have its selection value\n * modified as a result of calling `setGroup`, even if this handle was the\n * most recent handle to set the selection of the group.\n *\n * The group being switched to (if any) will also not have its selection value\n * modified as a result of calling `setGroup`. If you want to set the\n * selection value of the new group, call `set` explicitly.\n *\n * @param {string} group - The name of the Crosstalk group, or null (or\n * undefined) to clear the group.\n */\n setGroup(group) {\n // If group is unchanged, do nothing\n if (this._group === group)\n return;\n // Treat null, undefined, and other falsy values the same\n if (!this._group && !group)\n return;\n\n if (this._var) {\n this._var.off(\"change\", this._varOnChangeSub);\n this._var = null;\n this._varOnChangeSub = null;\n }\n\n this._group = group;\n\n if (group) {\n this._var = grp(group).var(\"selection\");\n let sub = this._var.on(\"change\", (e) => {\n this._eventRelay.trigger(\"change\", e, this);\n });\n this._varOnChangeSub = sub;\n }\n }\n\n /**\n * Retrieves the current selection for the group represented by this\n * `SelectionHandle`.\n *\n * - If no selection is active, then this value will be falsy.\n * - If a selection is active, but no data points are selected, then this\n * value will be an empty array.\n * - If a selection is active, and data points are selected, then the keys\n * of the selected data points will be present in the array.\n */\n get value() {\n return this._var ? this._var.get() : null;\n }\n\n /**\n * Combines the given `extraInfo` (if any) with the handle's default\n * `_extraInfo` (if any).\n * @private\n */\n _mergeExtraInfo(extraInfo) {\n // Important incidental effect: shallow clone is returned\n return util.extend({},\n this._extraInfo ? this._extraInfo : null,\n extraInfo ? extraInfo : null);\n }\n\n /**\n * Overwrites the current selection for the group, and raises the `\"change\"`\n * event among all of the group's '`SelectionHandle` instances (including\n * this one).\n *\n * @fires SelectionHandle#change\n * @param {string[]} selectedKeys - Falsy, empty array, or array of keys (see\n * {@link SelectionHandle#value}).\n * @param {Object} [extraInfo] - Extra properties to be included on the event\n * object that's passed to listeners (in addition to any options that were\n * passed into the `SelectionHandle` constructor).\n */\n set(selectedKeys, extraInfo) {\n if (this._var)\n this._var.set(selectedKeys, this._mergeExtraInfo(extraInfo));\n }\n\n /**\n * Overwrites the current selection for the group, and raises the `\"change\"`\n * event among all of the group's '`SelectionHandle` instances (including\n * this one).\n *\n * @fires SelectionHandle#change\n * @param {Object} [extraInfo] - Extra properties to be included on the event\n * object that's passed to listeners (in addition to any that were passed\n * into the `SelectionHandle` constructor).\n */\n clear(extraInfo) {\n if (this._var)\n this.set(void 0, this._mergeExtraInfo(extraInfo));\n }\n\n /**\n * Subscribes to events on this `SelectionHandle`.\n *\n * @param {string} eventType - Indicates the type of events to listen to.\n * Currently, only `\"change\"` is supported.\n * @param {SelectionHandle~listener} listener - The callback function that\n * will be invoked when the event occurs.\n * @return {string} - A token to pass to {@link SelectionHandle#off} to cancel\n * this subscription.\n */\n on(eventType, listener) {\n return this._emitter.on(eventType, listener);\n }\n\n /**\n * Cancels event subscriptions created by {@link SelectionHandle#on}.\n *\n * @param {string} eventType - The type of event to unsubscribe.\n * @param {string|SelectionHandle~listener} listener - Either the callback\n * function previously passed into {@link SelectionHandle#on}, or the\n * string that was returned from {@link SelectionHandle#on}.\n */\n off(eventType, listener) {\n return this._emitter.off(eventType, listener);\n }\n\n /**\n * Shuts down the `SelectionHandle` object.\n *\n * Removes all event listeners that were added through this handle.\n */\n close() {\n this._emitter.removeAllListeners();\n this.setGroup(null);\n }\n\n /**\n * @callback SelectionHandle~listener\n * @param {Object} event - An object containing details of the event. For\n * `\"change\"` events, this includes the properties `value` (the new\n * value of the selection, or `undefined` if no selection is active),\n * `oldValue` (the previous value of the selection), and `sender` (the\n * `SelectionHandle` instance that made the change).\n */\n\n /**\n * @event SelectionHandle#change\n * @type {object}\n * @property {object} value - The new value of the selection, or `undefined`\n * if no selection is active.\n * @property {object} oldValue - The previous value of the selection.\n * @property {SelectionHandle} sender - The `SelectionHandle` instance that\n * changed the value.\n */\n}\n", - "export function extend(target, ...sources) {\n for (let i = 0; i < sources.length; i++) {\n let src = sources[i];\n if (typeof(src) === \"undefined\" || src === null)\n continue;\n\n for (let key in src) {\n if (src.hasOwnProperty(key)) {\n target[key] = src[key];\n }\n }\n }\n return target;\n}\n\nexport function checkSorted(list) {\n for (let i = 1; i < list.length; i++) {\n if (list[i] <= list[i-1]) {\n throw new Error(\"List is not sorted or contains duplicate\");\n }\n }\n}\n\nexport function diffSortedLists(a, b) {\n let i_a = 0;\n let i_b = 0;\n\n if (!a) a = [];\n if (!b) b = [];\n\n let a_only = [];\n let b_only = [];\n\n checkSorted(a);\n checkSorted(b);\n\n while (i_a < a.length && i_b < b.length) {\n if (a[i_a] === b[i_b]) {\n i_a++;\n i_b++;\n } else if (a[i_a] < b[i_b]) {\n a_only.push(a[i_a++]);\n } else {\n b_only.push(b[i_b++]);\n }\n }\n\n if (i_a < a.length)\n a_only = a_only.concat(a.slice(i_a));\n if (i_b < b.length)\n b_only = b_only.concat(b.slice(i_b));\n return {\n removed: a_only,\n added: b_only\n };\n}\n\n// Convert from wide: { colA: [1,2,3], colB: [4,5,6], ... }\n// to long: [ {colA: 1, colB: 4}, {colA: 2, colB: 5}, ... ]\nexport function dataframeToD3(df) {\n let names = [];\n let length;\n for (let name in df) {\n if (df.hasOwnProperty(name))\n names.push(name);\n if (typeof(df[name]) !== \"object\" || typeof(df[name].length) === \"undefined\") {\n throw new Error(\"All fields must be arrays\");\n } else if (typeof(length) !== \"undefined\" && length !== df[name].length) {\n throw new Error(\"All fields must be arrays of the same length\");\n }\n length = df[name].length;\n }\n let results = [];\n let item;\n for (let row = 0; row < length; row++) {\n item = {};\n for (let col = 0; col < names.length; col++) {\n item[names[col]] = df[names[col]][row];\n }\n results.push(item);\n }\n return results;\n}\n\n/**\n * Keeps track of all event listener additions/removals and lets all active\n * listeners be removed with a single operation.\n *\n * @private\n */\nexport class SubscriptionTracker {\n constructor(emitter) {\n this._emitter = emitter;\n this._subs = {};\n }\n\n on(eventType, listener) {\n let sub = this._emitter.on(eventType, listener);\n this._subs[sub] = eventType;\n return sub;\n }\n\n off(eventType, listener) {\n let sub = this._emitter.off(eventType, listener);\n if (sub) {\n delete this._subs[sub];\n }\n return sub;\n }\n\n removeAllListeners() {\n let current_subs = this._subs;\n this._subs = {};\n Object.keys(current_subs).forEach((sub) => {\n this._emitter.off(current_subs[sub], sub);\n });\n }\n}\n", - "import Events from \"./events\";\n\nexport default class Var {\n constructor(group, name, /*optional*/ value) {\n this._group = group;\n this._name = name;\n this._value = value;\n this._events = new Events();\n }\n\n get() {\n return this._value;\n }\n\n set(value, /*optional*/ event) {\n if (this._value === value) {\n // Do nothing; the value hasn't changed\n return;\n }\n let oldValue = this._value;\n this._value = value;\n // Alert JavaScript listeners that the value has changed\n let evt = {};\n if (event && typeof(event) === \"object\") {\n for (let k in event) {\n if (event.hasOwnProperty(k))\n evt[k] = event[k];\n }\n }\n evt.oldValue = oldValue;\n evt.value = value;\n this._events.trigger(\"change\", evt, this);\n\n // TODO: Make this extensible, to let arbitrary back-ends know that\n // something has changed\n if (global.Shiny && global.Shiny.onInputChange) {\n global.Shiny.onInputChange(\n \".clientValue-\" +\n (this._group.name !== null ? this._group.name + \"-\" : \"\") +\n this._name,\n typeof(value) === \"undefined\" ? null : value\n );\n }\n }\n\n on(eventType, listener) {\n return this._events.on(eventType, listener);\n }\n\n off(eventType, listener) {\n return this._events.off(eventType, listener);\n }\n}\n" - ] -} \ No newline at end of file diff --git a/docs/articles/lifetable_with_demotools_files/crosstalk-1.0.0/js/crosstalk.min.js b/docs/articles/lifetable_with_demotools_files/crosstalk-1.0.0/js/crosstalk.min.js deleted file mode 100644 index 55262e871..000000000 --- a/docs/articles/lifetable_with_demotools_files/crosstalk-1.0.0/js/crosstalk.min.js +++ /dev/null @@ -1,2 +0,0 @@ -!function a(b,c,d){function e(g,h){if(!c[g]){if(!b[g]){var i="function"==typeof require&&require;if(!h&&i)return i(g,!0);if(f)return f(g,!0);var j=new Error("Cannot find module '"+g+"'");throw j.code="MODULE_NOT_FOUND",j}var k=c[g]={exports:{}};b[g][0].call(k.exports,function(a){var c=b[g][1][a];return e(c?c:a)},k,k.exports,a,b,c,d)}return c[g].exports}for(var f="function"==typeof require&&require,g=0;gb?1:void 0}Object.defineProperty(c,"__esModule",{value:!0});var f=function(){function a(a,b){for(var c=0;c0&&void 0!==arguments[0]?arguments[0]:this._allKeys,b=Object.keys(this._handles).length;if(0===b)this._value=null;else{this._value=[];for(var c=0;c?@\[\\\]^`{|}~])/g,"\\$1")}function f(a){var b=h(a);Object.keys(i).forEach(function(c){if(b.hasClass(c)&&!b.hasClass("crosstalk-input-bound")){var d=i[c];g(d,a)}})}function g(a,b){var c=h(b).find("script[type='application/json'][data-for='"+e(b.id)+"']"),d=JSON.parse(c[0].innerText),f=a.factory(b,d);h(b).data("crosstalk-instance",f),h(b).addClass("crosstalk-input-bound")}Object.defineProperty(c,"__esModule",{value:!0}),c.register=b;var h=a.jQuery,i={};a.Shiny&&!function(){var b=new a.Shiny.InputBinding,c=a.jQuery;c.extend(b,{find:function(a){return c(a).find(".crosstalk-input")},initialize:function(a){c(a).hasClass("crosstalk-input-bound")||f(a)},getId:function(a){return a.id},getValue:function(a){},setValue:function(a,b){},receiveMessage:function(a,b){},subscribe:function(a,b){c(a).data("crosstalk-instance").resume()},unsubscribe:function(a){c(a).data("crosstalk-instance").suspend()}}),a.Shiny.inputBindings.register(b,"crosstalk.inputBinding")}()}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{}],7:[function(a,b,c){(function(b){"use strict";function c(a){if(a&&a.__esModule)return a;var b={};if(null!=a)for(var c in a)Object.prototype.hasOwnProperty.call(a,c)&&(b[c]=a[c]);return b.default=a,b}var d=a("./input"),e=c(d),f=a("./filter"),g=b.jQuery;e.register({className:"crosstalk-input-checkboxgroup",factory:function(a,b){var c=new f.FilterHandle(b.group),d=void 0,e=g(a);return e.on("change","input[type='checkbox']",function(){var a=e.find("input[type='checkbox']:checked");0===a.length?(d=null,c.clear()):!function(){var e={};a.each(function(){b.map[this.value].forEach(function(a){e[a]=!0})});var f=Object.keys(e);f.sort(),d=f,c.set(f)}()}),{suspend:function(){c.clear()},resume:function(){d&&c.set(d)}}}})}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./filter":2,"./input":6}],8:[function(a,b,c){(function(b){"use strict";function c(a){if(a&&a.__esModule)return a;var b={};if(null!=a)for(var c in a)Object.prototype.hasOwnProperty.call(a,c)&&(b[c]=a[c]);return b.default=a,b}var d=a("./input"),e=c(d),f=a("./util"),g=c(f),h=a("./filter"),i=b.jQuery;e.register({className:"crosstalk-input-select",factory:function(a,b){var c=[{value:"",label:"(All)"}],d=g.dataframeToD3(b.items),e={options:c.concat(d),valueField:"value",labelField:"label",searchField:"label"},f=i(a).find("select")[0],j=i(f).selectize(e)[0].selectize,k=new h.FilterHandle(b.group),l=void 0;return j.on("change",function(){0===j.items.length?(l=null,k.clear()):!function(){var a={};j.items.forEach(function(c){b.map[c].forEach(function(b){a[b]=!0})});var c=Object.keys(a);c.sort(),l=c,k.set(c)}()}),{suspend:function(){k.clear()},resume:function(){l&&k.set(l)}}}})}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./filter":2,"./input":6,"./util":11}],9:[function(a,b,c){(function(b){"use strict";function c(a){if(a&&a.__esModule)return a;var b={};if(null!=a)for(var c in a)Object.prototype.hasOwnProperty.call(a,c)&&(b[c]=a[c]);return b.default=a,b}function d(a,b){for(var c=a.toString();c.length=i&&m<=j&&k.push(b.keys[l])}k.sort(),d.set(k),p=k}}),{suspend:function(){d.clear()},resume:function(){p&&d.set(p)}}}})}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./filter":2,"./input":6}],10:[function(a,b,c){"use strict";function d(a){if(a&&a.__esModule)return a;var b={};if(null!=a)for(var c in a)Object.prototype.hasOwnProperty.call(a,c)&&(b[c]=a[c]);return b.default=a,b}function e(a){return a&&a.__esModule?a:{default:a}}function f(a,b){if(!(a instanceof b))throw new TypeError("Cannot call a class as a function")}Object.defineProperty(c,"__esModule",{value:!0}),c.SelectionHandle=void 0;var g=function(){function a(a,b){for(var c=0;c0&&void 0!==arguments[0]?arguments[0]:null,c=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;f(this,a),this._eventRelay=new i.default,this._emitter=new m.SubscriptionTracker(this._eventRelay),this._group=null,this._var=null,this._varOnChangeSub=null,this._extraInfo=m.extend({sender:this},c),this.setGroup(b)}return g(a,[{key:"setGroup",value:function(a){var b=this;if(this._group!==a&&(this._group||a)&&(this._var&&(this._var.off("change",this._varOnChangeSub),this._var=null,this._varOnChangeSub=null),this._group=a,a)){this._var=(0,k.default)(a).var("selection");var c=this._var.on("change",function(a){b._eventRelay.trigger("change",a,b)});this._varOnChangeSub=c}}},{key:"_mergeExtraInfo",value:function(a){return m.extend({},this._extraInfo?this._extraInfo:null,a?a:null)}},{key:"set",value:function(a,b){this._var&&this._var.set(a,this._mergeExtraInfo(b))}},{key:"clear",value:function(a){this._var&&this.set(void 0,this._mergeExtraInfo(a))}},{key:"on",value:function(a,b){return this._emitter.on(a,b)}},{key:"off",value:function(a,b){return this._emitter.off(a,b)}},{key:"close",value:function(){this._emitter.removeAllListeners(),this.setGroup(null)}},{key:"value",get:function(){return this._var?this._var.get():null}}]),a}()},{"./events":1,"./group":4,"./util":11}],11:[function(a,b,c){"use strict";function d(a,b){if(!(a instanceof b))throw new TypeError("Cannot call a class as a function")}function e(a){for(var b=arguments.length,c=Array(b>1?b-1:0),d=1;d {\n this._eventRelay.trigger(\"change\", e, this);\n });\n this._varOnChangeSub = sub;\n }\n }\n\n /**\n * Combine the given `extraInfo` (if any) with the handle's default\n * `_extraInfo` (if any).\n * @private\n */\n _mergeExtraInfo(extraInfo) {\n return util.extend({},\n this._extraInfo ? this._extraInfo : null,\n extraInfo ? extraInfo : null);\n }\n\n /**\n * Close the handle. This clears this handle's contribution to the filter set,\n * and unsubscribes all event listeners.\n */\n close() {\n this._emitter.removeAllListeners();\n this.clear();\n this.setGroup(null);\n }\n\n /**\n * Clear this handle's contribution to the filter set.\n *\n * @param {Object} [extraInfo] - Extra properties to be included on the event\n * object that's passed to listeners (in addition to any options that were\n * passed into the `FilterHandle` constructor).\n */\n clear(extraInfo) {\n if (!this._filterSet)\n return;\n this._filterSet.clear(this._id);\n this._onChange(extraInfo);\n }\n\n /**\n * Set this handle's contribution to the filter set. This array should consist\n * of the keys of the rows that _should_ be displayed; any keys that are not\n * present in the array will be considered _filtered out_. Note that multiple\n * `FilterHandle` instances in the group may each contribute an array of keys,\n * and only those keys that appear in _all_ of the arrays make it through the\n * filter.\n *\n * @param {string[]} keys - Empty array, or array of keys. To clear the\n * filter, don't pass an empty array; instead, use the\n * {@link FilterHandle#clear} method.\n * @param {Object} [extraInfo] - Extra properties to be included on the event\n * object that's passed to listeners (in addition to any options that were\n * passed into the `FilterHandle` constructor).\n */\n set(keys, extraInfo) {\n if (!this._filterSet)\n return;\n this._filterSet.update(this._id, keys);\n this._onChange(extraInfo);\n }\n\n /**\n * @return {string[]|null} - Either: 1) an array of keys that made it through\n * all of the `FilterHandle` instances, or, 2) `null`, which means no filter\n * is being applied (all data should be displayed).\n */\n get filteredKeys() {\n return this._filterSet ? this._filterSet.value : null;\n }\n\n /**\n * Subscribe to events on this `FilterHandle`.\n *\n * @param {string} eventType - Indicates the type of events to listen to.\n * Currently, only `\"change\"` is supported.\n * @param {FilterHandle~listener} listener - The callback function that\n * will be invoked when the event occurs.\n * @return {string} - A token to pass to {@link FilterHandle#off} to cancel\n * this subscription.\n */\n on(eventType, listener) {\n return this._emitter.on(eventType, listener);\n }\n\n /**\n * Cancel event subscriptions created by {@link FilterHandle#on}.\n *\n * @param {string} eventType - The type of event to unsubscribe.\n * @param {string|FilterHandle~listener} listener - Either the callback\n * function previously passed into {@link FilterHandle#on}, or the\n * string that was returned from {@link FilterHandle#on}.\n */\n off(eventType, listener) {\n return this._emitter.off(eventType, listener);\n }\n\n _onChange(extraInfo) {\n if (!this._filterSet)\n return;\n this._filterVar.set(this._filterSet.value, this._mergeExtraInfo(extraInfo));\n }\n\n /**\n * @callback FilterHandle~listener\n * @param {Object} event - An object containing details of the event. For\n * `\"change\"` events, this includes the properties `value` (the new\n * value of the filter set, or `null` if no filter set is active),\n * `oldValue` (the previous value of the filter set), and `sender` (the\n * `FilterHandle` instance that made the change).\n */\n\n /**\n * @event FilterHandle#change\n * @type {object}\n * @property {object} value - The new value of the filter set, or `null`\n * if no filter set is active.\n * @property {object} oldValue - The previous value of the filter set.\n * @property {FilterHandle} sender - The `FilterHandle` instance that\n * changed the value.\n */\n}\n","import { diffSortedLists } from \"./util\";\n\nfunction naturalComparator(a, b) {\n if (a === b) {\n return 0;\n } else if (a < b) {\n return -1;\n } else if (a > b) {\n return 1;\n }\n}\n\n/**\n * @private\n */\nexport default class FilterSet {\n constructor() {\n this.reset();\n }\n\n reset() {\n // Key: handle ID, Value: array of selected keys, or null\n this._handles = {};\n // Key: key string, Value: count of handles that include it\n this._keys = {};\n this._value = null;\n this._activeHandles = 0;\n }\n\n get value() {\n return this._value;\n }\n\n update(handleId, keys) {\n if (keys !== null) {\n keys = keys.slice(0); // clone before sorting\n keys.sort(naturalComparator);\n }\n\n let {added, removed} = diffSortedLists(this._handles[handleId], keys);\n this._handles[handleId] = keys;\n\n for (let i = 0; i < added.length; i++) {\n this._keys[added[i]] = (this._keys[added[i]] || 0) + 1;\n }\n for (let i = 0; i < removed.length; i++) {\n this._keys[removed[i]]--;\n }\n\n this._updateValue(keys);\n }\n\n /**\n * @param {string[]} keys Sorted array of strings that indicate\n * a superset of possible keys.\n * @private\n */\n _updateValue(keys = this._allKeys) {\n let handleCount = Object.keys(this._handles).length;\n if (handleCount === 0) {\n this._value = null;\n } else {\n this._value = [];\n for (let i = 0; i < keys.length; i++) {\n let count = this._keys[keys[i]];\n if (count === handleCount) {\n this._value.push(keys[i]);\n }\n }\n }\n }\n\n clear(handleId) {\n if (typeof(this._handles[handleId]) === \"undefined\") {\n return;\n }\n\n let keys = this._handles[handleId];\n if (!keys) {\n keys = [];\n }\n\n for (let i = 0; i < keys.length; i++) {\n this._keys[keys[i]]--;\n }\n delete this._handles[handleId];\n\n this._updateValue();\n }\n\n get _allKeys() {\n let allKeys = Object.keys(this._keys);\n allKeys.sort(naturalComparator);\n return allKeys;\n }\n}\n","import Var from \"./var\";\n\n// Use a global so that multiple copies of crosstalk.js can be loaded and still\n// have groups behave as singletons across all copies.\nglobal.__crosstalk_groups = global.__crosstalk_groups || {};\nlet groups = global.__crosstalk_groups;\n\nexport default function group(groupName) {\n if (groupName && typeof(groupName) === \"string\") {\n if (!groups.hasOwnProperty(groupName)) {\n groups[groupName] = new Group(groupName);\n }\n return groups[groupName];\n } else if (typeof(groupName) === \"object\" && groupName._vars && groupName.var) {\n // Appears to already be a group object\n return groupName;\n } else if (Array.isArray(groupName) &&\n groupName.length == 1 &&\n typeof(groupName[0]) === \"string\") {\n return group(groupName[0]);\n } else {\n throw new Error(\"Invalid groupName argument\");\n }\n}\n\nclass Group {\n constructor(name) {\n this.name = name;\n this._vars = {};\n }\n\n var(name) {\n if (!name || typeof(name) !== \"string\") {\n throw new Error(\"Invalid var name\");\n }\n\n if (!this._vars.hasOwnProperty(name))\n this._vars[name] = new Var(this, name);\n return this._vars[name];\n }\n\n has(name) {\n if (!name || typeof(name) !== \"string\") {\n throw new Error(\"Invalid var name\");\n }\n\n return this._vars.hasOwnProperty(name);\n }\n}\n","import group from \"./group\";\nimport { SelectionHandle } from \"./selection\";\nimport { FilterHandle } from \"./filter\";\nimport \"./input\";\nimport \"./input_selectize\";\nimport \"./input_checkboxgroup\";\nimport \"./input_slider\";\n\nconst defaultGroup = group(\"default\");\n\nfunction var_(name) {\n return defaultGroup.var(name);\n}\n\nfunction has(name) {\n return defaultGroup.has(name);\n}\n\nif (global.Shiny) {\n global.Shiny.addCustomMessageHandler(\"update-client-value\", function(message) {\n if (typeof(message.group) === \"string\") {\n group(message.group).var(message.name).set(message.value);\n } else {\n var_(message.name).set(message.value);\n }\n });\n}\n\nconst crosstalk = {\n group: group,\n var: var_,\n has: has,\n SelectionHandle: SelectionHandle,\n FilterHandle: FilterHandle\n};\n\n/**\n * @namespace crosstalk\n */\nexport default crosstalk;\nglobal.crosstalk = crosstalk;\n","let $ = global.jQuery;\n\nlet bindings = {};\n\nexport function register(reg) {\n bindings[reg.className] = reg;\n if (global.document && global.document.readyState !== \"complete\") {\n $(() => {\n bind();\n });\n } else if (global.document) {\n setTimeout(bind, 100);\n }\n}\n\nfunction bind() {\n Object.keys(bindings).forEach(function(className) {\n let binding = bindings[className];\n $(\".\" + binding.className).not(\".crosstalk-input-bound\").each(function(i, el) {\n bindInstance(binding, el);\n });\n });\n}\n\n// Escape jQuery identifier\nfunction $escape(val) {\n return val.replace(/([!\"#$%&'()*+,.\\/:;<=>?@\\[\\\\\\]^`{|}~])/g, \"\\\\$1\");\n}\n\nfunction bindEl(el) {\n let $el = $(el);\n Object.keys(bindings).forEach(function(className) {\n if ($el.hasClass(className) && !$el.hasClass(\"crosstalk-input-bound\")) {\n let binding = bindings[className];\n bindInstance(binding, el);\n }\n });\n}\n\nfunction bindInstance(binding, el) {\n let jsonEl = $(el).find(\"script[type='application/json'][data-for='\" + $escape(el.id) + \"']\");\n let data = JSON.parse(jsonEl[0].innerText);\n\n let instance = binding.factory(el, data);\n $(el).data(\"crosstalk-instance\", instance);\n $(el).addClass(\"crosstalk-input-bound\");\n}\n\nif (global.Shiny) {\n let inputBinding = new global.Shiny.InputBinding();\n let $ = global.jQuery;\n $.extend(inputBinding, {\n find: function(scope) {\n return $(scope).find(\".crosstalk-input\");\n },\n initialize: function(el) {\n if (!$(el).hasClass(\"crosstalk-input-bound\")) {\n bindEl(el);\n }\n },\n getId: function(el) {\n return el.id;\n },\n getValue: function(el) {\n\n },\n setValue: function(el, value) {\n\n },\n receiveMessage: function(el, data) {\n\n },\n subscribe: function(el, callback) {\n $(el).data(\"crosstalk-instance\").resume();\n },\n unsubscribe: function(el) {\n $(el).data(\"crosstalk-instance\").suspend();\n }\n });\n global.Shiny.inputBindings.register(inputBinding, \"crosstalk.inputBinding\");\n}\n","import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n className: \"crosstalk-input-checkboxgroup\",\n\n factory: function(el, data) {\n /*\n * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n * group: \"ct-groupname\"\n */\n let ctHandle = new FilterHandle(data.group);\n\n let lastKnownKeys;\n let $el = $(el);\n $el.on(\"change\", \"input[type='checkbox']\", function() {\n let checked = $el.find(\"input[type='checkbox']:checked\");\n if (checked.length === 0) {\n lastKnownKeys = null;\n ctHandle.clear();\n } else {\n let keys = {};\n checked.each(function() {\n data.map[this.value].forEach(function(key) {\n keys[key] = true;\n });\n });\n let keyArray = Object.keys(keys);\n keyArray.sort();\n lastKnownKeys = keyArray;\n ctHandle.set(keyArray);\n }\n });\n\n return {\n suspend: function() {\n ctHandle.clear();\n },\n resume: function() {\n if (lastKnownKeys)\n ctHandle.set(lastKnownKeys);\n }\n };\n }\n});\n","import * as input from \"./input\";\nimport * as util from \"./util\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n className: \"crosstalk-input-select\",\n\n factory: function(el, data) {\n /*\n * items: {value: [...], label: [...]}\n * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n * group: \"ct-groupname\"\n */\n\n let first = [{value: \"\", label: \"(All)\"}];\n let items = util.dataframeToD3(data.items);\n let opts = {\n options: first.concat(items),\n valueField: \"value\",\n labelField: \"label\",\n searchField: \"label\"\n };\n\n let select = $(el).find(\"select\")[0];\n\n let selectize = $(select).selectize(opts)[0].selectize;\n\n let ctHandle = new FilterHandle(data.group);\n\n let lastKnownKeys;\n selectize.on(\"change\", function() {\n if (selectize.items.length === 0) {\n lastKnownKeys = null;\n ctHandle.clear();\n } else {\n let keys = {};\n selectize.items.forEach(function(group) {\n data.map[group].forEach(function(key) {\n keys[key] = true;\n });\n });\n let keyArray = Object.keys(keys);\n keyArray.sort();\n lastKnownKeys = keyArray;\n ctHandle.set(keyArray);\n }\n });\n\n return {\n suspend: function() {\n ctHandle.clear();\n },\n resume: function() {\n if (lastKnownKeys)\n ctHandle.set(lastKnownKeys);\n }\n };\n }\n});\n","import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\nlet strftime = global.strftime;\n\ninput.register({\n className: \"crosstalk-input-slider\",\n\n factory: function(el, data) {\n /*\n * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n * group: \"ct-groupname\"\n */\n let ctHandle = new FilterHandle(data.group);\n\n let opts = {};\n let $el = $(el).find(\"input\");\n let dataType = $el.data(\"data-type\");\n let timeFormat = $el.data(\"time-format\");\n let timeFormatter;\n\n // Set up formatting functions\n if (dataType === \"date\") {\n timeFormatter = strftime.utc();\n opts.prettify = function(num) {\n return timeFormatter(timeFormat, new Date(num));\n };\n\n } else if (dataType === \"datetime\") {\n let timezone = $el.data(\"timezone\");\n if (timezone)\n timeFormatter = strftime.timezone(timezone);\n else\n timeFormatter = strftime;\n\n opts.prettify = function(num) {\n return timeFormatter(timeFormat, new Date(num));\n };\n }\n\n $el.ionRangeSlider(opts);\n\n function getValue() {\n let result = $el.data(\"ionRangeSlider\").result;\n\n // Function for converting numeric value from slider to appropriate type.\n let convert;\n let dataType = $el.data(\"data-type\");\n if (dataType === \"date\") {\n convert = function(val) {\n return formatDateUTC(new Date(+val));\n };\n } else if (dataType === \"datetime\") {\n convert = function(val) {\n // Convert ms to s\n return +val / 1000;\n };\n } else {\n convert = function(val) { return +val; };\n }\n\n if ($el.data(\"ionRangeSlider\").options.type === \"double\") {\n return [convert(result.from), convert(result.to)];\n } else {\n return convert(result.from);\n }\n }\n\n let lastKnownKeys = null;\n\n $el.on(\"change.crosstalkSliderInput\", function(event) {\n if (!$el.data(\"updating\") && !$el.data(\"animating\")) {\n let [from, to] = getValue();\n let keys = [];\n for (let i = 0; i < data.values.length; i++) {\n let val = data.values[i];\n if (val >= from && val <= to) {\n keys.push(data.keys[i]);\n }\n }\n keys.sort();\n ctHandle.set(keys);\n lastKnownKeys = keys;\n }\n });\n\n\n // let $el = $(el);\n // $el.on(\"change\", \"input[type=\"checkbox\"]\", function() {\n // let checked = $el.find(\"input[type=\"checkbox\"]:checked\");\n // if (checked.length === 0) {\n // ctHandle.clear();\n // } else {\n // let keys = {};\n // checked.each(function() {\n // data.map[this.value].forEach(function(key) {\n // keys[key] = true;\n // });\n // });\n // let keyArray = Object.keys(keys);\n // keyArray.sort();\n // ctHandle.set(keyArray);\n // }\n // });\n\n return {\n suspend: function() {\n ctHandle.clear();\n },\n resume: function() {\n if (lastKnownKeys)\n ctHandle.set(lastKnownKeys);\n }\n };\n }\n});\n\n\n// Convert a number to a string with leading zeros\nfunction padZeros(n, digits) {\n let str = n.toString();\n while (str.length < digits)\n str = \"0\" + str;\n return str;\n}\n\n// Given a Date object, return a string in yyyy-mm-dd format, using the\n// UTC date. This may be a day off from the date in the local time zone.\nfunction formatDateUTC(date) {\n if (date instanceof Date) {\n return date.getUTCFullYear() + \"-\" +\n padZeros(date.getUTCMonth()+1, 2) + \"-\" +\n padZeros(date.getUTCDate(), 2);\n\n } else {\n return null;\n }\n}\n","import Events from \"./events\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\nexport class SelectionHandle {\n\n /**\n * @classdesc\n * Use this class to read and write (and listen for changes to) the selection\n * for a Crosstalk group. This is intended to be used for linked brushing.\n *\n * If two (or more) `SelectionHandle` instances in the same webpage share the\n * same group name, they will share the same state. Setting the selection using\n * one `SelectionHandle` instance will result in the `value` property instantly\n * changing across the others, and `\"change\"` event listeners on all instances\n * (including the one that initiated the sending) will fire.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n * null or undefined (or any other falsy value). This can be changed later\n * via the [SelectionHandle#setGroup](#setGroup) method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n * the event object whenever an event is emitted.\n */\n constructor(group = null, extraInfo = null) {\n this._eventRelay = new Events();\n this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n // Name of the group we're currently tracking, if any. Can change over time.\n this._group = null;\n // The Var we're currently tracking, if any. Can change over time.\n this._var = null;\n // The event handler subscription we currently have on var.on(\"change\").\n this._varOnChangeSub = null;\n\n this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n this.setGroup(group);\n }\n\n /**\n * Changes the Crosstalk group membership of this SelectionHandle. The group\n * being switched away from (if any) will not have its selection value\n * modified as a result of calling `setGroup`, even if this handle was the\n * most recent handle to set the selection of the group.\n *\n * The group being switched to (if any) will also not have its selection value\n * modified as a result of calling `setGroup`. If you want to set the\n * selection value of the new group, call `set` explicitly.\n *\n * @param {string} group - The name of the Crosstalk group, or null (or\n * undefined) to clear the group.\n */\n setGroup(group) {\n // If group is unchanged, do nothing\n if (this._group === group)\n return;\n // Treat null, undefined, and other falsy values the same\n if (!this._group && !group)\n return;\n\n if (this._var) {\n this._var.off(\"change\", this._varOnChangeSub);\n this._var = null;\n this._varOnChangeSub = null;\n }\n\n this._group = group;\n\n if (group) {\n this._var = grp(group).var(\"selection\");\n let sub = this._var.on(\"change\", (e) => {\n this._eventRelay.trigger(\"change\", e, this);\n });\n this._varOnChangeSub = sub;\n }\n }\n\n /**\n * Retrieves the current selection for the group represented by this\n * `SelectionHandle`.\n *\n * - If no selection is active, then this value will be falsy.\n * - If a selection is active, but no data points are selected, then this\n * value will be an empty array.\n * - If a selection is active, and data points are selected, then the keys\n * of the selected data points will be present in the array.\n */\n get value() {\n return this._var ? this._var.get() : null;\n }\n\n /**\n * Combines the given `extraInfo` (if any) with the handle's default\n * `_extraInfo` (if any).\n * @private\n */\n _mergeExtraInfo(extraInfo) {\n // Important incidental effect: shallow clone is returned\n return util.extend({},\n this._extraInfo ? this._extraInfo : null,\n extraInfo ? extraInfo : null);\n }\n\n /**\n * Overwrites the current selection for the group, and raises the `\"change\"`\n * event among all of the group's '`SelectionHandle` instances (including\n * this one).\n *\n * @fires SelectionHandle#change\n * @param {string[]} selectedKeys - Falsy, empty array, or array of keys (see\n * {@link SelectionHandle#value}).\n * @param {Object} [extraInfo] - Extra properties to be included on the event\n * object that's passed to listeners (in addition to any options that were\n * passed into the `SelectionHandle` constructor).\n */\n set(selectedKeys, extraInfo) {\n if (this._var)\n this._var.set(selectedKeys, this._mergeExtraInfo(extraInfo));\n }\n\n /**\n * Overwrites the current selection for the group, and raises the `\"change\"`\n * event among all of the group's '`SelectionHandle` instances (including\n * this one).\n *\n * @fires SelectionHandle#change\n * @param {Object} [extraInfo] - Extra properties to be included on the event\n * object that's passed to listeners (in addition to any that were passed\n * into the `SelectionHandle` constructor).\n */\n clear(extraInfo) {\n if (this._var)\n this.set(void 0, this._mergeExtraInfo(extraInfo));\n }\n\n /**\n * Subscribes to events on this `SelectionHandle`.\n *\n * @param {string} eventType - Indicates the type of events to listen to.\n * Currently, only `\"change\"` is supported.\n * @param {SelectionHandle~listener} listener - The callback function that\n * will be invoked when the event occurs.\n * @return {string} - A token to pass to {@link SelectionHandle#off} to cancel\n * this subscription.\n */\n on(eventType, listener) {\n return this._emitter.on(eventType, listener);\n }\n\n /**\n * Cancels event subscriptions created by {@link SelectionHandle#on}.\n *\n * @param {string} eventType - The type of event to unsubscribe.\n * @param {string|SelectionHandle~listener} listener - Either the callback\n * function previously passed into {@link SelectionHandle#on}, or the\n * string that was returned from {@link SelectionHandle#on}.\n */\n off(eventType, listener) {\n return this._emitter.off(eventType, listener);\n }\n\n /**\n * Shuts down the `SelectionHandle` object.\n *\n * Removes all event listeners that were added through this handle.\n */\n close() {\n this._emitter.removeAllListeners();\n this.setGroup(null);\n }\n\n /**\n * @callback SelectionHandle~listener\n * @param {Object} event - An object containing details of the event. For\n * `\"change\"` events, this includes the properties `value` (the new\n * value of the selection, or `undefined` if no selection is active),\n * `oldValue` (the previous value of the selection), and `sender` (the\n * `SelectionHandle` instance that made the change).\n */\n\n /**\n * @event SelectionHandle#change\n * @type {object}\n * @property {object} value - The new value of the selection, or `undefined`\n * if no selection is active.\n * @property {object} oldValue - The previous value of the selection.\n * @property {SelectionHandle} sender - The `SelectionHandle` instance that\n * changed the value.\n */\n}\n","export function extend(target, ...sources) {\n for (let i = 0; i < sources.length; i++) {\n let src = sources[i];\n if (typeof(src) === \"undefined\" || src === null)\n continue;\n\n for (let key in src) {\n if (src.hasOwnProperty(key)) {\n target[key] = src[key];\n }\n }\n }\n return target;\n}\n\nexport function checkSorted(list) {\n for (let i = 1; i < list.length; i++) {\n if (list[i] <= list[i-1]) {\n throw new Error(\"List is not sorted or contains duplicate\");\n }\n }\n}\n\nexport function diffSortedLists(a, b) {\n let i_a = 0;\n let i_b = 0;\n\n if (!a) a = [];\n if (!b) b = [];\n\n let a_only = [];\n let b_only = [];\n\n checkSorted(a);\n checkSorted(b);\n\n while (i_a < a.length && i_b < b.length) {\n if (a[i_a] === b[i_b]) {\n i_a++;\n i_b++;\n } else if (a[i_a] < b[i_b]) {\n a_only.push(a[i_a++]);\n } else {\n b_only.push(b[i_b++]);\n }\n }\n\n if (i_a < a.length)\n a_only = a_only.concat(a.slice(i_a));\n if (i_b < b.length)\n b_only = b_only.concat(b.slice(i_b));\n return {\n removed: a_only,\n added: b_only\n };\n}\n\n// Convert from wide: { colA: [1,2,3], colB: [4,5,6], ... }\n// to long: [ {colA: 1, colB: 4}, {colA: 2, colB: 5}, ... ]\nexport function dataframeToD3(df) {\n let names = [];\n let length;\n for (let name in df) {\n if (df.hasOwnProperty(name))\n names.push(name);\n if (typeof(df[name]) !== \"object\" || typeof(df[name].length) === \"undefined\") {\n throw new Error(\"All fields must be arrays\");\n } else if (typeof(length) !== \"undefined\" && length !== df[name].length) {\n throw new Error(\"All fields must be arrays of the same length\");\n }\n length = df[name].length;\n }\n let results = [];\n let item;\n for (let row = 0; row < length; row++) {\n item = {};\n for (let col = 0; col < names.length; col++) {\n item[names[col]] = df[names[col]][row];\n }\n results.push(item);\n }\n return results;\n}\n\n/**\n * Keeps track of all event listener additions/removals and lets all active\n * listeners be removed with a single operation.\n *\n * @private\n */\nexport class SubscriptionTracker {\n constructor(emitter) {\n this._emitter = emitter;\n this._subs = {};\n }\n\n on(eventType, listener) {\n let sub = this._emitter.on(eventType, listener);\n this._subs[sub] = eventType;\n return sub;\n }\n\n off(eventType, listener) {\n let sub = this._emitter.off(eventType, listener);\n if (sub) {\n delete this._subs[sub];\n }\n return sub;\n }\n\n removeAllListeners() {\n let current_subs = this._subs;\n this._subs = {};\n Object.keys(current_subs).forEach((sub) => {\n this._emitter.off(current_subs[sub], sub);\n });\n }\n}\n","import Events from \"./events\";\n\nexport default class Var {\n constructor(group, name, /*optional*/ value) {\n this._group = group;\n this._name = name;\n this._value = value;\n this._events = new Events();\n }\n\n get() {\n return this._value;\n }\n\n set(value, /*optional*/ event) {\n if (this._value === value) {\n // Do nothing; the value hasn't changed\n return;\n }\n let oldValue = this._value;\n this._value = value;\n // Alert JavaScript listeners that the value has changed\n let evt = {};\n if (event && typeof(event) === \"object\") {\n for (let k in event) {\n if (event.hasOwnProperty(k))\n evt[k] = event[k];\n }\n }\n evt.oldValue = oldValue;\n evt.value = value;\n this._events.trigger(\"change\", evt, this);\n\n // TODO: Make this extensible, to let arbitrary back-ends know that\n // something has changed\n if (global.Shiny && global.Shiny.onInputChange) {\n global.Shiny.onInputChange(\n \".clientValue-\" +\n (this._group.name !== null ? this._group.name + \"-\" : \"\") +\n this._name,\n typeof(value) === \"undefined\" ? null : value\n );\n }\n }\n\n on(eventType, listener) {\n return this._events.on(eventType, listener);\n }\n\n off(eventType, listener) {\n return this._events.off(eventType, listener);\n }\n}\n"]} \ No newline at end of file diff --git a/docs/articles/lifetable_with_demotools_files/dt-core-1.10.19/css/jquery.dataTables.extra.css b/docs/articles/lifetable_with_demotools_files/dt-core-1.10.19/css/jquery.dataTables.extra.css deleted file mode 100644 index dbbac6ddd..000000000 --- a/docs/articles/lifetable_with_demotools_files/dt-core-1.10.19/css/jquery.dataTables.extra.css +++ /dev/null @@ -1,8 +0,0 @@ -/* Selected rows/cells */ -table.dataTable tr.selected td, table.dataTable td.selected { - background-color: #b0bed9 !important; -} -/* In case of scrollX/Y or FixedHeader */ -.dataTables_scrollBody .dataTables_sizing { - visibility: hidden; -} diff --git a/docs/articles/lifetable_with_demotools_files/dt-core-1.10.19/js/jquery.dataTables.min.js b/docs/articles/lifetable_with_demotools_files/dt-core-1.10.19/js/jquery.dataTables.min.js deleted file mode 100644 index 07af1c399..000000000 --- a/docs/articles/lifetable_with_demotools_files/dt-core-1.10.19/js/jquery.dataTables.min.js +++ /dev/null @@ -1,166 +0,0 @@ -/*! - DataTables 1.10.19 - ©2008-2018 SpryMedia Ltd - datatables.net/license -*/ -(function(h){"function"===typeof define&&define.amd?define(["jquery"],function(E){return h(E,window,document)}):"object"===typeof exports?module.exports=function(E,H){E||(E=window);H||(H="undefined"!==typeof window?require("jquery"):require("jquery")(E));return h(H,E,E.document)}:h(jQuery,window,document)})(function(h,E,H,k){function Z(a){var b,c,d={};h.each(a,function(e){if((b=e.match(/^([^A-Z]+?)([A-Z])/))&&-1!=="a aa ai ao as b fn i m o s ".indexOf(b[1]+" "))c=e.replace(b[0],b[2].toLowerCase()), -d[c]=e,"o"===b[1]&&Z(a[e])});a._hungarianMap=d}function J(a,b,c){a._hungarianMap||Z(a);var d;h.each(b,function(e){d=a._hungarianMap[e];if(d!==k&&(c||b[d]===k))"o"===d.charAt(0)?(b[d]||(b[d]={}),h.extend(!0,b[d],b[e]),J(a[d],b[d],c)):b[d]=b[e]})}function Ca(a){var b=n.defaults.oLanguage,c=b.sDecimal;c&&Da(c);if(a){var d=a.sZeroRecords;!a.sEmptyTable&&(d&&"No data available in table"===b.sEmptyTable)&&F(a,a,"sZeroRecords","sEmptyTable");!a.sLoadingRecords&&(d&&"Loading..."===b.sLoadingRecords)&&F(a, -a,"sZeroRecords","sLoadingRecords");a.sInfoThousands&&(a.sThousands=a.sInfoThousands);(a=a.sDecimal)&&c!==a&&Da(a)}}function fb(a){A(a,"ordering","bSort");A(a,"orderMulti","bSortMulti");A(a,"orderClasses","bSortClasses");A(a,"orderCellsTop","bSortCellsTop");A(a,"order","aaSorting");A(a,"orderFixed","aaSortingFixed");A(a,"paging","bPaginate");A(a,"pagingType","sPaginationType");A(a,"pageLength","iDisplayLength");A(a,"searching","bFilter");"boolean"===typeof a.sScrollX&&(a.sScrollX=a.sScrollX?"100%": -"");"boolean"===typeof a.scrollX&&(a.scrollX=a.scrollX?"100%":"");if(a=a.aoSearchCols)for(var b=0,c=a.length;b").css({position:"fixed",top:0,left:-1*h(E).scrollLeft(),height:1,width:1, -overflow:"hidden"}).append(h("
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-Z(n.defaults.column);n.models.oSettings={oFeatures:{bAutoWidth:null,bDeferRender:null,bFilter:null,bInfo:null,bLengthChange:null,bPaginate:null,bProcessing:null,bServerSide:null,bSort:null,bSortMulti:null,bSortClasses:null,bStateSave:null},oScroll:{bCollapse:null,iBarWidth:0,sX:null,sXInner:null,sY:null},oLanguage:{fnInfoCallback:null},oBrowser:{bScrollOversize:!1,bScrollbarLeft:!1,bBounding:!1,barWidth:0},ajax:null,aanFeatures:[],aoData:[],aiDisplay:[],aiDisplayMaster:[],aIds:{},aoColumns:[],aoHeader:[], -aoFooter:[],oPreviousSearch:{},aoPreSearchCols:[],aaSorting:null,aaSortingFixed:[],asStripeClasses:null,asDestroyStripes:[],sDestroyWidth:0,aoRowCallback:[],aoHeaderCallback:[],aoFooterCallback:[],aoDrawCallback:[],aoRowCreatedCallback:[],aoPreDrawCallback:[],aoInitComplete:[],aoStateSaveParams:[],aoStateLoadParams:[],aoStateLoaded:[],sTableId:"",nTable:null,nTHead:null,nTFoot:null,nTBody:null,nTableWrapper:null,bDeferLoading:!1,bInitialised:!1,aoOpenRows:[],sDom:null,searchDelay:null,sPaginationType:"two_button", -iStateDuration:0,aoStateSave:[],aoStateLoad:[],oSavedState:null,oLoadedState:null,sAjaxSource:null,sAjaxDataProp:null,bAjaxDataGet:!0,jqXHR:null,json:k,oAjaxData:k,fnServerData:null,aoServerParams:[],sServerMethod:null,fnFormatNumber:null,aLengthMenu:null,iDraw:0,bDrawing:!1,iDrawError:-1,_iDisplayLength:10,_iDisplayStart:0,_iRecordsTotal:0,_iRecordsDisplay:0,oClasses:{},bFiltered:!1,bSorted:!1,bSortCellsTop:null,oInit:null,aoDestroyCallback:[],fnRecordsTotal:function(){return"ssp"==y(this)?1*this._iRecordsTotal: -this.aiDisplayMaster.length},fnRecordsDisplay:function(){return"ssp"==y(this)?1*this._iRecordsDisplay:this.aiDisplay.length},fnDisplayEnd:function(){var a=this._iDisplayLength,b=this._iDisplayStart,c=b+a,d=this.aiDisplay.length,e=this.oFeatures,f=e.bPaginate;return e.bServerSide?!1===f||-1===a?b+d:Math.min(b+a,this._iRecordsDisplay):!f||c>d||-1===a?d:c},oInstance:null,sInstance:null,iTabIndex:0,nScrollHead:null,nScrollFoot:null,aLastSort:[],oPlugins:{},rowIdFn:null,rowId:null};n.ext=x={buttons:{}, -classes:{},builder:"-source-",errMode:"alert",feature:[],search:[],selector:{cell:[],column:[],row:[]},internal:{},legacy:{ajax:null},pager:{},renderer:{pageButton:{},header:{}},order:{},type:{detect:[],search:{},order:{}},_unique:0,fnVersionCheck:n.fnVersionCheck,iApiIndex:0,oJUIClasses:{},sVersion:n.version};h.extend(x,{afnFiltering:x.search,aTypes:x.type.detect,ofnSearch:x.type.search,oSort:x.type.order,afnSortData:x.order,aoFeatures:x.feature,oApi:x.internal,oStdClasses:x.classes,oPagination:x.pager}); 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div.dataTables_scrollBody>table{border-bottom:none}.dataTables_wrapper:after{visibility:hidden;display:block;content:"";clear:both;height:0}@media screen and (max-width: 767px){.dataTables_wrapper .dataTables_info,.dataTables_wrapper .dataTables_paginate{float:none;text-align:center}.dataTables_wrapper .dataTables_paginate{margin-top:0.5em}}@media screen and (max-width: 640px){.dataTables_wrapper .dataTables_length,.dataTables_wrapper .dataTables_filter{float:none;text-align:center}.dataTables_wrapper .dataTables_filter{margin-top:0.5em}} diff --git a/docs/articles/lifetable_with_demotools.html b/docs/articles/lifetables_with_demotools.html similarity index 57% rename from docs/articles/lifetable_with_demotools.html rename to docs/articles/lifetables_with_demotools.html index c12a08c60..f08a2563c 100644 --- a/docs/articles/lifetable_with_demotools.html +++ b/docs/articles/lifetables_with_demotools.html @@ -5,7 +5,7 @@ -The lifetable with DemoTools • DemoTools +Lifetables with DemoTools • DemoTools @@ -17,7 +17,7 @@ - + + + + + + + + +Redistribute population over a specified age based on a stable standard fit to the data — OPAG • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                +
                                + + + + +
                                + +
                                +
                                +
                                +

                                Redistribute population over a specified age based on a stable standard fit to the data

                                + Source: R/OPAG.R + +
                                + +
                                +

                                This can be used as an external check of population counts +in older ages, assuming the stable population standard is representative enough, or it can be used to redistribute population in ages above a +specified ages Redistribute_from. This is handy, for instance, for +ensuring all censuses extend to a specified maximum age (e.g. 100+) +prior to intercensal interpolations. The assumption is that, at least in +ages including Age_fit and higher ages, the population should follow +a stable pattern proportional to a given survival curve subject to +constant growth, r.

                                +
                                + + 
                                OPAG(
+  Pop,
+  Age_Pop,
+  nLx,
+  Age_nLx,
+  Age_fit = NULL,
+  AgeInt_fit = NULL,
+  Redistribute_from = max(Age_Pop),
+  OAnew = max(Age_nLx),
+  method = "mono"
+)
                                + +

                                Arguments

                                + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                Pop

                                numeric vector of population counts

                                Age_Pop

                                integer vector of the lower bounds of the population age groups

                                nLx

                                numeric vector of stationary population age structure in arbitrary integer age groups

                                Age_nLx

                                integer vector of lower bounds of age groups of nLx

                                Age_fit

                                integer vector of lower bounds for age groups of Pop_fit

                                AgeInt_fit

                                integer vector of widths of age groups of Pop_fit

                                Redistribute_from

                                integer lower age bound that forms the cutoff, above which we redistribute counts using the stable standard.

                                OAnew

                                integer. Desired open age group in the output (must being element of Age_nLx)

                                method

                                character, graduation method used for intermediate graduation. Default "mono". Other reasonable choices include "pclm" or "uniform".

                                + +

                                Details

                                + +

                                It may be helpful to try more than one fitting possibility, +and more than one Redistribute_from cut point, as results may vary.

                                +

                                Redistribute_from can be lower than your current open age group, +and OAnew can be higher, as long as it is within the range of Age_nLx. +If Age_nLx doesn't go high enough for your needs, you can extrapolate +it ahead of time. For this, you'd want the nMx the underlie it, and you +can use lt_abridged(), specifying a higher open age, and then +extracting nLx again from it.

                                + +

                                Examples

                                + 
                                # India Males, 1971
+Pop            <- smooth_age_5(pop1m_ind,
+                         Age = 0:100,
+                         method = "Arriaga")
+Age_Pop        <- names2age(Pop)
+AgeInt_Pop     <- age2int(Age_Pop, OAvalue = 1)
+
+nLx            <- downloadnLx(NULL, "India","male",1971)
+
                                #> Downloading nLx data for India, years 1971, gender male
                                Age_nLx        <- names2age(nLx)
+AgeInt_nLx     <- age2int(Age_nLx, OAvalue = 1)
+
+Pop_fit <- OPAG(Pop,
+    Age_Pop = Age_Pop,
+    nLx = nLx,
+    Age_nLx = Age_nLx,
+    Age_fit =  c(60,70),
+    AgeInt_fit = c(10,10),
+    Redistribute_from = 80)
+
                                #> 
+#> Age_Pop and Age_nLx age intervals are different!
                                
+if (FALSE) {
+# look at 75+
+ind <- Age_Pop >= 75
+plot(Age_Pop[ind], Pop[ind])
+lines(Age_Pop[ind], Pop_fit$Pop_out[ind], col = "blue")
+
+# relative differences in ages 80+
+ind <- Age_Pop >= 80
+plot(Age_Pop[ind],  (Pop_fit$Pop_out[ind] - Pop[ind]) / Pop[ind])
+}
+
                                +
                                + +
                                + + +
                                +
                                +

                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                +
                                + +
                                +

                                Site built with pkgdown 1.6.1.

                                +
                                + +
                                +
                                + + + + + + + + diff --git a/docs/reference/OPAG_fit_stable_standard.html b/docs/reference/OPAG_fit_stable_standard.html new file mode 100644 index 000000000..8d95204c8 --- /dev/null +++ b/docs/reference/OPAG_fit_stable_standard.html @@ -0,0 +1,270 @@ + + + + + + + + +creates stable standard based on optimizing the growth rate — OPAG_fit_stable_standard • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                +
                                + + + + +
                                + +
                                +
                                +
                                +

                                creates stable standard based on optimizing the growth rate

                                + Source: R/OPAG.R + +
                                + +
                                +

                                The stationary standard, nLx is transformed into a stable standard by optimizing a growth rate, r such that the stable standard matches observed population counts in selected age groups. Usually the ages used for fitting are wide age groups in older ages preceding the open age group. The standard output by this function is used by OPAG to create the standard used to redistribute counts over older age groups up to a specified open age group, such as 100.

                                +
                                + + 
                                OPAG_fit_stable_standard(Pop_fit, Age_fit, AgeInt_fit, Lx1, Age_Lx1)
                                + +

                                Arguments

                                + + + + + + + + + + + + + + + + + + + + + + +
                                Pop_fit

                                numeric vector of at least two population counts to use for fitting

                                Age_fit

                                integer vector of lower bounds for age groups of Pop_fit

                                AgeInt_fit

                                integer vector of widths of age groups of Pop_fit

                                Lx1

                                numeric vector of stable population standard by single ages

                                Age_Lx1

                                integer vector of lower bounds for age groups of Lx1

                                + +

                                Value

                                + +

                                list constaining

                                  +

                                1. Standard numeric vector, the transformed nLx to be used for +redistribution in OPAG()

                                  2. +

                                2. r_opt the output of optimize(), where min is the growth parameter, r

                                  3. +
                                + +

                                Details

                                + +

                                The argument method don't have much leverage on the result. In short, the stable population transformation is done by ungrouping nLx to single ages (if it isn't already), and method controls which graduation method is used for this, where "uniform", "mono", "pclm" are the reasonable choices at this writing.

                                + +

                                Examples

                                + 
                                Pop_fit    <- c(85000,37000)
+Age_fit    <- c(70,80)
+nLx        <- downloadnLx(NULL, "Spain","female",1971)
+
                                #> Downloading nLx data for Spain, years 1971, gender female
                                Age_nLx    <- names2age(nLx)
+Lx1 <- graduate(nLx,Age=Age_nLx,method = "mono")
+Age_Lx1 <- 0:100
+# India Males, 1971
+Pop        <- smooth_age_5(pop1m_ind,
+                           Age = 0:100,
+                           method = "Arriaga")
+Pop80      <- groupOAG(Pop, names2age(Pop), 80)
+Age        <- names2age(Pop80)
+
+nLx        <- downloadnLx(NULL, "India","male",1971)
+
                                #> Downloading nLx data for India, years 1971, gender male
                                Age_nLx    <- names2age(nLx)
+
+
+Pop_fit    <- groupAges(Pop80, Age, N = 10)[c("60","70")]
+Age_fit    <- c(60,70)
+AgeInt_fit <- c(10,10)
+
+Standard <- OPAG_fit_stable_standard(
+  Pop_fit = Pop_fit,
+  Age_fit = Age_fit,
+  AgeInt_fit = AgeInt_fit,
+  Lx1=Lx1,
+  Age_Lx1 = Age_Lx1
+)
+
+# A visual comparison:
+nL60 <- rescale_vector(nLx[Age_nLx >= 60])
+St60p <- rescale_vector( Standard$Standard[c(0:100) >= 60] )
+ages_plot <- seq(60,100,by=5)
+if (FALSE) {
+  plot(ages_plot,nL60, type = 'l')
+  lines(60:100, St60p, col = "blue")
+}
+
                                +
                                + +
                                + + +
                                +
                                +

                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                +
                                + +
                                +

                                Site built with pkgdown 1.6.1.

                                +
                                + +
                                +
                                + + + + + + + + diff --git a/docs/reference/OPAG_nLx_warp_r.html b/docs/reference/OPAG_nLx_warp_r.html new file mode 100644 index 000000000..cb2d230a2 --- /dev/null +++ b/docs/reference/OPAG_nLx_warp_r.html @@ -0,0 +1,234 @@ + + + + + + + + +Warps a given stationary population into a stable population — OPAG_nLx_warp_r • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                +
                                + + + + +
                                + +
                                +
                                +
                                +

                                Warps a given stationary population into a stable population

                                + Source: R/OPAG.R + +
                                + +
                                +

                                We take nLx as indicative of a stationary population age structure, +then subject the population structure to long-term growth by a constant rate, r.

                                +
                                + + 
                                OPAG_nLx_warp_r(Lx1, Age_Lx1, r)
                                + +

                                Arguments

                                + + + + + + + + + + + + + + +
                                Lx1

                                numeric vector of stationary population age structure in arbitrary integer age groups

                                Age_Lx1

                                integer vector of lower bounds of age groups of nLx

                                r

                                stable growth rate

                                + +

                                Value

                                + +

                                numeric vector of the transformed nLx. Note, this vector sums to 1.

                                +

                                Details

                                + +

                                Lx1 could be any population structure of any scale, as long as you're comfortable +assuming it's stationary and can be warped into stable. For the oldest ages, this is probably +quite often an acceptable and useful approximation. The transformation is applied at the single-age scale, even if the input nLx is in wider (e.g. abridged) age groups. When needed, we reduce to single ages using (default) graduate_uniform(), then apply the transformation, then group back. This is innocuous if nLx is given in single ages. You may want to change method to "mono" or "pclm".

                                + +

                                Examples

                                + 
                                Lx  <- downloadnLx(NULL, "Spain","female",1971)
+
                                #> Downloading nLx data for Spain, years 1971, gender female
                                Age <- names2age(Lx)
+r   <- .01
+ai  <- age2int(Age)
+if (FALSE) {
+plot(Age, Lx/sum(Lx) / ai, type = 's',ylim = c(0,0.015))
+lines(Age,OPAG_nLx_warp_r(Lx,Age,0.005)/ai,type='s',col = "red")
+lines(Age,OPAG_nLx_warp_r(Lx,Age,-0.005)/ai,type='s',col = "blue")
+}
+
+
                                +
                                + +
                                + + +
                                +
                                +

                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                +
                                + +
                                +

                                Site built with pkgdown 1.6.1.

                                +
                                + +
                                +
                                + + + + + + + + diff --git a/docs/reference/OPAG_r_min.html b/docs/reference/OPAG_r_min.html new file mode 100644 index 000000000..e362487a2 --- /dev/null +++ b/docs/reference/OPAG_r_min.html @@ -0,0 +1,261 @@ + + + + + + + + +calculates residual for optimizing growth rate r for OPAG family — OPAG_r_min • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                +
                                + + + + +
                                + +
                                +
                                +
                                +

                                calculates residual for optimizing growth rate r for OPAG family

                                + Source: R/OPAG.R + +
                                + +
                                +

                                For a given set of age groups to fit against, and a given stable growth rate, $r$, +what is the error implied given the current $r$ and stationary standard?

                                +
                                + + 
                                OPAG_r_min(r, Age_fit, Pop_fit, AgeInt_fit, Lx1, Age_Lx1)
                                + +

                                Arguments

                                + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                r

                                given stable growth rate

                                Age_fit

                                integer vector of lower bounds for age groups of Pop_fit

                                Pop_fit

                                numeric vector of at least two population counts to use for fitting

                                AgeInt_fit

                                integer vector of widths of age groups of Pop_fit

                                Lx1

                                numeric vector of stable population standard by single ages

                                Age_Lx1

                                integer vector of lower bounds for age groups of Lx1

                                + +

                                Value

                                + +

                                numeric. A residual that you're presumably trying to minimize.

                                +

                                Details

                                + +

                                This is a utility function for OPAG(), which needs to optimize $r$ for a +given population vector and stationary standard.

                                + +

                                Examples

                                + 
                                # Make up some population data to fit to:
+Pop_fit    <- c(85000,37000)
+Age_fit    <- c(70,80)
+AgeInt_fit <- c(10,10)
+nLx        <- downloadnLx(NULL, "Spain","female",1971)
+
                                #> Downloading nLx data for Spain, years 1971, gender female
                                # graduate(nLx, Age_nLx, method = method, constrain = TRUE)
+Ageab    <- names2age(nLx)
+Lx1 <- graduate(c(nLx), Ageab, method = "mono", constrain = TRUE)
+Age_Lx1 <- 0:100
+r          <- .01
+
+OPAG_r_min(r,
+           Pop_fit = Pop_fit,
+           Age_fit = Age_fit,
+           AgeInt_fit = AgeInt_fit,
+           Lx1 = Lx1,
+           Age_Lx1 = Age_Lx1)
+
                                #> [1] 0.03025071
                                
+(r_opt <- optimize(OPAG_r_min,
+         Pop_fit = Pop_fit,
+         Age_fit = Age_fit,
+         AgeInt_fit = AgeInt_fit,
+         Lx1 = Lx1,
+         Age_Lx1 = Age_Lx1,
+         interval = c(-0.05,.05))$min)
+
                                #> [1] 0.002199804
                                
+
                                +
                                + +
                                + + +
                                +
                                +

                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                +
                                + +
                                +

                                Site built with pkgdown 1.6.1.

                                +
                                + +
                                +
                                + + + + + + + + diff --git a/docs/reference/OPAG_simple.html b/docs/reference/OPAG_simple.html index 9f63a1512..986258528 100644 --- a/docs/reference/OPAG_simple.html +++ b/docs/reference/OPAG_simple.html @@ -6,7 +6,7 @@ -redistripute an open age group count over higher ages proportional to an arbitrary standard — OPAG_simple • DemoTools +redistribute an open age group count over higher ages proportional to an arbitrary standard — OPAG_simple • DemoTools @@ -46,7 +46,7 @@ - + @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                                @@ -88,7 +88,7 @@
                                • - +
                                  • @@ -129,13 +129,13 @@
                                  • - +
                                  • - + github @@ -153,7 +153,7 @@
                                    -

                                    redistripute an open age group count over higher ages proportional to an arbitrary standard

                                    +

                                    redistribute an open age group count over higher ages proportional to an arbitrary standard

                                    Source: R/OPAG.R
                                    @@ -200,7 +200,7 @@

                                    R

                                    United States Census Bureau (2017). “Population Analysis System (PAS) Software.” -https://www.census.gov/data/software/pas.html.

                                    +https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.

                                    Examples

                                     Pop        <- c(38129,38382,38824,39275,39500,37304,35152,
diff --git a/docs/reference/RDM.html b/docs/reference/RDM.html
index aff2c2365..69cb19f35 100644
--- a/docs/reference/RDM.html
+++ b/docs/reference/RDM.html
@@ -83,7 +83,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
                                    
 
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diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png
new file mode 100644
index 000000000..17a358060
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diff --git a/docs/reference/age2ageN.html b/docs/reference/age2ageN.html
index 0968738bc..0af588fca 100644
--- a/docs/reference/age2ageN.html
+++ b/docs/reference/age2ageN.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
                                    @@ -88,7 +88,7 @@
                                    • - +
                                      • @@ -129,13 +129,13 @@
                                      • - +
                                      • - + github diff --git a/docs/reference/age2int.html b/docs/reference/age2int.html index 68a4b664d..be1fc959d 100644 --- a/docs/reference/age2int.html +++ b/docs/reference/age2int.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                                    @@ -88,7 +88,7 @@
                                    • - +
                                      • @@ -129,13 +129,13 @@
                                      • - +
                                      • - + github diff --git a/docs/reference/ageRatioScore.html b/docs/reference/ageRatioScore.html index e626d2daa..d9d20ba05 100644 --- a/docs/reference/ageRatioScore.html +++ b/docs/reference/ageRatioScore.html @@ -82,7 +82,7 @@ DemoTools - 01.13.20 + 01.13.76
                                @@ -90,7 +90,7 @@
                                • - +
                                  • @@ -131,13 +131,13 @@
                                  • - +
                                  • - + github @@ -213,7 +213,7 @@

                                    Details It is also assumed that the final age group is open, unless ageMax < max(Age). Setting OAG = FALSE will override this and potentially include max(Age) in calculations. The method argument determines the weighting of numerators and denominators, where the UN method puts -twice the numerator over the sum of the adjacent ages classes, Zelnich does thrice the +twice the numerator over the sum of the adjacent ages classes, Zelnik does thrice the numerator over the sum of the whole range from the next lowest to the next highest age, and Ramachandran does four times the numerator over the same sum, but with the central age double-counted in the numerator.

                                    @@ -229,7 +229,7 @@

                                    R United States Census Bureau (2017). “Population Analysis System (PAS) Software.” -https://www.census.gov/data/software/pas.html.

                                    +https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.

                                    Examples

                                    # data from PAS spreadsheet AGESEX.xlsx
diff --git a/docs/reference/ageSexAccuracy.html b/docs/reference/ageSexAccuracy.html
index ffa14c63c..3571a6a8b 100644
--- a/docs/reference/ageSexAccuracy.html
+++ b/docs/reference/ageSexAccuracy.html
@@ -82,7 +82,7 @@
       
       
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@@ -236,7 +236,7 @@ 
                                        R
 
 United States Census Bureau (2017).
 “Population Analysis System (PAS) Software.”
-https://www.census.gov/data/software/pas.html.
                                        
+https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.
                                        
 
     
                                        Examples
                                        
     
                                        Males   <- c(4677000,4135000,3825000,3647000,3247000,2802000,2409000,2212000,
diff --git a/docs/reference/ageSexAccuracyDasGupta.html b/docs/reference/ageSexAccuracyDasGupta.html
index 0d623be90..3eaaf089a 100644
--- a/docs/reference/ageSexAccuracyDasGupta.html
+++ b/docs/reference/ageSexAccuracyDasGupta.html
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diff --git a/docs/reference/age_abridge_force.html b/docs/reference/age_abridge_force.html
index acb425f6b..238646a47 100644
--- a/docs/reference/age_abridge_force.html
+++ b/docs/reference/age_abridge_force.html
@@ -47,7 +47,7 @@
 
 
 
-
+
 
 
 
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
                                @@ -88,7 +88,7 @@
                                -

                                This is a robustness utility, in place to avoid annoying hang-ups in LTAbr(). If data are given in non-standard ages, they are forced to standard abrdiged ages on the fly. Really this should happen prior to calling lt_abridged()

                                +

                                This is a robustness utility, in place to avoid annoying hang-ups in LTAbr(). If data are given in non-standard ages, they are forced to standard abridged ages on the fly. Really this should happen prior to calling lt_abridged()

                                age_abridge_force(Value, AgeInt, Age)
                                diff --git a/docs/reference/ages_asfr_five.html b/docs/reference/ages_asfr_five.html new file mode 100644 index 000000000..3b916a52b --- /dev/null +++ b/docs/reference/ages_asfr_five.html @@ -0,0 +1,204 @@ + + + + + + + + +Ages between 15 and 45 in five year age groups — ages_asfr_five • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                +
                                + + + + +
                                + +
                                +
                                +
                                +

                                Ages between 15 and 45 in five year age groups

                                + Source: R/data.R + +
                                + +
                                +

                                Ages between 15 and 45 in five year age groups for unknown +country

                                +
                                + + 
                                ages_asfr_five
                                + + +

                                Format

                                + +

                                A vector of length 7

                                +

                                Source

                                + +

                                Migration residual PAS spreadsheet

                                + +
                                + +
                                + + +
                                +
                                +

                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                +
                                + +
                                +

                                Site built with pkgdown 1.6.1.

                                +
                                + +
                                +
                                + + + + + + + + diff --git a/docs/reference/ages_asfr_single.html b/docs/reference/ages_asfr_single.html new file mode 100644 index 000000000..4f8fc2b8f --- /dev/null +++ b/docs/reference/ages_asfr_single.html @@ -0,0 +1,202 @@ + + + + + + + + +Single ages between 15 and 49 — ages_asfr_single • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                +
                                + + + + +
                                + +
                                +
                                +
                                +

                                Single ages between 15 and 49

                                + Source: R/data.R + +
                                + +
                                +

                                Single ages between 15 and 49 for Sweden

                                +
                                + + 
                                ages_asfr_single
                                + + +

                                Format

                                + +

                                A vector of length 36

                                +

                                Source

                                + +

                                Migration residual PAS spreadsheet

                                + +
                                + +
                                + + +
                                +
                                +

                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                +
                                + +
                                +

                                Site built with pkgdown 1.6.1.

                                +
                                + +
                                +
                                + + + + + + + + diff --git a/docs/reference/ages_five.html b/docs/reference/ages_five.html new file mode 100644 index 000000000..aea88783f --- /dev/null +++ b/docs/reference/ages_five.html @@ -0,0 +1,204 @@ + + + + + + + + +Ages between 0 and 100 abridged in five year age groups — ages_five • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                +
                                + + + + +
                                + +
                                +
                                +
                                +

                                Ages between 0 and 100 abridged in five year age groups

                                + Source: R/data.R + +
                                + +
                                +

                                Ages between 0 and 100 abridged in five year age groups for unknown +country

                                +
                                + + 
                                ages_five
                                + + +

                                Format

                                + +

                                A vector of length 21

                                +

                                Source

                                + +

                                Migration residual PAS spreadsheet

                                + +
                                + +
                                + + +
                                +
                                +

                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                +
                                + +
                                +

                                Site built with pkgdown 1.6.1.

                                +
                                + +
                                +
                                + + + + + + + + diff --git a/docs/reference/ages_single.html b/docs/reference/ages_single.html new file mode 100644 index 000000000..ea629c897 --- /dev/null +++ b/docs/reference/ages_single.html @@ -0,0 +1,202 @@ + + + + + + + + +Single ages between 0 and 100 — ages_single • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                +
                                + + + + +
                                + +
                                +
                                +
                                +

                                Single ages between 0 and 100

                                + Source: R/data.R + +
                                + +
                                +

                                Single ages between 0 and 100 for Sweden, 1999-2019.

                                +
                                + + 
                                ages_single
                                + + +

                                Format

                                + +

                                A vector of length 101

                                +

                                Source

                                + +

                                Migration residual PAS spreadsheet

                                + +
                                + +
                                + + +
                                +
                                +

                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                +
                                + +
                                +

                                Site built with pkgdown 1.6.1.

                                +
                                + +
                                +
                                + + + + + + + + diff --git a/docs/reference/agesmth1.html b/docs/reference/agesmth1.html index 4f9ba9b10..69700e986 100644 --- a/docs/reference/agesmth1.html +++ b/docs/reference/agesmth1.html @@ -83,7 +83,7 @@ DemoTools - 01.13.20 + 01.13.76
                                @@ -91,7 +91,7 @@
                                • - +
                                  • @@ -132,13 +132,13 @@
                                  • - +
                                  • - + github @@ -221,7 +221,7 @@

                                    Details

                                    LOESS (locally weighted smoothing) helps to smooth data over age, preserving the open age group if necessary. It is a popular tool to create a smooth line through a timeplot or scatter plot. Loess smoothness may be tweaked by specifying an optional "span" argument. -Polynomial fitting is used to mooth data over age or time fitting linear models. +Polynomial fitting is used to smooth data over age or time fitting linear models. It can be tweaked by changing the degree and by either log or power transforming. The open age group can be kept as-is if desired by specifying OAG = TRUE. May be used on any age groups, including irregularly spaced, single age, or 5-year age groups. diff --git a/docs/reference/asfr_mat_five.html b/docs/reference/asfr_mat_five.html new file mode 100644 index 000000000..f02703456 --- /dev/null +++ b/docs/reference/asfr_mat_five.html @@ -0,0 +1,204 @@ + + + + + + + + +Age-specific fertility rates for age groups 15 to 45 between 1950 and 2045 — asfr_mat_five • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

                                    +
                                    + + + + +
                                    + +
                                    +
                                    +
                                    +

                                    Age-specific fertility rates for age groups 15 to 45 between 1950 and 2045

                                    + Source: R/data.R + +
                                    + +
                                    +

                                    Age-specific fertility rates for age groups 15 to 45 between 1950 and 2045 +for unknown country

                                    +
                                    + + 
                                    asfr_mat_five
                                    + + +

                                    Format

                                    + +

                                    A matrix of dimensions 7 x 20

                                    +

                                    Source

                                    + +

                                    Migration residual PAS spreadsheet

                                    + +
                                    + +
                                    + + +
                                    +
                                    +

                                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                    +
                                    + +
                                    +

                                    Site built with pkgdown 1.6.1.

                                    +
                                    + +
                                    +
                                    + + + + + + + + diff --git a/docs/reference/asfr_mat_single.html b/docs/reference/asfr_mat_single.html new file mode 100644 index 000000000..38df32f9f --- /dev/null +++ b/docs/reference/asfr_mat_single.html @@ -0,0 +1,204 @@ + + + + + + + + +Age-specific fertility rates for single ages 15 to 49 between 1999 and 2018 — asfr_mat_single • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                    +
                                    + + + + +
                                    + +
                                    +
                                    +
                                    +

                                    Age-specific fertility rates for single ages 15 to 49 between 1999 and 2018

                                    + Source: R/data.R + +
                                    + +
                                    +

                                    Age-specific fertility rates for single ages 15 to 49 between 1999 and 2018 +for Sweden

                                    +
                                    + + 
                                    asfr_mat_single
                                    + + +

                                    Format

                                    + +

                                    A matrix of dimensions 35 x 20

                                    +

                                    Source

                                    + +

                                    Migration residual PAS spreadsheet

                                    + +
                                    + +
                                    + + +
                                    +
                                    +

                                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                    +
                                    + +
                                    +

                                    Site built with pkgdown 1.6.1.

                                    +
                                    + +
                                    +
                                    + + + + + + + + diff --git a/docs/reference/avg_adj.html b/docs/reference/avg_adj.html index 763602f9e..c755ce6e5 100644 --- a/docs/reference/avg_adj.html +++ b/docs/reference/avg_adj.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76

                                @@ -88,7 +88,7 @@
                                • - +
                                  • @@ -129,13 +129,13 @@
                                  • - +
                                  • - + github diff --git a/docs/reference/basepop_five.html b/docs/reference/basepop_five.html new file mode 100644 index 000000000..22c740b9f --- /dev/null +++ b/docs/reference/basepop_five.html @@ -0,0 +1,754 @@ + + + + + + + + +BPA and BPE methods for adjusting age groups under 10 — basepop_five • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                    +
                                    + + + + +
                                    + +
                                    +
                                    +
                                    +

                                    BPA and BPE methods for adjusting age groups under 10

                                    + Source: R/basepop.R + +
                                    + +
                                    +

                                    Adjust population counts for the age groups 0 to 10

                                    +
                                    + + 
                                    basepop_five(
+  location = NULL,
+  refDate,
+  Age = NULL,
+  Females_five,
+  Males_five = NULL,
+  nLxFemale = NULL,
+  nLxMale = NULL,
+  nLxDatesIn = NULL,
+  AsfrMat = NULL,
+  AsfrDatesIn = NULL,
+  ...,
+  SRB = NULL,
+  SRBDatesIn = NULL,
+  radix = NULL,
+  verbose = TRUE
+)
                                    + +

                                    Arguments

                                    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                    location

                                    UN Pop Division LocName or LocID

                                    refDate

                                    The reference year for which the reported population pertain +(these are the population counts in Females_five and +Males_five). Can either be a decimal date, a Date class. +If nLxDatesIn or AsfrDatesIn are not supplied and the +corresponding nLxFemale/Male/AsfrMat is not supplied, +refDate must be at a minimum 1962.5. This is because we can only +fetch WPP data from 1955 onwards, and these minimum date is assumed to be +7.5 years before refDate, meaning 1955.

                                    Age

                                    integer vector of lower bounds of abridged age groups given in Females_five and Males_five.

                                    Females_five

                                    A named numeric vector with the population counts for +five-year abridged age groups for females in refDate. The names of the +vector should reflect the age groups. See the example section for some +examples.

                                    Males_five

                                    A named numeric vector with the population counts for +five-year abridged age groups for males in refDate. The names of +the vector should reflect the age groups. See the example section for +some examples.

                                    nLxFemale

                                    A numeric matrix. The female nLx function of two abridged life tables +with ages in the rows and time in columns. The earlier date should be at least +7.5 years before the reference date of the "reported" population. The later +date should be no earlier than one-half year before the reference date of +the "reported" population. If not provided, it's automatically downloaded if +location, refDate and the equivalent population counts +*_five are provided.

                                    nLxMale

                                    A numeric matrix. The male nLx function of two abridged life tables +with ages in the rows and time in columns. The dates which are represented +in the columns are assumed to be the same as nLxDatesIn. This +argument is only used when female is set to FALSE and +Males_five is provided. If Males_five is provided and +female set to FALSE, the nLx for males is +automatically downloaded for the dates in nLxDatesIn.

                                    nLxDatesIn

                                    A vector of numeric years (for example, 1986). The dates +which pertain to the columns in nLxFemale and nLxMale. If not +provided, the function automatically determines two dates which are 8 years +before refDate and 0.5 years after refDate.

                                    AsfrMat

                                    A numeric matrix. An age-period matrix of age specific +fertility rates with age in rows, time in columns. If not provided, the +function automatically downloads the ASFR matrix based on the dates in +AsfrDatesIn.

                                    AsfrDatesIn

                                    A vector of numeric years (for example, 1986). These are +the dates which pertain to the columns in AsfrMat. If not provided, +the function automatically determines two dates which are 8 years before +refDate and 0.5 before refDate.

                                    ...

                                    Arguments passed to \link{interp}. In particular, users +might be interested in changing the interpolation method for the nLx* +matrices and the Asfr matrix. By default, it's linearly interpolated.

                                    SRB

                                    A numeric. Sex ratio at birth (males / females). Default is set +to 1.046. Only a maximum of three values permitted.

                                    SRBDatesIn

                                    A vector of numeric years (for example, 1986). Only a maximum +number of three dates allowed. These are +the dates which pertain to the values in SRB. If not provided, +the function automatically determines three dates which are 7.5 years, +2.5 and 0.5 years before refDate.

                                    radix

                                    starting point to use in the adjustment of the three first age +groups. Default is NULL. If not provided, it is inferred based on the scale of age 1L0.

                                    verbose

                                    when downloading new data, should the function print details +about the download at each step? Defaults to TRUE. We recommend the +user to set this to TRUE at all times because the function needs to +make decisions (such as picking the dates for the Asfr and nLx) that the user +should be aware of.

                                    + +

                                    Value

                                    + +

                                    basepop_five returns a list with the following elements: +*

                                      +

                                    • Females_adjusted numeric vector of adjusted population counts for females. Age groups 0, 1-4, and 5-9 are adjusted, while ages 10 and higher are unchanged.

                                      • +

                                    • Males_adjusted numeric vector of adjusted population counts for males. Age groups 0, 1-4, and 5-9 are adjusted, while ages 10 and higher are unchanged.

                                      • +

                                    • Females_five numeric vector of female population counts given as input.

                                      • +

                                    • Males_five numeric vector of male population counts given as input.

                                      • +

                                    • nLxf numeric matrix of female nLx, abridged ages in rows and (potentially interpolated) time in columns. Potentially downloaded.

                                      • +

                                    • nLxm numeric matrix of male nLx, abridged ages in rows and (potentially interpolated) time in columns. Potentially downloaded.

                                      • +

                                    • Asfr numeric matrix of age specific fertility in 5-year age groups ages 15-19 until 45-49 in rows, and (potentially interpolated) time in columns. Potentially downloaded.

                                      • +

                                    • Exposure_female numeric matrix of approximated age-specific exposure in 5-year age groups ages 15-19 until 45-49 in rows, and (potentially interpolated) time in columns.

                                      • +

                                    • Bt births at three time points prior to census corresponding to the midpoints of the cohorts entering ages 0, 1-4, and 5-9.

                                      • +

                                    • SRB sex ratio at birth at three time points prior to census corresponding to the midpoints of the cohorts entering ages 0, 1-4, and 5-9. Potentially downloaded.

                                      • +

                                    • Age age groups of the input population counts.

                                      • +
                                    + +

                                    Details

                                    + +

                                    basepop_five and basepop_single can estimate both the BPA and +BPE methods. If the user specifies SmoothedFemales, both +basepop_* functions will return the BPA method. +If SmoothedFemales is left empty, both basepop_* functions will +adjust using the BPE method.

                                    +

                                    For basepop_five, adjusting the female population counts is the +default. For this, only the location, refDate and +Females_five are needed. All other arguments are downloaded +or set to sensible defaults. For adjusting the male population +counts, the user needs to specify the Males_five population +counts and set female = FALSE.

                                    +

                                    Currently, basepop_five works only with five year abridged age groups

                                    +

                                    The BPE method is used by default. To adjust the counts using +the BPA method, the user needs to provide the SmoothedFemales +argument. This is the female population counts passed through +a smoothing function such as smooth_age_5. See the examples +section for some examples.

                                    +

                                    BPA

                                    + + + + +

                                    Description:

                                    +

                                    The method estimates a smoothed population ages 10 and over and adjusts +the population under age 10 using the smoothed population and estimates +of fertility and mortality.

                                    +

                                    Based on the smoothed female population counts, it rejuvenates the female +"reported" population 20 to 59 years of age for the two 5 year periods prior +to the census date to represent the female population in reproductive ages +5 and 10 years earlier. Based on the rejuvenated population and fertility +and mortality levels, the method then estimates the male and female births +during the two 5 year periods prior to the census date. Next, it projects +the two 5-year birth cohorts to the census date. The projected figures +represent the adjusted population ages 0 to 4 years and 5 to 9 years +at the census date.

                                    +

                                    Advantages:

                                    +

                                    (1) The method adjusts under-10 population to be consistent with fertility +and mortality levels and adjusted adult female population.

                                    +

                                    Limitations:

                                    +

                                    (1) BPA assumes a linear change in fertility and mortality during the decade +prior to the reference year.

                                    +

                                    (2) The procedure ignores migration, which can lead to misleading results. +There are two issues. First, age groups 0-4 and 5-9 are subject to migration, +which would affect the comparability of estimated and reported populations +in the base year. Second, the estimated size of age groups 0-4 and 5-9 are +calculated from numbers of women of reproductive age in the base year +rejuvenated to points in the past. With migration, rejuvenated number of +women may exceed or be smaller than the number present, and giving +birth to children, in the decade prior to the base year.

                                    +

                                    (3) BPA’s smoothing calculations may mask unusual, but real, variations +in population age group size. Smoothing irregularities in age structure +not attributable to age misreporting will distort estimated births and +survived children in the base year.

                                    +

                                    Assumptions:

                                    +

                                    (1) No significant international migration took place within the +reference periods for the population, mortality, and fertility input.

                                    +

                                    (2) The data input as the "reported" population is not affected by +underenumeration of persons in certain ages, nor by age misreporting.

                                    +

                                    BPE

                                    + + + + +

                                    Description:

                                    +

                                    The method adjusts the population under age 10 using the reported population +ages 10 and above and estimates of fertility and mortality.

                                    +

                                    The method rejuvenates the reported female population 20 to 59 years of age +for the two 5 year periods prior to the census date to represent the female +population in reproductive ages 5 and 10 years earlier. Based on the +rejuvenated population and fertility and mortality levels, the method then +estimates the male and female births during the two 5 year periods prior to +the census date. Next, it projects the two 5-year birth cohorts to the +census date. The projected figures represent the adjusted population ages +0 to 4 years and 5 to 9 years at the census date.

                                    +

                                    Advantages:

                                    +

                                    (1) The method adjusts the under-10 population to be consistent with +fertility and mortality levels and adult female population.

                                    +

                                    Limitations:

                                    +

                                    (1) BPE assumes a linear change in fertility and mortality during the decade +prior to the reference year.

                                    +

                                    (2) The procedure ignores migration, which can lead to misleading results. +There are two issues. First, age groups 0-4 and 5-9 are subject to +migration, which would affect the comparability of estimated and reported +populations in the base year. Second, the estimated size of age groups +0-4 and 5-9 are calculated from numbers of women of reproductive age in +the base year rejuvenated to points in the past. With migration, rejuvenated +number of women may exceed or be smaller than the number present, and +giving birth to children, in the decade prior to the base year.

                                    +

                                    (3) The method does not adjust for possible underenumeration and age +misreporting errors in the over-10 “reported” population. If the +reported population is subject to age-misreporting or age-sex-specific +underenumeration, the over-10 population should be smoothed or otherwise +corrected prior to use.

                                    +

                                    Assumptions:

                                    +

                                    (1) No significant international migration took place within the reference +periods for the population, mortality, and fertility input.

                                    +

                                    (2) The data input as the “reported” population is not affected by +underenumeration of persons in certain ages, nor by age misreporting.

                                    +

                                    References

                                    + +

                                    Arriaga EE, Johnson PD, Jamison E (1994). +Population analysis with microcomputers, volume 1. +Bureau of the Census. + +United States Census Bureau (2017). +“Population Analysis System (PAS) Software.” +https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.

                                    + +

                                    Examples

                                    + 
                                    
+ if (FALSE) {
+
+################ BPE (five year age groups) #####################
+
+# Grab population counts for females
+refDate <- 1986
+location <- "Brazil"
+pop_female_single <- fertestr::FetchPopWpp2019(location,
+                                               refDate,
+                                               ages = 0:100,
+                                               sex = "female")
+pop_female_counts <- single2abridged(setNames(pop_female_single$pop,
+                                              pop_female_single$ages))
+pop_male_single   <- fertestr::FetchPopWpp2019(location,
+                                               refDate,
+                                               ages = 0:100,
+                                               sex = "male")
+pop_male_counts   <- single2abridged(setNames(pop_male_single$pop,
+                                              pop_male_single$ages))
+Age <- names2age(pop_male_counts)
+# Automatically downloads the nLx, ASFR, and SRB data
+bpe <- basepop_five(
+  location = location,
+  refDate = refDate,
+  Females_five = pop_female_counts,
+  Males_five = pop_male_counts,
+  Age = Age
+)
+
+# The counts for the first three age groups have been adjusted:
+bpe$Females_adjusted[1:3]
+pop_female_counts[1:3]
+
+bpe$Males_adjusted[1:3]
+pop_male_counts[1:3]
+
+
+################ BPE (for single ages) ############################
+# blocked out for now, until single age function refactored as
+# TR: actually, it just needs to be rethought for single ages..
+# pop_female_single <- setNames(pop_female_single$pop, pop_female_single$ages)
+#
+# # Automatically downloads the nLx and ASFR data
+# bpe_female <- basepop_single(
+#   location = location,
+#   refDate = refDate,
+#   Females_single = pop_female_single
+# )
+#
+# # The counts for the first 10 age groups have been adjusted:
+# bpe_female[1:10]
+# pop_female_single[1:10]
+################ BPA (five year age groups) #####################
+# for BPA, smooth counts in advance
+smoothed_females <- smooth_age_5(Value = pop_female_counts,
+                                 Age = Age,
+                                 method = "Arriaga",
+                                 OAG = TRUE,
+                                 young.tail = "Original")
+# Note, smooth_age_5() will group infants into the 0-4 age group. So,
+# we manually stick them back in place.
+smoothed_females <- c(pop_female_counts[1:2], smoothed_females[-1])
+smoothed_males <- smooth_age_5(Value = pop_male_counts,
+                               Age = Age,
+                               method = "Arriaga",
+                               OAG = TRUE,
+                               young.tail = "Original")
+smoothed_males <- c(smoothed_males[1:2], smoothed_males[-1])
+
+# Automatically downloads the nLx, ASFR, and SRB data
+bpa <- basepop_five(
+  location = location,
+  refDate = refDate,
+  Females_five = smoothed_females,
+  Males_five = smoothed_males,
+  Age = Age
+)
+
+# The counts for the first three age groups have been adjusted:
+bpa$Females_adjusted[1:3]
+smoothed_females[1:3]
+pop_female_counts[1:3]
+
+bpa$Males_adjusted[1:3]
+smoothed_males[1:3]
+pop_male_counts[1:3]
+
+################ PAS example ###############################
+
+ # (1) refDate
+ refDate <- 1986.21
+
+ # (2) Reported population by 5-year age groups and sex in the base year
+ # (Include unknowns).
+
+ pop_male_counts <- c(11684, 46738, 55639, 37514, 29398, 27187, 27770, 20920, 16973,
+                      14999, 11330, 10415, 6164, 7330, 3882, 3882, 1840, 4200)
+
+ pop_female_counts <- c(11673, 46693, 55812, 35268, 33672, 31352, 33038, 24029, 16120,
+                        14679, 8831, 9289, 4172, 6174, 2715, 3344, 1455, 4143)
+ Age <- c(0,1, seq(5, 80, by = 5))
+
+ # (4) Sex ratio at birth (m/f)
+ sex_ratio <- 1.0300
+
+ # (6) The male and female nLx functions for ages under 1 year, 1 to 4 years, and 5 to 9
+ # years, pertaining to an earlier and later date
+ nLxDatesIn <- c(1977.31, 1986.50)
+
+ nLxMale <- matrix(c(87732, 304435, 361064, 88451, 310605, 370362),
+                   nrow = 3, ncol = 2)
+
+ nLxFemale <- matrix(c(89842, 314521, 372681, 353053, 340650, 326588,
+                       311481, 295396, 278646, 261260, 241395,217419,
+                       90478, 320755, 382531, 364776, 353538, 340687,
+                       326701, 311573, 295501, 278494, 258748,234587),
+                     nrow = 12,
+                     ncol = 2)
+
+ # (7) A set of age-specific fertility rates pertaining to an earlier and later
+ # date
+
+ asfrmat <- structure(
+    c(0.2, 0.3, 0.3, 0.25, 0.2, 0.15, 0.05, 0.15, 0.2,
+      0.275, 0.225, 0.175, 0.125, 0.05), .Dim = c(7L, 2L),
+    .Dimnames = list(
+      c("15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49"),
+      c("1977.81", "1985.71")))
+
+ # for BPA, smooth counts in advance
+ smoothed_females <- smooth_age_5(Value = pop_female_counts,
+                                  Age = Age,
+                                  method = "Arriaga",
+                                  OAG = TRUE,
+                                  young.tail = "Original")
+ smoothed_females <- c(pop_female_counts[1:2], smoothed_females[-1])
+ smoothed_males <- smooth_age_5(Value = pop_male_counts,
+                                  Age = Age,
+                                  method = "Arriaga",
+                                  OAG = TRUE,
+                                  young.tail = "Original")
+ smoothed_males <- c(pop_male_counts[1:2], smoothed_males[-1])
+ ## This is the only number that messes up the whole calculation.
+ ## smooth_age_5 returns the same result as the PASS excel sheet
+ ## except for the age groups 10-15 and 15-19. Here we only use
+ ## age group 15-19. If we plug in manually the correct value,
+ ## we get all results match exactly, otherwise there are
+ ## some differences.
+ smoothed_females[4] <- 34721
+
+ # For adjusting using BPA for males, we need to specify
+ # female = FALSE with Males and nLxMale.
+ bpa <-
+   basepop_five(
+     refDate = refDate,
+     Males_five = smoothed_males,
+     Females_five = smoothed_females,
+     Age = Age,
+     SRB = sex_ratio,
+     nLxFemale = nLxFemale,
+     nLxMale = nLxMale,
+     nLxDatesIn = nLxDatesIn,
+     AsfrMat = asfrmat,
+     AsfrDatesIn = AsfrDatesIn,
+     radix = 1e5
+   )
+
+ # See adjustments?
+ pop_male_counts[1:3]
+ bpa$Male_adjusted[1:3]
+
+ pop_female_counts[1:3]
+ bpa$Female_adjusted[1:3]
+
+ # For adjustment using BPE, we use exactly the same definitions as above
+ # but use the original inputs
+
+ bpe <-
+   basepop_five(
+     refDate = refDate,
+     Females_five = pop_female_counts,
+     Males_five = pop_male_counts,
+     SRB = sex_ratio,
+     nLxFemale = nLxFemale,
+     nLxDatesIn = nLxDatesIn,
+     AsfrMat = asfrmat,
+     AsfrDatesIn = AsfrDatesIn
+   )
+
+ pop_female_counts[1:3]
+ bpe$Females_adjusted[1:3]
+
+# basepop_single for single ages
+# Single ages for males and females
+
+# pop_male_counts <-
+#   c(11684, 11473, 11647, 11939, 11680, 10600, 11100, 11157, 11238,
+#     11544, 7216, 7407, 7461, 7656, 7774, 5709, 5629, 5745, 6056,
+#     6259, 5303, 5423, 5497, 5547, 5417, 5441, 5466, 5500, 5668, 5694,
+#     4365, 4252, 4122, 4142, 4039, 3210, 3222, 3258, 3413, 3871, 2684,
+#     2844, 3052, 3182, 3237, 2263, 2298, 2318, 2257, 2194, 2231, 2172,
+#     2072, 2008, 1932, 1301, 1262, 1213, 1197, 1191, 1601, 1593, 1490,
+#     1348, 1299, 568, 745, 843, 801, 925, 806, 883, 796, 725, 672,
+#     470, 441, 340, 300, 289, 4200)
+#
+# pop_female_counts <-
+#   c(11673, 11474, 11670, 11934, 11614, 10603, 11144, 11179, 11269,
+#     11617, 6772, 6948, 7030, 7211, 7306, 6531, 6443, 6535, 6951,
+#     7213, 6096, 6234, 6327, 6410, 6285, 6464, 6492, 6549, 6739, 6795,
+#     5013, 4888, 4735, 4747, 4646, 3040, 3068, 3107, 3246, 3658, 2650,
+#     2788, 2977, 3108, 3156, 1756, 1784, 1802, 1764, 1724, 1982, 1935,
+#     1846, 1795, 1731, 863, 850, 825, 819, 816, 1348, 1342, 1246,
+#     1138, 1101, 391, 520, 585, 560, 659, 670, 750, 686, 634, 604,
+#     353, 340, 270, 246, 247, 4143)
+#  Age <- 0:80
+#
+#  smoothed_females <- smooth_age_5(Value = pop_female_counts,
+#                                   Age = Age,
+#                                   method = "Arriaga",
+#                                   OAG = TRUE,
+#                                   young.tail = "Original")
+#  smoothed_males <- smooth_age_5(Value = pop_male_counts,
+#                                   Age = Age,
+#                                   method = "Arriaga",
+#                                   OAG = TRUE,
+#                                   young.tail = "Original")
+
+ # For adjusting using BPA for males, we need to specify
+ # female = FALSE with Males and nLxMale.
+
+ # This needs work still
+ # bpa_male <-
+ #   basepop_single(
+ #     refDate = refDate,
+ #     Males_single = pop_male_counts,
+ #     Females_single = pop_female_counts,
+ #     SRB = sex_ratio,
+ #     nLxFemale = nLxFemale,
+ #     nLxMale = nLxMale,
+ #     nLxDatesIn = nLxDatesIn,
+ #     AsfrMat = asfrmat,
+ #     AsfrDatesIn = AsfrDatesIn
+ #     )
+
+ # See adjustments?
+ # pop_male_counts[1:10]
+ # bpa_male[1:10]
+
+ # Adjusting the BPA for females requires less arguments
+ # bpa_female <-
+ #   basepop_single(
+ #     refDate = refDate,
+ #     Females_single = pop_female_counts,
+ #     SmoothedFemales = smoothed_females,
+ #     SRB = sex_ratio,
+ #     nLxFemale = nLxFemale,
+ #     nLxDatesIn = nLxDatesIn,
+ #     AsfrMat = asfrmat,
+ #     AsfrDatesIn = AsfrDatesIn
+ #   )
+
+ # pop_female_counts[1:10]
+ # bpa_female[1:10]
+ #
+ # # For adjustment using BPE, we use exactly the same definitions as above
+ # # but remove SmoothedFemales.
+ # bpe_male <-
+ #   basepop_single(
+ #     refDate = refDate,
+ #     Males_single = pop_male_counts,
+ #     Females_single = pop_female_counts,
+ #     SRB = sex_ratio,
+ #     nLxFemale = nLxFemale,
+ #     nLxMale = nLxMale,
+ #     nLxDatesIn = nLxDatesIn,
+ #     AsfrMat = asfrmat,
+ #     AsfrDatesIn = AsfrDatesIn,
+ #     female = FALSE
+ #     )
+
+ # See adjustments?
+ # pop_male_counts[1:10]
+ # bpa_male[1:10]
+ # bpe_male[1:10]
+
+ # Adjusting the BPA for females requires less arguments
+ # bpe_female <-
+ #   basepop_single(
+ #     refDate = refDate,
+ #     Females_single = pop_female_counts,
+ #     SRB = sex_ratio,
+ #     nLxFemale = nLxFemale,
+ #     nLxDatesIn = nLxDatesIn,
+ #     AsfrMat = asfrmat,
+ #     AsfrDatesIn = AsfrDatesIn
+ #   )
+ #
+ # pop_female_counts[1:10]
+ # bpa_female[1:10]
+ # bpe_female[1:10]
+
+ }
+
+
                                    +
                                    + +
                                    + + +
                                    +
                                    +

                                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                    +
                                    + +
                                    +

                                    Site built with pkgdown 1.6.1.

                                    +
                                    + +
                                    +
                                    + + + + + + + + diff --git a/docs/reference/birthCohorts.html b/docs/reference/birthCohorts.html index a2b984ec1..1a98d5c51 100644 --- a/docs/reference/birthCohorts.html +++ b/docs/reference/birthCohorts.html @@ -82,7 +82,7 @@ DemoTools - 01.13.20 + 01.13.76
                                @@ -90,7 +90,7 @@
                                • - +
                                  • @@ -131,13 +131,13 @@
                                  • - +
                                  • - + github @@ -205,7 +205,7 @@

                                    R

                                    United States Census Bureau (2017). “Population Analysis System (PAS) Software.” -https://www.census.gov/data/software/pas.html.

                                    +https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.

                                    Examples

                                    Males      <- c(18052, 16523, 15555, 12063, 9654, 9256, 10058, 10035, 9785, 9025,
diff --git a/docs/reference/calcAgeAbr.html b/docs/reference/calcAgeAbr.html
index a40efd10e..d2eb9cf7e 100644
--- a/docs/reference/calcAgeAbr.html
+++ b/docs/reference/calcAgeAbr.html
@@ -81,7 +81,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
                                    
 
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                                    • 
   
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+    
      
   
 
                                      • 
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                                        • 
 
                                      • 
   
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diff --git a/docs/reference/calcAgeN.html b/docs/reference/calcAgeN.html
index eeab539fb..1438828e9 100644
--- a/docs/reference/calcAgeN.html
+++ b/docs/reference/calcAgeN.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
                                @@ -88,7 +88,7 @@
                                • - +
                                  • @@ -129,13 +129,13 @@
                                  • - +
                                  • - + github @@ -187,7 +187,7 @@

                                    Value

                                    Details

                                    If you shift the groupings, then the first age groups may have a negative lower bound -(for example of -5). These counts would be discarded for the osculatory version of Sprague smoothing, +(for example of -5). These counts would be discarded for the oscillatory version of Sprague smoothing, for example, but they are preserved in this function. The important thing to know is that if you shift the groups, the first and last groups won't be N years wide. For example if shiftdown is 1, the first age group is 4-ages wide.

                                    diff --git a/docs/reference/census_cohort_adjust.html b/docs/reference/census_cohort_adjust.html new file mode 100644 index 000000000..72703885f --- /dev/null +++ b/docs/reference/census_cohort_adjust.html @@ -0,0 +1,338 @@ + + + + + + + + +shift census populations to match single year cohorts — census_cohort_adjust • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                    +
                                    + + + + +
                                    + +
                                    +
                                    +
                                    +

                                    shift census populations to match single year cohorts

                                    + Source: R/interp_coh.R + +
                                    + +
                                    +

                                    Matches the (single) ages of a census to single cohorts. For use in intercensal interpolations. Ages are potentially blended to match single cohort line assuming that the population in each age is uniformly distributed over the age group.

                                    +
                                    + + 
                                    census_cohort_adjust(pop, age, date)
                                    + +

                                    Arguments

                                    + + + + + + + + + + + + + + +
                                    pop

                                    numeric vector. Population counts in age groups, presumably from a census with an exact reference date.

                                    age

                                    integer vector. Lower bound of single age groups

                                    date

                                    Either a Date class object or an unambiguous character string in the format "YYYY-MM-DD".

                                    + + +

                                    Examples

                                    + 
                                    pop <- seq(10000,100,length.out = 101)
+age <- 0:100
+d1 <- "2020-01-01"
+d2 <- "2020-07-01"
+d3 <- "2020-12-21"
+
+census_cohort_adjust(pop, age, d1)
+
                                    #> $pop
+#>   [1] 10000  9901  9802  9703  9604  9505  9406  9307  9208  9109  9010  8911
+#>  [13]  8812  8713  8614  8515  8416  8317  8218  8119  8020  7921  7822  7723
+#>  [25]  7624  7525  7426  7327  7228  7129  7030  6931  6832  6733  6634  6535
+#>  [37]  6436  6337  6238  6139  6040  5941  5842  5743  5644  5545  5446  5347
+#>  [49]  5248  5149  5050  4951  4852  4753  4654  4555  4456  4357  4258  4159
+#>  [61]  4060  3961  3862  3763  3664  3565  3466  3367  3268  3169  3070  2971
+#>  [73]  2872  2773  2674  2575  2476  2377  2278  2179  2080  1981  1882  1783
+#>  [85]  1684  1585  1486  1387  1288  1189  1090   991   892   793   694   595
+#>  [97]   496   397   298   199   100
+#> 
+#> $cohort
+#>   [1] 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005
+#>  [16] 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990
+#>  [31] 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976 1975
+#>  [46] 1974 1973 1972 1971 1970 1969 1968 1967 1966 1965 1964 1963 1962 1961 1960
+#>  [61] 1959 1958 1957 1956 1955 1954 1953 1952 1951 1950 1949 1948 1947 1946 1945
+#>  [76] 1944 1943 1942 1941 1940 1939 1938 1937 1936 1935 1934 1933 1932 1931 1930
+#>  [91] 1929 1928 1927 1926 1925 1924 1923 1922 1921 1920 1919
+#> 
+#> $date
+#> [1] 2020
+#> 
+#> $f1
+#> [1] 0
+#> 
                                    census_cohort_adjust(pop, age, d2)
+
                                    #> $pop
+#>   [1] 4972.6776 9950.7705 9851.7705 9752.7705 9653.7705 9554.7705 9455.7705
+#>   [8] 9356.7705 9257.7705 9158.7705 9059.7705 8960.7705 8861.7705 8762.7705
+#>  [15] 8663.7705 8564.7705 8465.7705 8366.7705 8267.7705 8168.7705 8069.7705
+#>  [22] 7970.7705 7871.7705 7772.7705 7673.7705 7574.7705 7475.7705 7376.7705
+#>  [29] 7277.7705 7178.7705 7079.7705 6980.7705 6881.7705 6782.7705 6683.7705
+#>  [36] 6584.7705 6485.7705 6386.7705 6287.7705 6188.7705 6089.7705 5990.7705
+#>  [43] 5891.7705 5792.7705 5693.7705 5594.7705 5495.7705 5396.7705 5297.7705
+#>  [50] 5198.7705 5099.7705 5000.7705 4901.7705 4802.7705 4703.7705 4604.7705
+#>  [57] 4505.7705 4406.7705 4307.7705 4208.7705 4109.7705 4010.7705 3911.7705
+#>  [64] 3812.7705 3713.7705 3614.7705 3515.7705 3416.7705 3317.7705 3218.7705
+#>  [71] 3119.7705 3020.7705 2921.7705 2822.7705 2723.7705 2624.7705 2525.7705
+#>  [78] 2426.7705 2327.7705 2228.7705 2129.7705 2030.7705 1931.7705 1832.7705
+#>  [85] 1733.7705 1634.7705 1535.7705 1436.7705 1337.7705 1238.7705 1139.7705
+#>  [92] 1040.7705  941.7705  842.7705  743.7705  644.7705  545.7705  446.7705
+#>  [99]  347.7705  248.7705  149.7705
+#> 
+#> $cohort
+#>   [1] 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006
+#>  [16] 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991
+#>  [31] 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976
+#>  [46] 1975 1974 1973 1972 1971 1970 1969 1968 1967 1966 1965 1964 1963 1962 1961
+#>  [61] 1960 1959 1958 1957 1956 1955 1954 1953 1952 1951 1950 1949 1948 1947 1946
+#>  [76] 1945 1944 1943 1942 1941 1940 1939 1938 1937 1936 1935 1934 1933 1932 1931
+#>  [91] 1930 1929 1928 1927 1926 1925 1924 1923 1922 1921 1920
+#> 
+#> $date
+#> [1] 2020.497
+#> 
+#> $f1
+#> [1] 0.4972678
+#> 
                                    census_cohort_adjust(pop, age, d3)
+
                                    #> $pop
+#>   [1] 9699.4536 9903.9754 9804.9754 9705.9754 9606.9754 9507.9754 9408.9754
+#>   [8] 9309.9754 9210.9754 9111.9754 9012.9754 8913.9754 8814.9754 8715.9754
+#>  [15] 8616.9754 8517.9754 8418.9754 8319.9754 8220.9754 8121.9754 8022.9754
+#>  [22] 7923.9754 7824.9754 7725.9754 7626.9754 7527.9754 7428.9754 7329.9754
+#>  [29] 7230.9754 7131.9754 7032.9754 6933.9754 6834.9754 6735.9754 6636.9754
+#>  [36] 6537.9754 6438.9754 6339.9754 6240.9754 6141.9754 6042.9754 5943.9754
+#>  [43] 5844.9754 5745.9754 5646.9754 5547.9754 5448.9754 5349.9754 5250.9754
+#>  [50] 5151.9754 5052.9754 4953.9754 4854.9754 4755.9754 4656.9754 4557.9754
+#>  [57] 4458.9754 4359.9754 4260.9754 4161.9754 4062.9754 3963.9754 3864.9754
+#>  [64] 3765.9754 3666.9754 3567.9754 3468.9754 3369.9754 3270.9754 3171.9754
+#>  [71] 3072.9754 2973.9754 2874.9754 2775.9754 2676.9754 2577.9754 2478.9754
+#>  [78] 2379.9754 2280.9754 2181.9754 2082.9754 1983.9754 1884.9754 1785.9754
+#>  [85] 1686.9754 1587.9754 1488.9754 1389.9754 1290.9754 1191.9754 1092.9754
+#>  [92]  993.9754  894.9754  795.9754  696.9754  597.9754  498.9754  399.9754
+#>  [99]  300.9754  201.9754  102.9754
+#> 
+#> $cohort
+#>   [1] 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006
+#>  [16] 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991
+#>  [31] 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976
+#>  [46] 1975 1974 1973 1972 1971 1970 1969 1968 1967 1966 1965 1964 1963 1962 1961
+#>  [61] 1960 1959 1958 1957 1956 1955 1954 1953 1952 1951 1950 1949 1948 1947 1946
+#>  [76] 1945 1944 1943 1942 1941 1940 1939 1938 1937 1936 1935 1934 1933 1932 1931
+#>  [91] 1930 1929 1928 1927 1926 1925 1924 1923 1922 1921 1920
+#> 
+#> $date
+#> [1] 2020.97
+#> 
+#> $f1
+#> [1] 0.9699454
+#> 
                                    census_cohort_adjust(pop, age, 2020.5)
+
                                    #> $pop
+#>   [1] 5000.0 9950.5 9851.5 9752.5 9653.5 9554.5 9455.5 9356.5 9257.5 9158.5
+#>  [11] 9059.5 8960.5 8861.5 8762.5 8663.5 8564.5 8465.5 8366.5 8267.5 8168.5
+#>  [21] 8069.5 7970.5 7871.5 7772.5 7673.5 7574.5 7475.5 7376.5 7277.5 7178.5
+#>  [31] 7079.5 6980.5 6881.5 6782.5 6683.5 6584.5 6485.5 6386.5 6287.5 6188.5
+#>  [41] 6089.5 5990.5 5891.5 5792.5 5693.5 5594.5 5495.5 5396.5 5297.5 5198.5
+#>  [51] 5099.5 5000.5 4901.5 4802.5 4703.5 4604.5 4505.5 4406.5 4307.5 4208.5
+#>  [61] 4109.5 4010.5 3911.5 3812.5 3713.5 3614.5 3515.5 3416.5 3317.5 3218.5
+#>  [71] 3119.5 3020.5 2921.5 2822.5 2723.5 2624.5 2525.5 2426.5 2327.5 2228.5
+#>  [81] 2129.5 2030.5 1931.5 1832.5 1733.5 1634.5 1535.5 1436.5 1337.5 1238.5
+#>  [91] 1139.5 1040.5  941.5  842.5  743.5  644.5  545.5  446.5  347.5  248.5
+#> [101]  149.5
+#> 
+#> $cohort
+#>   [1] 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006
+#>  [16] 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991
+#>  [31] 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976
+#>  [46] 1975 1974 1973 1972 1971 1970 1969 1968 1967 1966 1965 1964 1963 1962 1961
+#>  [61] 1960 1959 1958 1957 1956 1955 1954 1953 1952 1951 1950 1949 1948 1947 1946
+#>  [76] 1945 1944 1943 1942 1941 1940 1939 1938 1937 1936 1935 1934 1933 1932 1931
+#>  [91] 1930 1929 1928 1927 1926 1925 1924 1923 1922 1921 1920
+#> 
+#> $date
+#> [1] 2020.5
+#> 
+#> $f1
+#> [1] 0.5
+#>
                                    +
                                    + +
                                    + + +
                                    +
                                    +

                                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                    +
                                    + +
                                    +

                                    Site built with pkgdown 1.6.1.

                                    +
                                    + +
                                    +
                                    + + + + + + + + diff --git a/docs/reference/check_heaping_bachi.html b/docs/reference/check_heaping_bachi.html index 5c978eecb..b5785bba2 100644 --- a/docs/reference/check_heaping_bachi.html +++ b/docs/reference/check_heaping_bachi.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                                @@ -88,7 +88,7 @@
                                • - +
                                  • @@ -129,13 +129,13 @@
                                  • - +
                                  • - + github @@ -166,7 +166,7 @@

                                    calculate Bachi's index of age heaping

                                    Value, Age, ageMin = 23, - ageMax = 77, + ageMax = NULL, method = "orig", details = FALSE, OAG = TRUE @@ -211,12 +211,12 @@

                                    Value

                                    Details

                                    ageMax is an inclusive upper bound, treated as interval. If you want ages -23 to 77, then give ageMin = 23 and ageMax = 77, not 80. The ageMin is respected strictly, whereas ageMax could be higher than the actual maximum age used. You can see the age ranges actually used by specifying details = TRUE.

                                    +23 to 77, then give ageMin = 23 and ageMax = 77. The ageMin is respected strictly, whereas ageMax is calculated flexibly- if you specify something too high then it is reduced and we warn accordingly, and if it's missing then we pick something reasonable. You can see the age ranges actually used by specifying details = TRUE.

                                    References

                                    United States Census Bureau (2017). “Population Analysis System (PAS) Software.” -https://www.census.gov/data/software/pas.html. +https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html. Bachi R (1951). “The tendency to round off age returns: measurement and correction.” diff --git a/docs/reference/check_heaping_coale_li.html b/docs/reference/check_heaping_coale_li.html index 4b4c61330..588851a29 100644 --- a/docs/reference/check_heaping_coale_li.html +++ b/docs/reference/check_heaping_coale_li.html @@ -84,7 +84,7 @@ DemoTools - 01.13.20 + 01.13.76

                                @@ -92,7 +92,7 @@
                                • - +
                                  • @@ -133,13 +133,13 @@
                                  • - +
                                  • - + github diff --git a/docs/reference/check_heaping_jdanov.html b/docs/reference/check_heaping_jdanov.html index f8cb03ca8..23638f5b3 100644 --- a/docs/reference/check_heaping_jdanov.html +++ b/docs/reference/check_heaping_jdanov.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                                @@ -88,7 +88,7 @@
                                • - +
                                  • @@ -129,13 +129,13 @@
                                  • - +
                                  • - + github diff --git a/docs/reference/check_heaping_kannisto.html b/docs/reference/check_heaping_kannisto.html index d6385e963..1bc6ac67d 100644 --- a/docs/reference/check_heaping_kannisto.html +++ b/docs/reference/check_heaping_kannisto.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                                @@ -88,7 +88,7 @@
                                • - +
                                  • @@ -129,13 +129,13 @@
                                  • - +
                                  • - + github @@ -190,7 +190,7 @@

                                    Value

                                    The value of the index.

                                    Details

                                    -

                                    The index looks down two ages and up two ages, so the data must accommodate that range. The denominator is a mean of the counts in the sourrounding 5 single ages. The kind of mean can be controlled with the pow argument. By default, this takes the antilog of the arithmetic mean of the natural log of the five denominator counts. That will fail if one of the counts is equal to 0. In such cases, another power, such as 2 or 10 or 100 may be used, which is more robust to 0s. The higher the power, the closer the result will resemble the default output. If pow=="exp" but a 0 is detected among the denominator ages, then pow is assigned a value of 1000. pow=1 would imply an arithmetic mean in the denominator.

                                    +

                                    The index looks down two ages and up two ages, so the data must accommodate that range. The denominator is a mean of the counts in the surrounding 5 single ages. The kind of mean can be controlled with the pow argument. By default, this takes the antilog of the arithmetic mean of the natural log of the five denominator counts. That will fail if one of the counts is equal to 0. In such cases, another power, such as 2 or 10 or 100 may be used, which is more robust to 0s. The higher the power, the closer the result will resemble the default output. If pow=="exp" but a 0 is detected among the denominator ages, then pow is assigned a value of 1000. pow=1 would imply an arithmetic mean in the denominator.

                                    References

                                    Kannisto V (1999). diff --git a/docs/reference/check_heaping_myers.html b/docs/reference/check_heaping_myers.html index a94a4ffee..e3a7db7e9 100644 --- a/docs/reference/check_heaping_myers.html +++ b/docs/reference/check_heaping_myers.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76

                            @@ -88,7 +88,7 @@
                            • - +
                              • @@ -129,13 +129,13 @@
                              • - +
                              • - + github @@ -220,7 +220,7 @@

                                R United States Census Bureau (2017). “Population Analysis System (PAS) Software.” -https://www.census.gov/data/software/pas.html.

                                +https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.

                                Examples

                                Age <- 0:99
diff --git a/docs/reference/check_heaping_noumbissi.html b/docs/reference/check_heaping_noumbissi.html
index 8a1071d55..e2e4c1e1d 100644
--- a/docs/reference/check_heaping_noumbissi.html
+++ b/docs/reference/check_heaping_noumbissi.html
@@ -80,7 +80,7 @@
       
       
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diff --git a/docs/reference/check_heaping_roughness.html b/docs/reference/check_heaping_roughness.html
index 1eff50daf..2ece36f18 100644
--- a/docs/reference/check_heaping_roughness.html
+++ b/docs/reference/check_heaping_roughness.html
@@ -47,7 +47,7 @@
 
 
 
-
+
 
 
 
@@ -80,7 +80,7 @@
       
       
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-        01.13.20
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                            @@ -88,7 +88,7 @@
                            • - +
                              • @@ -129,13 +129,13 @@
                            -

                            For a given age-structured vector of counts, how rough is data after grouping to 5-year age bins? Data may require smoothing even if there is no detectable sawtooth pattern. It is best to use the value in this method together with visual evidence to gauage whether use of smooth_age_5() is recommended.

                            +

                            For a given age-structured vector of counts, how rough is data after grouping to 5-year age bins? Data may require smoothing even if there is no detectable sawtooth pattern. It is best to use the value in this method together with visual evidence to gauge whether use of smooth_age_5() is recommended.

                            check_heaping_roughness(
@@ -186,7 +186,7 @@ 
                            Arg
     
     
       ageMax
-      
                            integer evently divisibly by 5. Upper bound of evaluated age range, defaults to highest age evenly divisible by 10.
                            
+      
                            integer evenly divisible by 5. Upper bound of evaluated age range, defaults to highest age evenly divisible by 10.
                            
     
     
 
diff --git a/docs/reference/check_heaping_sawtooth.html b/docs/reference/check_heaping_sawtooth.html
index 7cfc6eea2..0a5292c2c 100644
--- a/docs/reference/check_heaping_sawtooth.html
+++ b/docs/reference/check_heaping_sawtooth.html
@@ -47,7 +47,7 @@
 
 
 
-
+
 
 
 
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
                            @@ -88,7 +88,7 @@
                            • - +
                              • @@ -129,13 +129,13 @@
                              • - +
                              • - + github @@ -159,7 +159,7 @@

                                Detect if heaping is worse on terminal digits 0s than on 5s

                            -

                            Ages ending in 0 often have higher apparent heaping than ages ending in 5. In this case, data in 5-year age bins might show a sawtooth pattern. If heaping ocurrs in roughly the same amount on 0s and 5s, then it may be sufficient to group data into 5-year age groups and then graduate back to single ages. However, if heaping is worse on 0s, then this procedure tends to produce a wavy pattern in count data, with 10-year periodicity. In this case it is recommended to use one of the methods of smooth_age_5() as an intermediate step before graduation.

                            +

                            Ages ending in 0 often have higher apparent heaping than ages ending in 5. In this case, data in 5-year age bins might show a sawtooth pattern. If heaping occurs in roughly the same amount on 0s and 5s, then it may be sufficient to group data into 5-year age groups and then graduate back to single ages. However, if heaping is worse on 0s, then this procedure tends to produce a wavy pattern in count data, with 10-year periodicity. In this case it is recommended to use one of the methods of smooth_age_5() as an intermediate step before graduation.

                            check_heaping_sawtooth(
@@ -186,7 +186,7 @@ 
                            Arg
     
     
       ageMax
-      
                            integer evently divisibly by 5. Upper bound of evaluated age range, defaults to highest age evenly divisible by 10.
                            
+      
                            integer evenly divisible by 5. Upper bound of evaluated age range, defaults to highest age evenly divisible by 10.
                            
     
     
 
diff --git a/docs/reference/check_heaping_spoorenberg.html b/docs/reference/check_heaping_spoorenberg.html
index a3fce84d8..d1045351f 100644
--- a/docs/reference/check_heaping_spoorenberg.html
+++ b/docs/reference/check_heaping_spoorenberg.html
@@ -81,7 +81,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
                            @@ -89,7 +89,7 @@
                            • - +
                              • @@ -130,13 +130,13 @@
                              • - +
                              • - + github diff --git a/docs/reference/check_heaping_whipple.html b/docs/reference/check_heaping_whipple.html index 723e29966..676b5cc11 100644 --- a/docs/reference/check_heaping_whipple.html +++ b/docs/reference/check_heaping_whipple.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                            @@ -88,7 +88,7 @@
                            • - +
                              • @@ -129,13 +129,13 @@
                              • - +
                              • - + github @@ -203,7 +203,7 @@

                                R United States Census Bureau (2017). “Population Analysis System (PAS) Software.” -https://www.census.gov/data/software/pas.html.

                                +https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.

                                Examples

                                 Age <- 0:99
diff --git a/docs/reference/dec.date.html b/docs/reference/dec.date.html
index ef9c0edae..6b570f755 100644
--- a/docs/reference/dec.date.html
+++ b/docs/reference/dec.date.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
                                
 
@@ -88,7 +88,7 @@
       
                                  
         
                                • 
   
-    
+    
      
   
 
                                  • 
@@ -129,13 +129,13 @@
       
                                    
         
                                  • 
   
-    
+    
      
   
 
                                    • 
 
                                  • 
   
-    
+    
      
     github
   
@@ -178,7 +178,7 @@ 
                                    Value
                                    
     
                                    Numeric expression of the date, year plus the fraction of the year passed as of the date.
                                    
     
                                    Details
                                    
 
-    
                                    This makes use of the lubridate::decimal_date to compute the proportion of the year that has passed. If the date is numeric, it is returned as such. If it is "character", we try to coerce to date through lubridate::ymd, ergo, it is best to specify a character string in an unambiguous "YYYY-MM-DD" format.  If date is given in a "Date" class it is dealt with accordingly.
                                    
+    
                                    This makes use of the lubridate::decimal_date to compute the proportion of the year that has passed. If the date is numeric, it is returned as such. If it is "character", we try to coerce to date through lubridate::ymd, ergo, it is best to specify a character string in an unambiguous "YYYY-MM-DD" format.  If date is given in a "Date" class it is dealt with accordingly.
                            @@ -88,7 +88,7 @@
                            • - +
                              • @@ -129,13 +129,13 @@
                              • - +
                              • - + github diff --git a/docs/reference/e0_swe.html b/docs/reference/e0_swe.html new file mode 100644 index 000000000..eabaeea4c --- /dev/null +++ b/docs/reference/e0_swe.html @@ -0,0 +1,208 @@ + + + + + + + + +Swedish life expectancy at birth — e0_swe • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                +
                                + + + + +
                                + +
                                +
                                +
                                +

                                Swedish life expectancy at birth

                                + Source: R/data.R + +
                                + +
                                +

                                Life expectancy at birth by sex in tidy format for dates from 1960-07-01 to 2015-07-01 by 5 calendar years.

                                +
                                + + 
                                e0_swe
                                + + +

                                Format

                                + +

                                A data frame with:

                                +
                                Date

                                Reference time.

                                +
                                Sex

                                Male m and female m.

                                +
                                e0

                                Life expectancy at birth.

                                + +
                                + +

                                Source

                                + +

                                Human Mortality Database. Retrieved 2021-20-01, from https://mortality.org

                                + +
                                + +
                                + + +
                                +
                                +

                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                +
                                + +
                                +

                                Site built with pkgdown 1.6.1.

                                +
                                + +
                                +
                                + + + + + + + + diff --git a/docs/reference/fetch_wpp_births.html b/docs/reference/fetch_wpp_births.html new file mode 100644 index 000000000..cd4ceb11a --- /dev/null +++ b/docs/reference/fetch_wpp_births.html @@ -0,0 +1,223 @@ + + + + + + + + +extract births from wpp2019 — fetch_wpp_births • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                +
                                + + + + +
                                + +
                                +
                                +
                                +

                                extract births from wpp2019

                                + Source: R/utils_downloads.R + +
                                + +
                                +

                                extract births from wpp2019

                                +
                                + + 
                                fetch_wpp_births(births, yrs_births, location, sex, verbose)
                                + +

                                Arguments

                                + + + + + + + + + + + + + + + + + + + + + + +
                                births

                                NULL or else a vector of births to simply return

                                yrs_births

                                vector of years to extract

                                location

                                UN Pop Dov LocName or LocID

                                sex

                                "male", "female", or "both"

                                verbose

                                logical, shall we send optional messages to the console?

                                + +

                                Value

                                + +

                                vector of births

                                + +
                                + +
                                + + +
                                +
                                +

                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                +
                                + +
                                +

                                Site built with pkgdown 1.6.1.

                                +
                                + +
                                +
                                + + + + + + + + diff --git a/docs/reference/find_my_case.html b/docs/reference/find_my_case.html new file mode 100644 index 000000000..827dfe539 --- /dev/null +++ b/docs/reference/find_my_case.html @@ -0,0 +1,212 @@ + + + + + + + + +Function that determines the case/problem we have to solve +It also performs some checks — find_my_case • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                +
                                + + + + +
                                + +
                                +
                                +
                                +

                                Function that determines the case/problem we have to solve +It also performs some checks

                                + Source: R/lt_model_lq.R + +
                                + +
                                +

                                Function that determines the case/problem we have to solve +It also performs some checks

                                +
                                + + 
                                find_my_case(par_ind)
                                + +

                                Arguments

                                + + + + + + +
                                par_ind

                                logical vector of length 5

                                + +

                                Details

                                + +

                                par_ind should consist in logicals in the following order: q0_5, q0_1, q15_45, q15_35, e0. This is faithfully constructed in calling functions as required.

                                + +
                                + +
                                + + +
                                +
                                +

                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                +
                                + +
                                +

                                Site built with pkgdown 1.6.1.

                                +
                                + +
                                +
                                + + + + + + + + diff --git a/docs/reference/fitted_logquad_b.html b/docs/reference/fitted_logquad_b.html index 8f09c69f7..c4b034418 100644 --- a/docs/reference/fitted_logquad_b.html +++ b/docs/reference/fitted_logquad_b.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                            @@ -88,7 +88,7 @@
                            • - +
                              • @@ -129,13 +129,13 @@
                              • - +
                              • - + github diff --git a/docs/reference/fitted_logquad_f.html b/docs/reference/fitted_logquad_f.html index cf2dab46e..81af7020a 100644 --- a/docs/reference/fitted_logquad_f.html +++ b/docs/reference/fitted_logquad_f.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76

                            @@ -88,7 +88,7 @@
                            • - +
                              • @@ -129,13 +129,13 @@
                              • - +
                              • - + github diff --git a/docs/reference/fitted_logquad_m.html b/docs/reference/fitted_logquad_m.html index fb44a93b8..0847add7c 100644 --- a/docs/reference/fitted_logquad_m.html +++ b/docs/reference/fitted_logquad_m.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                            @@ -88,7 +88,7 @@
                            • - +
                              • @@ -129,13 +129,13 @@
                              • - +
                              • - + github diff --git a/docs/reference/getModelLifeTable.html b/docs/reference/getModelLifeTable.html index 53f04e1fc..9836b9fb7 100644 --- a/docs/reference/getModelLifeTable.html +++ b/docs/reference/getModelLifeTable.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                            @@ -88,7 +88,7 @@
                            • - +
                              • @@ -129,13 +129,13 @@
                              • - +
                              • - + github diff --git a/docs/reference/graduate.html b/docs/reference/graduate.html index 14f34da40..4f89d286b 100644 --- a/docs/reference/graduate.html +++ b/docs/reference/graduate.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                        @@ -88,7 +88,7 @@
                        • - +
                          • @@ -129,13 +129,13 @@
                          • - +
                          • - + github @@ -223,7 +223,7 @@

                            Details

                            Beers may either be ordinary "beers(ord)" or modified "beers(mod)", and either can pass on the optional argument johnson = TRUE if desired (this has a different distribution pattern for young ages, FALSE by default). If method = "beers" is given, then "beers(ord)" is used.

                            This wrapper standardizes some inconsistencies in how open ages are dealt with. For example, with the "pclm" method, the last age group can be redistributed over a specified interval implied by increase OAnew beyond the range of Age. To get this same behavior from "mono", or "uniform" specify OAG = FALSE along with an appropriately high OAnew (or integer final value of AgeInt.

                            OAnew cannot be higher than max(Age)+4 for "sprague" or "beers" methods. For "uniform","mono","pclm" it can be higher than this, and in each case the open age group is completely redistributed within this range, meaning it's not really open anymore.

                            -

                            For all methods, negative values are detected in output. If present, we deal with these in the following way: we take the geometric mean between the given output (with negative imputed with 0s) and the output of graduate_mono(), which is guaranteed non-negative. This only affects age groups where negatives were produced in the first pass. In our experience this only arises when using sprague, beers, or grabill methods, whereas all others are guarateed non-negative.

                            +

                            For all methods, negative values are detected in output. If present, we deal with these in the following way: we take the geometric mean between the given output (with negative imputed with 0s) and the output of graduate_mono(), which is guaranteed non-negative. This only affects age groups where negatives were produced in the first pass. In our experience this only arises when using Sprague, Beers, or Grabill methods, whereas all others are guaranteed non-negative.

                            For any case where input data are in single ages, constraining results to sum to values in the original age groups will simply return the original input data, which is clearly not your intent. This might arise when using graduation as an implicit two-step smoother (group + graduate). In this case, separate the steps, first group using groupAges() then use graduate(..., constrain = TRUE).

                            References

                            @@ -503,58 +503,74 @@

                            Examp #> 98 99 100 #> 3.7144 6.4474 0.0000

                        graduate(Value, Age, method = "mono") -
                        #> 0 1 2 3 4 5 6 7 -#> 10000.000 10945.000 11401.250 11270.625 10553.125 9665.128 9122.756 8767.692 -#> 8 9 10 11 12 13 14 15 -#> 8599.936 8619.488 8655.760 8567.160 8453.480 8314.720 8150.880 7993.120 -#> 16 17 18 19 20 21 22 23 -#> 7852.080 7701.920 7542.640 7374.240 7205.232 7042.304 6880.288 6719.184 -#> 24 25 26 27 28 29 30 31 -#> 6558.992 6389.728 6219.856 6063.592 5920.936 5791.888 5662.824 5524.688 -#> 32 33 34 35 36 37 38 39 -#> 5386.576 5248.488 5110.424 4963.192 4815.944 4682.448 4562.704 4456.712 -#> 40 41 42 43 44 45 46 47 -#> 4370.464 4275.048 4150.016 3995.368 3811.104 3596.888 3402.416 3252.872 -#> 48 49 50 51 52 53 54 55 -#> 3148.256 3088.568 3062.144 3005.768 2899.496 2743.328 2537.264 2285.088 -#> 56 57 58 59 60 61 62 63 -#> 2066.176 1916.432 1835.856 1824.448 1849.056 1827.552 1752.024 1622.472 -#> 64 65 66 67 68 69 70 71 -#> 1438.896 1215.528 1028.176 900.512 832.536 824.248 821.544 779.048 -#> 72 73 74 75 76 77 78 79 -#> 728.176 668.928 601.304 526.576 457.432 398.944 351.112 313.936 -#> 80 81 82 83 84 85 86 87 -#> 268.888 216.736 176.392 147.856 131.128 108.496 77.992 56.344 -#> 88 89 90 91 92 93 94 95 -#> 43.552 39.616 32.752 19.984 11.608 7.624 8.032 7.432 -#> 96 97 98 99 100 -#> 3.904 1.888 1.384 2.392 0.000
                        graduate(Value, Age, method = "mono", keep0=TRUE) -
                        #> 0 1 2 3 4 5 6 7 -#> 10000.000 10945.000 11401.250 11270.625 10553.125 9665.128 9122.756 8767.692 -#> 8 9 10 11 12 13 14 15 -#> 8599.936 8619.488 8655.760 8567.160 8453.480 8314.720 8150.880 7993.120 -#> 16 17 18 19 20 21 22 23 -#> 7852.080 7701.920 7542.640 7374.240 7205.232 7042.304 6880.288 6719.184 -#> 24 25 26 27 28 29 30 31 -#> 6558.992 6389.728 6219.856 6063.592 5920.936 5791.888 5662.824 5524.688 -#> 32 33 34 35 36 37 38 39 -#> 5386.576 5248.488 5110.424 4963.192 4815.944 4682.448 4562.704 4456.712 -#> 40 41 42 43 44 45 46 47 -#> 4370.464 4275.048 4150.016 3995.368 3811.104 3596.888 3402.416 3252.872 -#> 48 49 50 51 52 53 54 55 -#> 3148.256 3088.568 3062.144 3005.768 2899.496 2743.328 2537.264 2285.088 -#> 56 57 58 59 60 61 62 63 -#> 2066.176 1916.432 1835.856 1824.448 1849.056 1827.552 1752.024 1622.472 -#> 64 65 66 67 68 69 70 71 -#> 1438.896 1215.528 1028.176 900.512 832.536 824.248 821.544 779.048 -#> 72 73 74 75 76 77 78 79 -#> 728.176 668.928 601.304 526.576 457.432 398.944 351.112 313.936 -#> 80 81 82 83 84 85 86 87 -#> 268.888 216.736 176.392 147.856 131.128 108.496 77.992 56.344 -#> 88 89 90 91 92 93 94 95 -#> 43.552 39.616 32.752 19.984 11.608 7.624 8.032 7.432 -#> 96 97 98 99 100 -#> 3.904 1.888 1.384 2.392 0.000
                        +
                        #> 0 1 2 3 4 5 +#> 10000.000000 10850.481727 11280.016176 11257.267224 10782.234872 9965.472958 +#> 6 7 8 9 10 11 +#> 9249.196842 8743.960363 8449.763521 8366.606317 8447.848263 8506.927414 +#> 12 13 14 15 16 17 +#> 8497.203282 8418.675868 8271.345172 8069.013989 7866.893502 7678.786508 +#> 18 19 20 21 22 23 +#> 7504.693006 7344.612996 7195.295781 7043.738577 6886.690686 6724.152109 +#> 24 25 26 27 28 29 +#> 6556.122846 6385.146886 6221.400190 6067.426747 5923.226557 5788.799621 +#> 30 31 32 33 34 35 +#> 5661.452674 5530.412664 5392.986327 5249.173663 5098.974671 4946.090416 +#> 36 37 38 39 40 41 +#> 4805.325153 4680.379945 4571.254792 4477.949695 4392.929661 4286.054725 +#> 42 43 44 45 46 47 +#> 4149.789895 3984.135169 3789.090550 3576.638938 3394.711946 3255.292477 +#> 48 49 50 51 52 53 +#> 3158.380531 3103.976107 3076.530586 3013.849489 2900.384196 2736.134707 +#> 54 55 56 57 58 59 +#> 2521.101022 2273.886718 2068.906098 1924.762739 1841.456641 1818.987804 +#> 60 61 62 63 64 65 +#> 1838.178543 1822.318119 1752.228848 1627.910728 1449.363761 1233.631109 +#> 66 67 68 69 70 71 +#> 1048.885424 912.169870 823.484446 782.829152 779.113021 767.972184 +#> 72 73 74 75 76 77 +#> 738.315673 690.143490 623.455633 542.884808 466.961841 400.319437 +#> 78 79 80 81 82 83 +#> 342.957596 294.876318 254.979747 218.884453 185.494579 154.810127 +#> 84 85 86 87 88 89 +#> 126.831094 101.692205 79.932348 61.686245 46.953897 35.735304 +#> 90 91 92 93 94 95 +#> 27.603433 20.850155 15.048439 10.198284 6.299690 3.438064 +#> 96 97 98 99 100 +#> 1.955032 1.936000 3.380968 6.289936 0.000000
                        graduate(Value, Age, method = "mono", keep0=TRUE) +
                        #> 0 1 2 3 4 5 +#> 10000.000000 10850.481727 11280.016176 11257.267224 10782.234872 9965.472958 +#> 6 7 8 9 10 11 +#> 9249.196842 8743.960363 8449.763521 8366.606317 8447.848263 8506.927414 +#> 12 13 14 15 16 17 +#> 8497.203282 8418.675868 8271.345172 8069.013989 7866.893502 7678.786508 +#> 18 19 20 21 22 23 +#> 7504.693006 7344.612996 7195.295781 7043.738577 6886.690686 6724.152109 +#> 24 25 26 27 28 29 +#> 6556.122846 6385.146886 6221.400190 6067.426747 5923.226557 5788.799621 +#> 30 31 32 33 34 35 +#> 5661.452674 5530.412664 5392.986327 5249.173663 5098.974671 4946.090416 +#> 36 37 38 39 40 41 +#> 4805.325153 4680.379945 4571.254792 4477.949695 4392.929661 4286.054725 +#> 42 43 44 45 46 47 +#> 4149.789895 3984.135169 3789.090550 3576.638938 3394.711946 3255.292477 +#> 48 49 50 51 52 53 +#> 3158.380531 3103.976107 3076.530586 3013.849489 2900.384196 2736.134707 +#> 54 55 56 57 58 59 +#> 2521.101022 2273.886718 2068.906098 1924.762739 1841.456641 1818.987804 +#> 60 61 62 63 64 65 +#> 1838.178543 1822.318119 1752.228848 1627.910728 1449.363761 1233.631109 +#> 66 67 68 69 70 71 +#> 1048.885424 912.169870 823.484446 782.829152 779.113021 767.972184 +#> 72 73 74 75 76 77 +#> 738.315673 690.143490 623.455633 542.884808 466.961841 400.319437 +#> 78 79 80 81 82 83 +#> 342.957596 294.876318 254.979747 218.884453 185.494579 154.810127 +#> 84 85 86 87 88 89 +#> 126.831094 101.692205 79.932348 61.686245 46.953897 35.735304 +#> 90 91 92 93 94 95 +#> 27.603433 20.850155 15.048439 10.198284 6.299690 3.438064 +#> 96 97 98 99 100 +#> 1.955032 1.936000 3.380968 6.289936 0.000000
                        graduate(Value, Age, method = "uniform")
                        #> 0 1 2 3 4 5 6 7 8 9 #> 10000.0 11042.5 11042.5 11042.5 11042.5 8955.0 8955.0 8955.0 8955.0 8955.0 @@ -579,74 +595,76 @@

                        Examp #> 100 #> 0.0

                        graduate(Value, Age, method = "pclm") -
                        #> 0 1 2 3 4 5 -#> 10022.206163 10787.283969 11254.932560 11275.885640 10807.951426 10074.821803 +
                        #> +#> 0s detected in Value, replacing with .01
                        #> 0 1 2 3 4 5 +#> 10022.215013 10787.284952 11254.927495 11275.878141 10807.945737 10074.820067 #> 6 7 8 9 10 11 -#> 9328.988743 8736.949690 8394.290482 8273.419213 8318.185348 8432.064234 +#> 9328.990796 8736.954126 8394.295366 8273.423000 8318.186994 8432.063491 #> 12 13 14 15 16 17 -#> 8510.164398 8497.044473 8366.765820 8151.826916 7903.088275 7662.042020 +#> 8510.161955 8497.041470 8366.763507 8151.826000 7903.088751 7662.043415 #> 18 19 20 21 22 23 -#> 7459.547646 7296.414763 7161.468632 7035.158814 6896.995478 6740.484441 +#> 7459.549206 7296.415870 7161.468953 7035.158379 6896.994675 6740.483732 #> 24 25 26 27 28 29 -#> 6566.732479 6387.045673 6215.809170 6060.717420 5925.005956 5801.821192 +#> 6566.732244 6387.046021 6215.809887 6060.718142 5925.006272 5801.820878 #> 30 31 32 33 34 35 -#> 5679.678101 5548.089016 5398.665319 5234.736857 5066.280112 4908.164620 +#> 5679.677222 5548.087900 5398.664503 5234.736788 5066.280979 4908.166239 #> 36 37 38 39 40 41 -#> 4776.277248 4674.567053 4598.978778 4531.954680 4447.598716 4325.845343 +#> 4776.279043 4674.568329 4598.978894 4531.953370 4447.596301 4325.842633 #> 42 43 44 45 46 47 -#> 4153.831515 3942.404243 3717.818155 3511.254514 3353.612942 3254.021669 +#> 4153.829634 3942.404051 3717.819885 3511.257711 3353.616522 3254.024393 #> 48 49 50 51 52 53 -#> 3206.436723 3185.639207 3149.061722 3061.490650 2899.845842 2676.397236 +#> 3206.437472 3185.637396 3149.057772 3061.485892 2899.842199 2676.396203 #> 54 55 56 57 58 59 -#> 2430.916306 2205.094663 2036.287305 1935.376745 1895.248588 1894.350582 +#> 2430.918221 2205.098826 2036.292267 1935.380958 1895.250695 1894.349683 #> 60 61 62 63 64 65 -#> 1891.972240 1851.214010 1745.251332 1577.505631 1381.551282 1192.256351 +#> 1891.968479 1851.208654 1745.246502 1577.503188 1381.551771 1192.259170 #> 66 67 68 69 70 71 -#> 1038.626280 929.281152 859.265601 818.951293 792.503364 766.385429 +#> 1038.630147 929.284819 859.268152 818.952155 792.502498 766.383285 #> 72 73 74 75 76 77 -#> 729.252758 676.036519 610.318840 538.450939 468.333489 404.619331 +#> 729.250203 676.034500 610.317889 538.451097 468.334362 404.620404 #> 78 79 80 81 82 83 -#> 348.592064 299.924683 257.028970 218.668992 184.007104 152.690098 +#> 348.592969 299.925248 257.029245 218.669115 184.007169 152.690106 #> 84 85 86 87 88 89 -#> 124.764602 100.309353 79.377465 61.866165 47.547799 36.084595 +#> 124.764414 100.308814 79.376504 61.864847 47.546364 36.083333 #> 90 91 92 93 94 95 -#> 27.084347 20.136711 14.848708 10.871851 7.909705 5.722058 +#> 27.083527 20.136500 14.849153 10.872898 7.911233 5.723911 #> 96 97 98 99 100 -#> 4.118689 2.951838 2.108558 1.502782 1.069736
                        graduate(Value, Age, method = "pclm", keep0=TRUE) -
                        #> 0 1 2 3 4 5 -#> 10000.000000 10798.027335 11266.141671 11287.115619 10818.715375 10074.821803 +#> 4.120705 2.953872 2.110491 1.504532 1.071260
                        graduate(Value, Age, method = "pclm", keep0=TRUE) +
                        #> +#> 0s detected in Value, replacing with .01
                        #> 0 1 2 3 4 5 +#> 10000.000000 10798.032545 11266.141010 11287.112529 10818.713915 10074.820067 #> 6 7 8 9 10 11 -#> 9328.988743 8736.949690 8394.290482 8273.419213 8318.185348 8432.064234 +#> 9328.990796 8736.954126 8394.295366 8273.423000 8318.186994 8432.063491 #> 12 13 14 15 16 17 -#> 8510.164398 8497.044473 8366.765820 8151.826916 7903.088275 7662.042020 +#> 8510.161955 8497.041470 8366.763507 8151.826000 7903.088751 7662.043415 #> 18 19 20 21 22 23 -#> 7459.547646 7296.414763 7161.468632 7035.158814 6896.995478 6740.484441 +#> 7459.549206 7296.415870 7161.468953 7035.158379 6896.994675 6740.483732 #> 24 25 26 27 28 29 -#> 6566.732479 6387.045673 6215.809170 6060.717420 5925.005956 5801.821192 +#> 6566.732244 6387.046021 6215.809887 6060.718142 5925.006272 5801.820878 #> 30 31 32 33 34 35 -#> 5679.678101 5548.089016 5398.665319 5234.736857 5066.280112 4908.164620 +#> 5679.677222 5548.087900 5398.664503 5234.736788 5066.280979 4908.166239 #> 36 37 38 39 40 41 -#> 4776.277248 4674.567053 4598.978778 4531.954680 4447.598716 4325.845343 +#> 4776.279043 4674.568329 4598.978894 4531.953370 4447.596301 4325.842633 #> 42 43 44 45 46 47 -#> 4153.831515 3942.404243 3717.818155 3511.254514 3353.612942 3254.021669 +#> 4153.829634 3942.404051 3717.819885 3511.257711 3353.616522 3254.024393 #> 48 49 50 51 52 53 -#> 3206.436723 3185.639207 3149.061722 3061.490650 2899.845842 2676.397236 +#> 3206.437472 3185.637396 3149.057772 3061.485892 2899.842199 2676.396203 #> 54 55 56 57 58 59 -#> 2430.916306 2205.094663 2036.287305 1935.376745 1895.248588 1894.350582 +#> 2430.918221 2205.098826 2036.292267 1935.380958 1895.250695 1894.349683 #> 60 61 62 63 64 65 -#> 1891.972240 1851.214010 1745.251332 1577.505631 1381.551282 1192.256351 +#> 1891.968479 1851.208654 1745.246502 1577.503188 1381.551771 1192.259170 #> 66 67 68 69 70 71 -#> 1038.626280 929.281152 859.265601 818.951293 792.503364 766.385429 +#> 1038.630147 929.284819 859.268152 818.952155 792.502498 766.383285 #> 72 73 74 75 76 77 -#> 729.252758 676.036519 610.318840 538.450939 468.333489 404.619331 +#> 729.250203 676.034500 610.317889 538.451097 468.334362 404.620404 #> 78 79 80 81 82 83 -#> 348.592064 299.924683 257.028970 218.668992 184.007104 152.690098 +#> 348.592969 299.925248 257.029245 218.669115 184.007169 152.690106 #> 84 85 86 87 88 89 -#> 124.764602 100.309353 79.377465 61.866165 47.547799 36.084595 +#> 124.764414 100.308814 79.376504 61.864847 47.546364 36.083333 #> 90 91 92 93 94 95 -#> 27.084347 20.136711 14.848708 10.871851 7.909705 5.722058 +#> 27.083527 20.136500 14.849153 10.872898 7.911233 5.723911 #> 96 97 98 99 100 -#> 4.118689 2.951838 2.108558 1.502782 1.069736
                        # pclm can also graduate rates if both +#> 4.120705 2.953872 2.110491 1.504532 1.071260
                        # pclm can also graduate rates if both # numerators and denominators are on hand: Exposures <- c(100958,466275,624134,559559,446736,370653,301862,249409, 247473,223014,172260,149338,127242,105715,79614,53660, diff --git a/docs/reference/graduate_beers.html b/docs/reference/graduate_beers.html index ba07b1576..2e668b811 100644 --- a/docs/reference/graduate_beers.html +++ b/docs/reference/graduate_beers.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                        @@ -88,7 +88,7 @@
                        - 
                        graduate_beers(Value, Age, AgeInt, OAG = TRUE, method = "mod", johnson = FALSE)
                        + 
                        graduate_beers(Value, Age, AgeInt, OAG = TRUE, method = "ord", johnson = FALSE)

                        Arguments

                        @@ -185,11 +185,11 @@

                        Arg

                        - + - +
                        method

                        character. Valid values are "mod" or "ord". Default "mod".

                        character. Valid values are "ord" or "mod". Default "ord".
                        johnson

                        logical. Whether or not to adjust young ages according to the Demographic Analysis & Population Projection System Software method. Default FALSE.

                        logical. Whether or not to adjust young ages according to the Demographic Analysis & Population Projection System Software method. Default FALSE.
                        @@ -198,8 +198,9 @@

                        Value

                        A numeric vector of single age data.

                        Details

                        -

                        Ages should refer to lower age bounds. Value must be labelled with ages unless Age is given separately. There must be at least six 5-year age groups (including the open group, 5 otherwise). If you want the johnson adjustment then Value must contain a single-year estimate of the population count in age 0. That means Value must come either as standard abridged or single age data.

                        -

                        If the highest age does not end in a 0 or 5, and OAG == TRUE, then the open age will be grouped down to the next highest age ending in 0 or 5. If the highest age does not end in a 0 or 5, and OAG == FALSE, then results extend to single ages covering the entire 5-year age group.

                        +

                        Ages should refer to lower age bounds. Value must be labeled with ages unless Age is given separately. There must be at least six 5-year age groups (including the open group, 5 otherwise). If you want the johnson adjustment then Value must contain a single-year estimate of the population count in age 0. That means Value must come either as standard abridged or single age data.

                        +

                        method option "ord" conserves sums in 5-year age groups, whereas "mod" does some smoothing between 5-year age groups too, and is not constrained.

                        +

                        If the highest age does not end in a 0 or 5, and OAG == TRUE, then the open age will be grouped down to the next highest age ending in 0 or 5. If the highest age does not end in a 0 or 5, and OAG = FALSE, then results extend to single ages covering the entire 5-year age group.

                        References

                        Beers HS (1945). @@ -231,10 +232,10 @@

                        Examp #> 25 30386 30605 30978 31439 31940

                        p1 <- graduate_beers(p5[,1], Age = a5, OAG = FALSE) head(p1)
                        #> 0 1 2 3 4 5 -#> 11952.235 11325.417 10766.300 10274.882 9851.165 9494.302
                        # note some negatives in high ages +#> 11932.734 11342.324 10788.613 10280.289 9826.041 9435.405
                        # note some negatives in high ages tail(p1)
                        #> 99 100 101 102 103 104 -#> 0.3184 -0.0886 -0.2724 -0.2281 0.0443 0.5448
                        sum(p1) - sum(p5[,1]) +#> 2.8740 2.8627 2.4001 1.1396 -1.2605 -5.1419
                        sum(p1) - sum(p5[,1])
                        #> [1] 0
                        # another case, starting with single ages # note beers() groups ages. diff --git a/docs/reference/graduate_beers_expand.html b/docs/reference/graduate_beers_expand.html index 2e6db857b..54917b69a 100644 --- a/docs/reference/graduate_beers_expand.html +++ b/docs/reference/graduate_beers_expand.html @@ -82,7 +82,7 @@ DemoTools - 01.13.20 + 01.13.76
                        @@ -90,7 +90,7 @@
                        • - +
                          • @@ -131,13 +131,13 @@
                          • - +
                          • - + github diff --git a/docs/reference/graduate_beers_johnson.html b/docs/reference/graduate_beers_johnson.html index 361c5e622..1b024ae6b 100644 --- a/docs/reference/graduate_beers_johnson.html +++ b/docs/reference/graduate_beers_johnson.html @@ -82,7 +82,7 @@ DemoTools - 01.13.20 + 01.13.76
                    @@ -90,7 +90,7 @@
                    • - +
                      • @@ -131,13 +131,13 @@
                      • - +
                      • - + github diff --git a/docs/reference/graduate_grabill.html b/docs/reference/graduate_grabill.html index b05220d02..2883c2ad3 100644 --- a/docs/reference/graduate_grabill.html +++ b/docs/reference/graduate_grabill.html @@ -81,7 +81,7 @@ DemoTools - 01.13.20 + 01.13.76
                    @@ -89,7 +89,7 @@
                    • - +
                      • @@ -130,13 +130,13 @@
                      • - +
                      • - + github @@ -164,7 +164,7 @@

                        The basic Grabill age-splitting method

                        Sprague estimated single-age population counts for the first and final ten ages. Open age groups are preserved, as are annual totals.

                    - 
                    graduate_grabill(Value, Age, OAG = TRUE)
                    + 
                    graduate_grabill(Value, Age, AgeInt, OAG = TRUE)

                    Arguments

                    @@ -177,6 +177,10 @@

                    Arg

                    + + + + @@ -188,7 +192,7 @@

                    Value

                    numeric vector in single ages.

                    Details

                    -

                    Dimension labelling is necessary. There must be at least six age groups (including the open group). One year of data will work as well, as long as it's given as a single-column matrix. Data may be given in either single or grouped ages. If the highest age does not end in a 0 or 5, and OAG == TRUE, then the open age will be grouped down to the next highest age ending in 0 or 5. If the highest age does not end in a 0 or 5, and OAG == FALSE, then results extend to single ages covering the entire 5-year age group.

                    +

                    Dimension labeling is necessary. There must be at least six age groups (including the open group). One year of data will work as well, as long as it's given as a single-column matrix. Data may be given in either single or grouped ages. If the highest age does not end in a 0 or 5, and OAG == TRUE, then the open age will be grouped down to the next highest age ending in 0 or 5. If the highest age does not end in a 0 or 5, and OAG == FALSE, then results extend to single ages covering the entire 5-year age group.

                    References

                    Shryock HS, Siegel JS, Larmon EA (1973). diff --git a/docs/reference/graduate_grabill_expand.html b/docs/reference/graduate_grabill_expand.html index fb6a18ec5..a8d47f640 100644 --- a/docs/reference/graduate_grabill_expand.html +++ b/docs/reference/graduate_grabill_expand.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@

                    • - +
                      • @@ -129,13 +129,13 @@
                      • - +
                      • - + github diff --git a/docs/reference/graduate_mono.html b/docs/reference/graduate_mono.html index f40bcf353..0d52e3874 100644 --- a/docs/reference/graduate_mono.html +++ b/docs/reference/graduate_mono.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                        • - +
                          • @@ -129,13 +129,13 @@
                          • - +
                          • - + github @@ -162,7 +162,7 @@

                            Graduate age groups using a monotonic spline.

                            Take the cumulative sum of Value and then run a monotonic spline through it. The first differences split back single-age estimates of Value. Optionally keep the open age group untouched.

                            - 
                            graduate_mono(Value, AgeInt, Age, OAG = TRUE)
                            + 
                            graduate_mono(Value, Age, AgeInt, OAG = TRUE)

                            Arguments

                    Age

                    integer vector, lower bounds of age groups
                    AgeInt

                    integer vector, age interval widths

                    OAG

                    logical, default = TRUE is the final age group open?
                    @@ -171,14 +171,14 @@

                    Arg

                    - - - - + + + + @@ -190,7 +190,7 @@

                    Value

                    Numeric. vector of single smoothed age counts.

                    Details

                    -

                    The "monoH.FC" method of stats::splinefun() is used to fit the spline because 1) it passes exactly through the points, 2) it is monotonic and therefore guarantees positive counts, and 3) it seems to be a bit less wiggly (lower average first differences of split counts). Single-age data is returned as-is. If you want to use this function as a smoother you first need to group to non-single ages.

                    +

                    The "hyman" method of stats::splinefun() is used to fit the spline because 1) it passes exactly through the points, 2) it is monotonic and therefore guarantees positive counts, and 3) it seems to be a bit less wiggly (lower average first differences of split counts). Single-age data is returned as-is. If you want to use this function as a smoother you first need to group to non-single ages. Age be any age range, it does not need to start at 0.

                    References

                    Fritsch FN, Carlson RE (1980). @@ -204,69 +204,80 @@

                    Examp "10", "15", "20", "25", "30", "35", "40", "45", "50", "55", "60", "65", "70", "75", "80", "85", "90", "95", "100")) -# overwrite open age group with a single age estimate for that age -# (doesn't extrapolate) -graduate_mono(Value) -
                    #> 0 1 2 3 4 5 6 7 -#> 17689.096 17653.592 17671.344 17742.352 17866.616 17984.832 18067.544 18159.328 -#> 8 9 10 11 12 13 14 15 -#> 18260.184 18370.112 18475.416 18574.672 18680.864 18793.992 18914.056 19033.000 -#> 16 17 18 19 20 21 22 23 -#> 19147.320 19263.320 19381.000 19500.360 19622.696 19743.912 19860.664 19972.952 -#> 24 25 26 27 28 29 30 31 -#> 20080.776 20196.120 20314.440 20421.480 20517.240 20601.720 20687.736 20781.272 -#> 32 33 34 35 36 37 38 39 -#> 20872.504 20961.432 21048.056 21128.104 21209.688 21301.136 21402.448 21513.624 -#> 40 41 42 43 44 45 46 47 -#> 21618.352 21716.504 21824.328 21941.824 22068.992 22222.144 22368.848 22476.576 -#> 48 49 50 51 52 53 54 55 -#> 22545.328 22575.104 22642.672 22736.224 22773.088 22753.264 22676.752 22644.112 -#> 56 57 58 59 60 61 62 63 -#> 22649.264 22588.608 22462.144 22269.872 22118.752 22011.504 21842.528 21611.824 -#> 64 65 66 67 68 69 70 71 -#> 21319.392 21034.528 20790.816 20535.752 20269.336 19991.568 19845.608 19707.216 -#> 72 73 74 75 76 77 78 79 -#> 19371.112 18837.296 18105.768 17465.200 16956.440 16311.240 15529.600 14611.520 -#> 80 81 82 83 84 85 86 87 -#> 13725.360 12930.160 12087.080 11196.120 10257.280 9269.208 8313.056 7430.752 -#> 88 89 90 91 92 93 94 95 -#> 6622.296 5887.688 5036.104 4135.288 3409.936 2860.048 2485.624 1914.640 -#> 96 97 98 99 100 -#> 1226.680 833.560 735.280 931.840 1110.000
                    # or respect open age group +# if the last age group is closed, then it's best to use AgeInt, otherwise, +# one is assumed from the age siphoned from the names attribute of Value. +graduate_mono(Value, OAG = FALSE) +
                    #> 0 1 2 3 4 5 6 +#> 17565.4135 17641.9827 17721.5760 17804.1933 17889.8345 17978.5187 18070.3217 +#> 7 8 9 10 11 12 13 +#> 18165.2623 18263.3406 18364.5567 18468.8156 18575.7386 18685.2308 18797.2922 +#> 14 15 16 17 18 19 20 +#> 18911.9229 19028.7708 19146.4281 19264.5427 19383.1146 19502.1439 19621.3811 +#> 21 22 23 24 25 26 27 +#> 19739.8291 19857.2386 19973.6094 20088.9417 20202.5367 20311.5993 20415.4310 +#> 28 29 30 31 32 33 34 +#> 20514.0317 20607.4013 20696.3123 20783.8535 20870.7974 20957.1439 21042.8930 +#> 35 36 37 38 39 40 41 +#> 21128.6463 21216.8106 21307.9874 21402.1768 21499.3788 21600.4704 21708.9601 +#> 42 43 44 45 46 47 48 +#> 21825.7249 21950.7648 22084.0798 22221.4960 22346.3171 22454.3691 22545.6520 +#> 49 50 51 52 53 54 55 +#> 22620.1659 22677.5137 22716.1076 22735.5508 22735.8432 22716.9847 22678.8334 +#> 56 57 58 59 60 61 62 +#> 22620.8204 22542.8037 22444.7833 22326.7592 22186.6569 22016.1788 21813.2503 +#> 63 64 65 66 67 68 69 +#> 21577.8715 21310.0425 21019.5630 20745.6326 20498.0511 20276.8185 20081.9348 +#> 70 71 72 73 74 75 76 +#> 19893.0670 19628.8829 19269.0494 18813.5665 18262.4342 17625.0648 16939.1078 +#> 77 78 79 80 81 82 83 +#> 16213.9754 15449.6676 14646.1844 13806.6336 12943.4459 12059.7291 11155.4832 +#> 84 85 86 87 88 89 90 +#> 10230.7082 9293.0808 8373.3085 7479.0681 6610.3596 5767.1829 4956.2991 +#> 91 92 93 94 95 96 97 +#> 4204.7519 3519.3024 2899.9505 2346.6962 1858.6909 1432.5399 1067.3944 +#> 98 99 100 101 102 103 104 +#> 763.2546 520.1203 338.1613 218.0567 159.9760 163.9193 229.8867
                    # or leave open age group in tact graduate_mono(Value, OAG = TRUE) -
                    #> 0 1 2 3 4 5 6 7 -#> 17689.096 17653.592 17671.344 17742.352 17866.616 17984.832 18067.544 18159.328 -#> 8 9 10 11 12 13 14 15 -#> 18260.184 18370.112 18475.416 18574.672 18680.864 18793.992 18914.056 19033.000 -#> 16 17 18 19 20 21 22 23 -#> 19147.320 19263.320 19381.000 19500.360 19622.696 19743.912 19860.664 19972.952 -#> 24 25 26 27 28 29 30 31 -#> 20080.776 20196.120 20314.440 20421.480 20517.240 20601.720 20687.736 20781.272 -#> 32 33 34 35 36 37 38 39 -#> 20872.504 20961.432 21048.056 21128.104 21209.688 21301.136 21402.448 21513.624 -#> 40 41 42 43 44 45 46 47 -#> 21618.352 21716.504 21824.328 21941.824 22068.992 22222.144 22368.848 22476.576 -#> 48 49 50 51 52 53 54 55 -#> 22545.328 22575.104 22642.672 22736.224 22773.088 22753.264 22676.752 22644.112 -#> 56 57 58 59 60 61 62 63 -#> 22649.264 22588.608 22462.144 22269.872 22118.752 22011.504 21842.528 21611.824 -#> 64 65 66 67 68 69 70 71 -#> 21319.392 21034.528 20790.816 20535.752 20269.336 19991.568 19845.608 19707.216 -#> 72 73 74 75 76 77 78 79 -#> 19371.112 18837.296 18105.768 17465.200 16956.440 16311.240 15529.600 14611.520 -#> 80 81 82 83 84 85 86 87 -#> 13725.360 12930.160 12087.080 11196.120 10257.280 9269.208 8313.056 7430.752 -#> 88 89 90 91 92 93 94 95 -#> 6622.296 5887.688 5036.104 4135.288 3409.936 2860.048 2485.624 1914.640 -#> 96 97 98 99 100 -#> 1226.680 833.560 735.280 931.840 1110.000
                    -# Also accepts single ages: -Value <- structure(pop1m_ind, .Names = 0:100) - - if (FALSE) { - ages <- seq(0,100,5) - plot(graduate_mono(Value),xlab = 'Age', ylab = 'Counts', type = 'l',main = 'Ungrouped counts') - } +
                    #> 0 1 2 3 4 5 6 +#> 17565.4135 17641.9827 17721.5760 17804.1933 17889.8345 17978.5187 18070.3217 +#> 7 8 9 10 11 12 13 +#> 18165.2623 18263.3406 18364.5567 18468.8156 18575.7386 18685.2308 18797.2922 +#> 14 15 16 17 18 19 20 +#> 18911.9229 19028.7708 19146.4281 19264.5427 19383.1146 19502.1439 19621.3811 +#> 21 22 23 24 25 26 27 +#> 19739.8291 19857.2386 19973.6094 20088.9417 20202.5367 20311.5993 20415.4310 +#> 28 29 30 31 32 33 34 +#> 20514.0317 20607.4013 20696.3123 20783.8535 20870.7974 20957.1439 21042.8930 +#> 35 36 37 38 39 40 41 +#> 21128.6463 21216.8106 21307.9874 21402.1768 21499.3788 21600.4704 21708.9601 +#> 42 43 44 45 46 47 48 +#> 21825.7249 21950.7648 22084.0798 22221.4960 22346.3171 22454.3691 22545.6520 +#> 49 50 51 52 53 54 55 +#> 22620.1658 22677.5136 22716.1076 22735.5508 22735.8432 22716.9848 22678.8335 +#> 56 57 58 59 60 61 62 +#> 22620.8205 22542.8038 22444.7833 22326.7589 22186.6564 22016.1783 21813.2500 +#> 63 64 65 66 67 68 69 +#> 21577.8718 21310.0435 21019.5648 20745.6344 20498.0520 20276.8176 20081.9312 +#> 70 71 72 73 74 75 76 +#> 19893.0605 19628.8761 19269.0459 18813.5698 18262.4477 17625.0893 16939.1330 +#> 77 78 79 80 81 82 83 +#> 16213.9883 15449.6553 14646.1340 13806.5422 12943.3518 12059.6807 11155.5289 +#> 84 85 86 87 88 89 90 +#> 10230.8964 9293.4219 8373.6598 7479.2489 6610.1891 5766.4804 4955.0263 +#> 91 92 93 94 95 96 97 +#> 4203.4409 3518.6278 2900.5869 2349.3182 1863.4410 1437.4325 1069.9120 +#> 98 99 100 +#> 760.8795 510.3350 1110.0000
                    +data(pop1m_ind) +Value5 <- groupAges(pop1m_ind,Age=0:100,N=5) + +Value1 <- graduate_mono(Value = Value5, Age = names2age(Value5), OAG =TRUE) + +if (FALSE) { + + plot(seq(0,100,5),Value5 / 5, xlab = 'Age', ylab = 'Counts', type = 's') + lines(0:100,Value1) +}
                    @@ -88,7 +88,7 @@
                    • - +
                      • @@ -129,13 +129,13 @@
                      • - +
                      • - + github @@ -212,7 +212,7 @@

                        Value

                        Details

                        The pivotAge must be at least 10 years below the maximum age detected from -rownames(popmat), but not lower than 75. In the exact pivotAge, we may either take the Sprague estimates or the spline estimates, depending on which is larger, then the single-age estimates for this 5-year age group are rescaled to sum to the original total in Value. Higher ages are taken from the spline-based age splits. The spline results are derive from the "monoH.FC" method of splinefun() on the cumulative sum of the original age grouped data. One could use this function to perform the same closeout to Grabill estimates, if these are given via the pops argument. See examples. Note that the Grabill split method mixed with this closeout will not necessarily preserve the annual totals, and this function performs to rescaling. The open age group is preserved (and must be included in Value).

                        +rownames(popmat), but not lower than 75. In the exact pivotAge, we may either take the Sprague estimates or the spline estimates, depending on which is larger, then the single-age estimates for this 5-year age group are rescaled to sum to the original total in Value. Higher ages are taken from the spline-based age splits. The spline results are derive from the "hyman" method of splinefun() on the cumulative sum of the original age grouped data. One could use this function to perform the same closeout to Grabill estimates, if these are given via the pops argument. See examples. Note that the Grabill split method mixed with this closeout will not necessarily preserve the annual totals, and this function performs to rescaling. The open age group is preserved (and must be included in Value).

                        Examples

                        a5 <- as.integer(rownames(pop5_mat))
@@ -220,36 +220,40 @@ 
                        Examp
 closed.out           <- graduate_mono_closeout(Value = popvec, Age = a5, OAG = TRUE)
 sum(closed.out) - sum(popvec)
 
                        #> [1] 0
                        graduate_mono_closeout(Value = popvec, pivotAge = 85, Age = a5, OAG = TRUE)
-
                        #>          0          1          2          3          4          5          6 
-#> 12172.4912 11380.4480 10717.4480 10171.0000  9728.6128  9377.7952  9106.0560 
-#>          7          8          9         10         11         12         13 
-#>  8900.9040  8749.8480  8640.3968  8573.6144  8550.5648  8490.9808  8355.2608 
-#>         14         15         16         17         18         19         20 
-#>  8171.5792  8008.3808  7852.7792  7694.2592  7534.6752  7373.9056  7210.8208 
-#>         21         22         23         24         25         26         27 
-#>  7046.6624  6881.6064  6715.9664  6550.9440  6386.5216  6221.6160  6065.6560 
-#>         28         29         30         31         32         33         34 
-#>  5922.9920  5789.2144  5656.4784  5527.4512  5393.8752  5251.2032  5103.9920 
-#>         35         36         37         38         39         40         41 
-#>  4958.4544  4809.8784  4676.3904  4566.5184  4469.7584  4372.0480  4282.1632 
-#>         42         43         44         45         46         47         48 
-#>  4161.7152  3992.0912  3793.9824  3600.3712  3397.0752  3237.6592  3149.6752 
-#>         49         50         51         52         53         54         55 
-#>  3104.2192  3052.7728  3014.5648  2923.2688  2744.0448  2513.3488  2294.2640 
-#>         56         57         58         59         60         61         62 
-#>  2064.3184  1897.9424  1835.3744  1836.1008  1826.9504  1831.6544  1779.1824 
-#>         63         64         65         66         67         68         69 
-#>  1629.7344  1422.4784  1231.8448  1037.5024   891.8304   826.6464   813.1760 
-#>         70         71         72         73         74         75         76 
-#>   792.0960   776.7936   745.8176   683.0176   601.2752   529.6992   462.5600 
-#>         77         78         79         80         81         82         83 
-#>   401.2800   349.5120   304.9488   261.4336   219.9616   183.1696   151.6816 
-#>         84         85         86         87         88         89         90 
-#>   124.7536   108.4960    77.9920    56.3440    43.5520    39.6160    32.7520 
-#>         91         92         93         94         95         96         97 
-#>    19.9840    11.6080     7.6240     8.0320     7.4320     3.9040     1.8880 
-#>         98         99        100 
-#>     1.3840     2.3920     0.0000 
                        # giving a different single-age split to close out this way:
+
                        #>            0            1            2            3            4            5 
+#> 12172.491200 11380.448000 10717.448000 10171.000000  9728.612800  9377.795200 
+#>            6            7            8            9           10           11 
+#>  9106.056000  8900.904000  8749.848000  8640.396800  8573.614400  8550.564800 
+#>           12           13           14           15           16           17 
+#>  8490.980800  8355.260800  8171.579200  8008.380800  7852.779200  7694.259200 
+#>           18           19           20           21           22           23 
+#>  7534.675200  7373.905600  7210.820800  7046.662400  6881.606400  6715.966400 
+#>           24           25           26           27           28           29 
+#>  6550.944000  6386.521600  6221.616000  6065.656000  5922.992000  5789.214400 
+#>           30           31           32           33           34           35 
+#>  5656.478400  5527.451200  5393.875200  5251.203200  5103.992000  4958.454400 
+#>           36           37           38           39           40           41 
+#>  4809.878400  4676.390400  4566.518400  4469.758400  4372.048000  4282.163200 
+#>           42           43           44           45           46           47 
+#>  4161.715200  3992.091200  3793.982400  3600.371200  3397.075200  3237.659200 
+#>           48           49           50           51           52           53 
+#>  3149.675200  3104.219200  3052.772800  3014.564800  2923.268800  2744.044800 
+#>           54           55           56           57           58           59 
+#>  2513.348800  2294.264000  2064.318400  1897.942400  1835.374400  1836.100800 
+#>           60           61           62           63           64           65 
+#>  1826.950400  1831.654400  1779.182400  1629.734400  1422.478400  1231.844800 
+#>           66           67           68           69           70           71 
+#>  1037.502400   891.830400   826.646400   813.176000   792.096000   776.793600 
+#>           72           73           74           75           76           77 
+#>   745.817600   683.017600   601.275200   529.699200   462.560000   401.280000 
+#>           78           79           80           81           82           83 
+#>   349.512000   304.948800   261.433600   219.961600   183.169600   151.681600 
+#>           84           85           86           87           88           89 
+#>   124.753600   101.692205    79.932348    61.686246    46.953898    35.735304 
+#>           90           91           92           93           94           95 
+#>    27.603433    20.850155    15.048439    10.198284     6.299690     3.438064 
+#>           96           97           98           99          100 
+#>     1.955032     1.936000     3.380968     6.289936     0.000000 
                        # giving a different single-age split to close out this way:
 popg                 <- graduate_grabill(Value = popvec, Age = a5, OAG = TRUE)
 grabill.closed.out   <- graduate_mono_closeout(Value = popvec, Age = a5, pops = popg)
 # totals not necessarily preserved if mixed w Grabill
@@ -258,7 +262,7 @@ 
                        Examp
 # one may wish to instead rescale results colSums() of
 # popg at age pivotAge and higher.
 sum(grabill.closed.out) - sum(popvec)
-
                        #> [1] -22.01899
                        # also works on an age-labelled vector of data
+
                        #> [1] -22.01899
                        # also works on an age-labeled vector of data
 closed.vec           <- graduate_mono_closeout(popvec, Age = a5, OAG = TRUE)
 # let's compare this one with sprague()
 simple.vec           <- graduate_sprague(popvec, Age = a5, OAG = TRUE)
diff --git a/docs/reference/graduate_pclm.html b/docs/reference/graduate_pclm.html
index bd1561314..60994af99 100644
--- a/docs/reference/graduate_pclm.html
+++ b/docs/reference/graduate_pclm.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
                        
 
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                            • 
 
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     github
   
@@ -162,7 +162,7 @@ 
                            wrapper for ungroup::pclm method of splitting binned counts
                            This is exactly the function pclm() from the ungroup package, except with arguments using standard DemoTools argument names.
                            
     
 
-    
                            graduate_pclm(Value, Age, OAnew = max(Age), ...)
                            
+    
                            graduate_pclm(Value, Age, AgeInt, OAnew = max(Age), OAG = TRUE, ...)
                            
 
     
                            Arguments

                    Value

                    numeric vector, presumably counts in grouped ages
                    AgeInt

                    integer vector, age interval widths

                    Age

                    integer vector, lower bounds of age groups
                    AgeInt

                    integer vector, age interval widths

                    OAG

                    logical, default = TRUE is the final age group open?
                    @@ -175,10 +175,18 @@

                    Arg

                    + + + + + + + + @@ -188,6 +196,8 @@

                    Arg

                    Details

                    The PCLM method can also be used to graduate rates using an offset if both numerators and denominators are available. In this case Value is the event count and offset is person years of exposure. The denominator must match the length of Value or else the length of the final single age result length(min(Age):OAnew). This method can be used to redistribute counts in the open age group if OAnew gives sufficient space. Likewise, it can give a rate extrapolation beyond the open age.

                    +

                    If there are 0s in Value, these are replaced with a small value prior to fitting. If negatives result from the pclm fit, we retry after multiplying Value by 10, 100, or 1000, as sometimes a temporary rescale for fitting can help performance.

                    +

                    Age be any age range, it does not need to start at 0.

                    References

                    Pascariu MD, Dańko MJ, Schöley J, Rizzi S (2018). @@ -205,7 +215,8 @@

                    Examp 
                    a5  <- seq(0,100,by=5)
 p5  <- pop5_mat[, 1]
 p1  <- graduate_pclm(Value = p5, Age = a5)
-p1s <- graduate_sprague(Value = p5, Age = a5)
+
                    #> 
+#> 0s detected in Value, replacing with .01
                    p1s <- graduate_sprague(Value = p5, Age = a5)
 if (FALSE) {
 plot(a5, p5/5, type = "s",xlim=c(40,60),ylim=c(2000,4000))
 lines(0:100, p1, lwd = 2, col = "red")
diff --git a/docs/reference/graduate_sprague.html b/docs/reference/graduate_sprague.html
index 7138a3af1..c87378119 100644
--- a/docs/reference/graduate_sprague.html
+++ b/docs/reference/graduate_sprague.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
                    
 
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@@ -162,7 +162,7 @@ 
                        The basic Sprague age-splitting method.
                        
     
                        This method is used to interpolate counts based on the Sprague formula. It is based on the first stage of the Sprague R script prepared by Thomas Buettner and Patrick Gerland, itself based on the description in Siegel and Swanson, 2004, p. 727.
                        
     
 
-    
                        graduate_sprague(Value, Age, OAG = TRUE)
                        
+    
                        graduate_sprague(Value, Age, AgeInt, OAG = TRUE)
                        
 
     
                        Arguments

                    Age

                    integer vector, lower bounds of age groups
                    AgeInt

                    integer vector, age interval widths

                    OAnew

                    integer, optional new open age, higher than max(Age). See details.
                    OAG

                    logical, default = TRUE is the final age group open?

                    ...

                    further arguments passed to ungroup::pclm()
                    @@ -175,6 +175,10 @@

                    Arg

                    + + + + @@ -186,7 +190,7 @@

                    Value

                    Numeric vector of counts split into single ages.

                    Details

                    -

                    Ages should refer to lower age bounds, ending in the open age group in the last row (not a closed terminal age). Dimension labelling is necessary. There must be at least six age groups (including the open group). One year of data will work as well, as long as it's given as or coercible to a single-column matrix. This method may produce negative values, most likely in the youngest or oldest ages. This case is dealt with in the graduate() wrapper function but not in this function.

                    +

                    Ages should refer to lower age bounds, ending in the open age group in the last row (not a closed terminal age). Dimension labeling is necessary. There must be at least six age groups (including the open group). One year of data will work as well, as long as it's given as or coercible to a single-column matrix. This method may produce negative values, most likely in the youngest or oldest ages. This case is dealt with in the graduate() wrapper function but not in this function.

                    If the highest age does not end in a 0 or 5, and OAG == TRUE, then the open age will be grouped down to the next highest age ending in 0 or 5. If the highest age does not end in a 0 or 5, and OAG == FALSE, then results extend to single ages covering the entire 5-year age group.

                    References

                    diff --git a/docs/reference/graduate_sprague_expand.html b/docs/reference/graduate_sprague_expand.html index 71ec8e8bc..40fd40d59 100644 --- a/docs/reference/graduate_sprague_expand.html +++ b/docs/reference/graduate_sprague_expand.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                    • - +
                      • @@ -129,13 +129,13 @@
                      • - +
                      • - + github diff --git a/docs/reference/graduate_uniform.html b/docs/reference/graduate_uniform.html index 1e11f154d..23402c0da 100644 --- a/docs/reference/graduate_uniform.html +++ b/docs/reference/graduate_uniform.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                        • - +
                          • @@ -129,13 +129,13 @@
                          • - +
                          • - + github @@ -162,7 +162,7 @@

                            Convert arbitrary age groupings into single years of age.

                            Uniformly splits aggregate counts in age groups into single year age groups.

                            - 
                            graduate_uniform(Value, AgeInt, Age, OAG = TRUE, OAvalue = 1)
                            + 
                            graduate_uniform(Value, Age, AgeInt, OAG = TRUE, OAvalue = 1)

                            Arguments

                    Age

                    integer vector, lower bounds of age groups
                    AgeInt

                    integer vector, age interval widths

                    OAG

                    logical, default = TRUE is the final age group open?
                    @@ -171,14 +171,14 @@

                    Arg

                    - - - - + + + + @@ -194,7 +194,7 @@

                    Value

                    Numeric vector of counts for single year age groups.

                    Details

                    -

                    Assumes that the population is uniformly distributed across each age interval, and that initial age intervals are integers greater than or equal to 1. If AgeInt is given, its final value is used as the interval for the final age group. If AgeInt is missing, then Age must be given, and the open age group is by default preserved OAvalue rather than split. To instead split the final age group into, e.g., a 5-year age class, either give AgeInt, or give Age, OAG = TRUE, and OAvalue = 5.

                    +

                    Assumes that the population is uniformly distributed across each age interval, and that initial age intervals are integers greater than or equal to 1. If AgeInt is given, its final value is used as the interval for the final age group. If AgeInt is missing, then Age must be given, and the open age group is by default preserved OAvalue rather than split. To instead split the final age group into, e.g., a 5-year age class, either give AgeInt, or give Age, OAG = TRUE, and OAvalue = 5. Age be any age range, it does not need to start at 0.

                    Examples

                    MalePop <- c(9544406,7471790,11590109,11881844,11872503,12968350,
diff --git a/docs/reference/groupAges.html b/docs/reference/groupAges.html
index 05b9f3f75..6bb1aae18 100644
--- a/docs/reference/groupAges.html
+++ b/docs/reference/groupAges.html
@@ -80,7 +80,7 @@
       
       
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@@ -206,7 +206,7 @@ 
                        Value
                        
     
                        Details
                        
 
     
                        If you shift the groupings, then the first age groups may have a negative lower bound
-(for example of -5). These counts would be discarded for the osculatory version of Sprague smoothing,
+(for example of -5). These counts would be discarded for the oscillatory version of Sprague smoothing,
 for example, but they are preserved in this function. The important thing to know is that if you shift
 the groups, the first and last groups will not be N years wide. For example if shiftdown is 1, the first age group is 4-ages wide. The ages themselves are not returned,
 but they are the name attribute of the output count vector. Note this will also correctly group abridged ages
diff --git a/docs/reference/groupOAG.html b/docs/reference/groupOAG.html
index e62f9a40b..a4bea3fd8 100644
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diff --git a/docs/reference/index.html b/docs/reference/index.html
index cb710333b..fb4ec87b1 100644
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@@ -79,7 +79,7 @@
       
       
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@@ -87,7 +87,7 @@
                    + + + + + + + + + + + + + + + + + + + + + + + + @@ -396,17 +432,39 @@

                    interp_coh()

                    -

                    + + + + + + + + + + + + + + + + + + + + @@ -729,7 +794,7 @@

                    lt_ambiguous()

                    -

                    + - + - + - + - + + + + + @@ -216,7 +221,7 @@

                    R

                    United States Census Bureau (2017). “Population Analysis System (PAS) Software.” -https://www.census.gov/data/software/pas.html.

                    +https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.

                    Author

                    Tim Riffe

                    diff --git a/docs/reference/interp_coh.html b/docs/reference/interp_coh.html new file mode 100644 index 000000000..24b2abb63 --- /dev/null +++ b/docs/reference/interp_coh.html @@ -0,0 +1,307 @@ + + + + + + + + +Cohort component intercensal interpolation — interp_coh • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                    +
                    + + + + +
                    + +
                    +
                    +
                    +

                    Cohort component intercensal interpolation

                    + Source: R/interp_coh.R + +
                    + +
                    +

                    Cohorts between two censuses are interpolated using a cohort component approach.

                    +
                    + + 
                    interp_coh(
+  c1,
+  c2,
+  date1,
+  date2,
+  age1 = 1:length(c1) - 1,
+  age2 = 1:length(c2) - 1,
+  dates_out = NULL,
+  lxMat = NULL,
+  age_lx = NULL,
+  dates_lx = NULL,
+  births = NULL,
+  years_births = NULL,
+  location = NULL,
+  sex = "both",
+  midyear = FALSE,
+  verbose = TRUE,
+  ...
+)
                    + +

                    Arguments

                    +
                    Value

                    numeric vector, presumably counts in grouped ages
                    AgeInt

                    integer vector, age interval widths

                    Age

                    integer vector, lower bounds of age groups
                    AgeInt

                    integer vector, age interval widths

                    OAG

                    logical, default = TRUE is the final age group open?

                    Calculate an abridged-age lifetable.

                    +

                    lt_single_mx()

                    +

                    calculate a single age lifetable

                    +

                    lt_single_qx()

                    +

                    calculate a single age lifetable from qx

                    +

                    lt_single2abridged()

                    +

                    calculate an abridged life table that is consistent with a life table by single year of age

                    +

                    lt_abridged2single()

                    +

                    create a life table by single year of age by graduating an abridged life table

                    +

                    lt_ambiguous()

                    +

                    calculate an abridged or single age lifetable from abridged or single age data

                    +

                    lt_smooth_ambiguous()

                    +

                    Smooth and apply lt_ambiguous

                    component-free intercensal cohort interpolation

                    Cohort component intercensal interpolation

                    Extrapolation

                    -

                    Functions to interpolate/extrapolate rates or counts - ‘interp_lc_lim’ - ‘lt_rule_m_extrapolate’ - ‘OPAG’

                    +

                    Functions to interpolate/extrapolate rates or counts
                    +

                    interp_lc_lim()

                    +

                    Lee-Carter method with limited data.

                    +

                    lt_rule_m_extrapolate()

                    +

                    Extrapolate old-age human mortality curve using mortality laws

                    +

                    OPAG()

                    +

                    Redistribute population over a specified age based on a stable standard fit to the data
                    @@ -421,6 +479,13 @@

                    +

                    		+

                    mig_beta()

                    +

                    Estimate intercensal migration by comparing census population, by age and +sex, to the results of a RUP projection.

                    mig_calculate_rc()

                    calculate an abidged or single age lifetable from abridged or sinlge age data

                    calculate an abridged or single age lifetable from abridged or single age data
                    @@ -959,7 +1024,7 @@

                    pop1m_ind

                    Indian male population 1991

                    Indian male population 1971
                    @@ -1123,7 +1188,7 @@

                    OPAG_simple()

                    redistripute an open age group count over higher ages proportional to an arbitrary standard

                    redistribute an open age group count over higher ages proportional to an arbitrary standard
                    @@ -1135,7 +1200,7 @@

                    downloadnLx()

                    Extract Lx estimates from WPP2019

                    Extract Lx estimates from WPP2019. Mainly an util function for other ones.
                    diff --git a/docs/reference/inferAgeIntAbr.html b/docs/reference/inferAgeIntAbr.html index a807ef27c..9ee5fde1f 100644 --- a/docs/reference/inferAgeIntAbr.html +++ b/docs/reference/inferAgeIntAbr.html @@ -82,7 +82,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -90,7 +90,7 @@
                    • - +
                      • @@ -131,13 +131,13 @@
                      • - +
                      • - + github diff --git a/docs/reference/int2age.html b/docs/reference/int2age.html index 77af17ee9..11a30c29b 100644 --- a/docs/reference/int2age.html +++ b/docs/reference/int2age.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                        • - +
                          • @@ -129,13 +129,13 @@
                          • - +
                          • - + github diff --git a/docs/reference/int2ageN.html b/docs/reference/int2ageN.html index d7c75d2a4..c740c5a78 100644 --- a/docs/reference/int2ageN.html +++ b/docs/reference/int2ageN.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                            • - +
                              • @@ -129,13 +129,13 @@
                              • - +
                              • - + github diff --git a/docs/reference/interp.html b/docs/reference/interp.html index b30f436dd..6b6991dc9 100644 --- a/docs/reference/interp.html +++ b/docs/reference/interp.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                • - +
                                  • @@ -129,13 +129,13 @@
                                  • - +
                                  • - + github @@ -169,6 +169,7 @@

                                    Interpolate between two population age distributions.

                                    method = c("linear", "exponential", "power"), power = 2, extrap = FALSE, + negatives = FALSE, ... ) @@ -197,7 +198,11 @@

                                    Arg

                    extrap

                    logical. In case datesOut is out of range of datesIn, do extrapolation using slope in extreme pairwise. Deafult FALSE.

                    logical. In case datesOut is out of range of datesIn, do extrapolation using slope in extreme pairwise. Default FALSE.

                    negatives

                    logical. In case negative output are accepted, set to TRUE. Default FALSE.
                    ...
                    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                    c1

                    numeric vector. The first (left) census in single age groups

                    c2

                    numeric vector. The second (right) census in single age groups

                    date1

                    reference date of c1`. Either a Date class object or an unambiguous character string in the format "YYYY-MM-DD".

                    date2

                    reference date of c2`. Either a Date class object or an unambiguous character string in the format "YYYY-MM-DD".

                    age1

                    integer vector. single ages of c1

                    age2

                    integer vector. single ages of c2

                    dates_out

                    vector of desired output dates coercible to numeric using dec.date()

                    lxMat

                    numeric matrix containing lifetable survivorship, l(x). Each row is an age group and each column a time point. At least two intercensal time points needed.

                    age_lx

                    integer vector. Age classes in lxMat

                    dates_lx

                    date, character, or numeric vector of the column time points for lxMat. If these are calendar-year estimates, then you can choose mid-year time points

                    births

                    integer vector. Raw birth counts for the corresponding (sub)-population, one value per each year of the intercensal period including both census years. The first and last years should include all births in the given year; don't discount them in advance.

                    years_births

                    numeric vector of calendar years of births.

                    location

                    UN Pop Division LocName or LocID

                    sex

                    character string, either "male", "female", or "both"

                    midyear

                    logical. FALSE means all Jan 1 dates between date1 and date2 are returned. TRUE means all July 1 intercensal dates are returned.

                    verbose

                    logical. Shall we send informative messages to the console?

                    ...

                    optional arguments passed to

                    + +

                    Details

                    + +

                    The basic approach is to i) align the censuses to single-year cohorts by blending adjacent ages assuming that the birthdays in each age group are uniformly distributed through the year ii) decrement the first census forward within cohorts using period-cohort survival probabilities calculated from (supplied or downloaded) l(x) values, iii) redistribute the residual at the time of the second census uniformly over time within cohorts. These steps are always done on Jan 1 reference dates. If midyear = TRUE, then we do within-age band arithmetic interpolation to July 1 reference dates.

                    +

                    See also

                    + +

                    interp

                    + +

                    Examples

                    + 
                    
+if (FALSE) {
+interp_coh(
+location = "Russian Federation",
+sex = "male",
+c1 = pop1m_rus2002,
+c2 = pop1m_rus2010,
+date1 = "2002-10-16",
+date2 = "2010-10-25",
+age1 = 0:100,
+births = c(719511L, 760934L, 772973L, 749554L, 760831L, 828772L, 880543L, 905380L, 919639L)
+)
+}
+
                    +
                    + +
                    + + +
                    +
                    +

                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                    +
                    + +
                    +

                    Site built with pkgdown 1.6.1.

                    +
                    + +
                    +
                    + + + + + + + + diff --git a/docs/reference/interp_coh_bare.html b/docs/reference/interp_coh_bare.html new file mode 100644 index 000000000..3b9f82b12 --- /dev/null +++ b/docs/reference/interp_coh_bare.html @@ -0,0 +1,231 @@ + + + + + + + + +component-free intercensalcohort interpolation — interp_coh_bare • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                    +
                    + + + + +
                    + +
                    +
                    +
                    +

                    component-free intercensalcohort interpolation

                    + Source: R/interp_coh.R + +
                    + +
                    +

                    Cohorts between two censuses are interpolated flexibly using linear, exponential, or power rules. The lower and upper intercensal triangles are filled using within-age interpolation. This function is experimental and still in development.

                    +
                    + + 
                    interp_coh_bare(c1, c2, date1, date2, age1, age2, ...)
                    + +

                    Arguments

                    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                    c1

                    numeric vector. The first (left) census in single age groups

                    c2

                    numeric vector. The second (right) census in single age groups

                    date1

                    reference date of c1`. Either a Date class object or an unambiguous character string in the format "YYYY-MM-DD".

                    date2

                    reference date of c2`. Either a Date class object or an unambiguous character string in the format "YYYY-MM-DD".

                    age1

                    integer vector. single ages of c1

                    age2

                    integer vector. single ages of c2

                    ...

                    extra arguments passed to interp(). Not currently in use.

                    + +

                    See also

                    + +

                    interp

                    + +
                    + +
                    + + +
                    +
                    +

                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                    +
                    + +
                    +

                    Site built with pkgdown 1.6.1.

                    +
                    + +
                    +
                    + + + + + + + + diff --git a/docs/reference/interp_lc_lim.html b/docs/reference/interp_lc_lim.html new file mode 100644 index 000000000..34e781153 --- /dev/null +++ b/docs/reference/interp_lc_lim.html @@ -0,0 +1,368 @@ + + + + + + + + +Lee-Carter method with limited data. — interp_lc_lim • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                    +
                    + + + + +
                    + +
                    +
                    +
                    +

                    Lee-Carter method with limited data.

                    + Source: R/interp_lc_lim.R + +
                    + +
                    +

                    Given a data frame with dates, sex and mortality data by age (rates, conditioned probabilities of death +or survival function), this function interpolate/extrapolate life tables +using the method for limited data suggested by Li et. al (2004) (at least three observed years).

                    +
                    + + 
                    interp_lc_lim(
+  input = NULL,
+  dates_out = dates_in,
+  Single = FALSE,
+  dates_e0 = NULL,
+  e0_Males = NULL,
+  e0_Females = NULL,
+  prev_divergence = FALSE,
+  OAG = TRUE,
+  verbose = TRUE,
+  SVD = FALSE,
+  ...
+)
                    + +

                    Arguments

                    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                    input

                    data.frame with cols: Date, Sex, Age, nMx (opt), nqx (opt), lx (opt)

                    dates_out

                    numeric. Vector of decimal years to interpolate or extrapolate.

                    Single

                    logical. Whether or not the lifetable output is by single ages.

                    dates_e0

                    numeric. Vector of decimal years where "e_0" should be fitted when apply method.

                    e0_Males

                    numeric. Vector of life expectancy by year to be fitted. Same length than "dates_e0".

                    e0_Females

                    numeric. Vector of life expectancy by year to be fitted. Same length than "dates_e0".

                    prev_divergence

                    logical. Whether or not prevent divergence and sex crossover. Default FALSE.

                    OAG

                    logical. Whether or not the last element of nMx (or nqx or lx) is an open age group. Default TRUE.

                    verbose

                    logical. Default FALSE.

                    SVD

                    logical. Use Singular Value Decomposition for estimate b and k or Maximum Likelihood Estimation. Default FALSE for Maximum Likelihood Estimation.

                    ...

                    Other arguments to be passed on to the lt_abridged function.

                    + +

                    Value

                    + +

                    List with:

                      +

                    • Interpolated/extrapolated lifetables in a data.frame with columns:

                        +

                      • Date numeric. Dates included in dates_out,

                        • +

                      • Sex character. Male "m" or female "f",

                        • +

                      • Age integer. Lower bound of abridged age class,

                        • +

                      • `AgeInt`` integer. Age class widths.

                        • +

                      • nMx numeric. Age-specific central death rates.

                        • +

                      • nAx numeric. Average time spent in interval by those deceased in interval.

                        • +

                      • nqx numeric. Age-specific conditional death probabilities.

                        • +

                      • lx numeric. Lifetable survivorship

                        • +

                      • ndx numeric. Lifetable deaths distribution.

                        • +

                      • nLx numeric. Lifetable exposure.

                        • +

                      • Sx numeric. Survivor ratios in uniform 5-year age groups.

                        • +

                      • Tx numeric. Lifetable total years left to live above age x.

                        • +

                      • ex numeric. Age-specific remaining life expectancy.

                        • +

                      • List with estimated Lee-Carter parameters for each sex:

                        • +

                      • kt numeric time vector. Time trend in mortality level.

                        • +

                      • ax numeric age vector. Average time of log(m_{x,t}).

                        • +

                      • bx numeric age vector. Pattern of change in response to kt.

                        • +
                      • +
                    + +

                    Details

                    + +

                    Based on spreadsheet "Li_2018_Limited_Lee-Carter-v4.xlsm" from UN. +Useful for abridged or single ages, and allows output in both formats also. +One option is the use of non-divergent method for sex coherency (Li & Lee, 2005). +The other is the possibility of fitting "k" to replicate "e_0" at some given dates.

                    +

                    Note

                    + +

                    Draft Version

                    +

                    References

                    + +

                    Li N, Lee R (2005). +“Coherent mortality forecasts for a group of populations: An extension of the Lee-Carter method.” +Demography, 42(3), 575. +doi: 10.1353/dem.2005.0021 +. + +Li N, Lee R, Tuljapurkar S (2004). +“Using the Lee-Carter Method to Forecast Mortality for Populations with Limited Data\(\ast\).” +Int. Stat. Rev., 72(1), 19--36. +ISSN 0306-7734, doi: 10.1111/j.1751-5823.2004.tb00221.x +.

                    +

                    See also

                    + +

                    lt_abridged

                    + +

                    Examples

                    + 
                    # mortality rates from Sweden, for specific dates
+
+# needs mortality rates in this dates: 
+dates_out <- as.Date(paste0(seq(1948,2018,5),"-07-01"))
+
+# apply LC with limited data to extrap/interpolate
+lc_lim_data <- interp_lc_lim(input = mA_swe, dates_out = dates_out, OAG = FALSE)$lt_hat
+
+if (FALSE) {
+lc_lim_data %>% ggplot(aes(Age,nMx,col=factor(round(Date,1)))) +
+  geom_step() + scale_color_viridis_d() + 
+  scale_y_log10() + theme_classic() + facet_wrap(~Sex)
+}
+
+# with simple ages as output
+lc_lim_data_single <- interp_lc_lim(input = mA_swe, dates_out = dates_out, OAG = FALSE,
+                                    Single = TRUE)$lt_hat
+
+if (FALSE) {
+lc_lim_data_single %>% ggplot(aes(Age,nMx,col=factor(round(Date,1)))) +
+  geom_step() + scale_color_viridis_d() + 
+  scale_y_log10() + theme_classic() + facet_wrap(~Sex)
+}
+
+# Avoiding cross-over between sex.
+lc_lim_nondiv <- interp_lc_lim(input = mA_swe, dates_out = dates_out, OAG = FALSE,
+                               prev_divergence = TRUE)$lt_hat
+if (FALSE) {
+lc_lim_nondiv %>% ggplot(aes(Age,nMx,col=factor(round(Date,1)))) +
+  geom_step() + scale_color_viridis_d() + 
+  scale_y_log10() + theme_classic() + facet_wrap(~Sex)
+}
+
+# Fitting information about e0 in Sweden for past years.
+lc_lim_fite0 <- interp_lc_lim(input = mA_swe, dates_out = dates_out, OAG = FALSE,
+                              dates_e0 = unique(e0_swe$Date),
+                              e0_Males = e0_swe$e0[e0_swe$Sex=="m"], 
+                              e0_Females = e0_swe$e0[e0_swe$Sex=="f"])$lt_hat
+if (FALSE) {                               
+ggplot() + 
+  geom_point(data = e0_swe, aes(Date,e0,col=factor(Sex)))+
+  geom_line(data = lc_lim_fite0[lc_lim_fite0$Age==0,], aes(Date,ex,col=factor(Sex)))+
+  labs(color = "Sex")+
+  theme_classic()
+}
+
+# smooth and/or extend open age group, in this case input is for 80+, and chosen law is Makeham.
+lc_lim_extOAg <- interp_lc_lim(input = mA_swe[mA_swe$Age<=80,], dates_out = dates_out,
+                               OAG = FALSE,
+                               OAnew=100,
+                               extrapLaw = "makeham")$lt_hat
+if (FALSE) {
+ggplot() + 
+  geom_step(data = lc_lim_extOAg, aes(Age,nMx,col=factor(round(Date,1)))) +
+  scale_y_log10() + scale_color_viridis_d() + theme_classic() + facet_wrap(~Sex)
+  }
+#End
+
                    +
                    + +
                    + + +
                    +
                    +

                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                    +
                    + +
                    +

                    Site built with pkgdown 1.6.1.

                    +
                    + +
                    +
                    + + + + + + + + diff --git a/docs/reference/interp_lc_lim_abk_m.html b/docs/reference/interp_lc_lim_abk_m.html new file mode 100644 index 000000000..bf5072706 --- /dev/null +++ b/docs/reference/interp_lc_lim_abk_m.html @@ -0,0 +1,212 @@ + + + + + + + + +wrapper fun for "interp_lc_lim_estimate" function — interp_lc_lim_abk_m • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                    +
                    + + + + +
                    + +
                    +
                    +
                    +

                    wrapper fun for "interp_lc_lim_estimate" function

                    + Source: R/interp_lc_lim.R + +
                    + +
                    +

                    wrapper fun to estimate rates from LC parameters

                    +
                    + + 
                    interp_lc_lim_abk_m(k, ax, bx)
                    + +

                    Arguments

                    + + + + + + + + + + + + + + +
                    k

                    numeric. k parameter from LC model.

                    ax

                    numeric. Vector (same length of age) of parameters from LC model.

                    bx

                    numeric. Vector (same length of age) of parameters from LC model.

                    + + +
                    + +
                    + + +
                    +
                    +

                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                    +
                    + +
                    +

                    Site built with pkgdown 1.6.1.

                    +
                    + +
                    +
                    + + + + + + + + diff --git a/docs/reference/interp_lc_lim_estimate.html b/docs/reference/interp_lc_lim_estimate.html new file mode 100644 index 000000000..79e7f9543 --- /dev/null +++ b/docs/reference/interp_lc_lim_estimate.html @@ -0,0 +1,226 @@ + + + + + + + + +Estimate LC with limited data parameters — interp_lc_lim_estimate • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                    +
                    + + + + +
                    + +
                    +
                    +
                    +

                    Estimate LC with limited data parameters

                    + Source: R/interp_lc_lim.R + +
                    + +
                    +

                    Estimate LC with limited data from a matrix of rates (age by dates).

                    +
                    + + 
                    interp_lc_lim_estimate(M, dates_in, dates_out, SVD = F)
                    + +

                    Arguments

                    + + + + + + + + + + + + + + + + + + +
                    M

                    numeric. Matrix with many rows as ages and columns as dates_in.

                    dates_in

                    numeric. Vector of dates with input rates.

                    dates_out

                    numeric. Vector of dates for estimate a set of rates.

                    SVD

                    logical. Use Singular Value Decomposition for estimate b and k or Maximum Likelihood Estimation. Default FALSE for Maximum Likelihood Estimation.

                    + +

                    Details

                    + +

                    SVD for ax and bx. Fit a simple linear model for k and interpolate/extrapolate for objective dates.

                    +

                    References

                    + +

                    Li N, Lee R, Tuljapurkar S (2004). +“Using the Lee-Carter Method to Forecast Mortality for Populations with Limited Data\(\ast\).” +Int. Stat. Rev., 72(1), 19--36. +ISSN 0306-7734, doi: 10.1111/j.1751-5823.2004.tb00221.x +.

                    + +
                    + +
                    + + +
                    +
                    +

                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                    +
                    + +
                    +

                    Site built with pkgdown 1.6.1.

                    +
                    + +
                    +
                    + + + + + + + + diff --git a/docs/reference/interp_lc_lim_group.html b/docs/reference/interp_lc_lim_group.html new file mode 100644 index 000000000..c7c68c851 --- /dev/null +++ b/docs/reference/interp_lc_lim_group.html @@ -0,0 +1,334 @@ + + + + + + + + +Lee-Carter method with limited data for groups. — interp_lc_lim_group • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                    +
                    + + + + +
                    + +
                    +
                    +
                    +

                    Lee-Carter method with limited data for groups.

                    + Source: R/interp_lc_lim_group.R + +
                    + +
                    +

                    Given a data frame with groups (country, region, sex), dates, sex and mortality data by age (rates, conditioned probabilities of death +or survival function), this function interpolate/extrapolate life tables +using the method for limited data suggested by Li et. al (2004) (at least three observed years).

                    +
                    + + 
                    interp_lc_lim_group(
+  input = NULL,
+  dates_out = NULL,
+  Single = FALSE,
+  input_e0 = NULL,
+  prev_divergence = FALSE,
+  weights = NULL,
+  OAG = TRUE,
+  verbose = TRUE,
+  SVD = FALSE,
+  ...
+)
                    + +

                    Arguments

                    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                    input

                    data.frame. Columns: id, Date, Sex, Age, nMx (opt), nqx (opt), lx (opt). +The first column (id) can be a numeric index or character vector identifying each group.

                    dates_out

                    numeric. Vector of decimal years to interpolate or extrapolate.

                    Single

                    logical. Whether or not the lifetable output is by single ages.

                    input_e0

                    data.frame with cols: id, Date, Sex and "e_0". This should be fitted when apply method.

                    prev_divergence

                    logical. Whether or not prevent divergence and sex crossover between groups. Default FALSE.

                    weights

                    list. For prev_divergence option. A double for each element of a list with names as id columns. Should sum up to 1. Default: same weight for each group.

                    OAG

                    logical. Whether or not the last element of nMx (or nqx or lx) is an open age group. Default TRUE.

                    verbose

                    logical. Default FALSE.

                    SVD

                    logical. Use Singular Value Decomposition for estimate b and k or Maximum Likelihood Estimation. Default FALSE for Maximum Likelihood Estimation.

                    ...

                    Other arguments to be passed on to the lt_abridged function.

                    + +

                    Value

                    + +

                    List with:

                      +

                    • Lifetable in a data.frame with columns:

                        +

                      • Date numeric. Dates included in dates_out,

                        • +

                      • Sex character. Male "m" or female "f",

                        • +

                      • Age integer. Lower bound of abridged age class,

                        • +

                      • `AgeInt`` integer. Age class widths.

                        • +

                      • nMx numeric. Age-specific central death rates.

                        • +

                      • nAx numeric. Average time spent in interval by those deceased in interval.

                        • +

                      • nqx numeric. Age-specific conditional death probabilities.

                        • +

                      • lx numeric. Lifetable survivorship

                        • +

                      • ndx numeric. Lifetable deaths distribution.

                        • +

                      • nLx numeric. Lifetable exposure.

                        • +

                      • Sx numeric. Survivor ratios in uniform 5-year age groups.

                        • +

                      • Tx numeric. Lifetable total years left to live above age x.

                        • +

                      • ex numeric. Age-specific remaining life expectancy.

                        • +

                      • List with parameters estimated for each group:

                        • +

                      • kt numeric time vector. Time trend in mortality level.

                        • +

                      • ax numeric age vector. Average time of log(m_{x,t}).

                        • +

                      • bx numeric age vector. Pattern of change in response to kt.

                        • +
                      • +
                    + +

                    Details

                    + +

                    Based on spreadsheet "Li_2018_Limited_Lee-Carter-v4.xlsm" from UN. +Useful for abridged or single ages, and allows output in both formats also. +One option is the use of non-divergent method for sex coherency (Li & Lee, 2005). +The other is the possibility of fitting "k" to replicate "e_0" at some given dates. +id column in input argument works for separate between groups. In case only one population/sex is given, +is recommended to give some group name to id, if not the function will try to infer the case.

                    +

                    Note

                    + +

                    Draft Version

                    +

                    References

                    + +

                    Li N, Lee R (2005). +“Coherent mortality forecasts for a group of populations: An extension of the Lee-Carter method.” +Demography, 42(3), 575. +doi: 10.1353/dem.2005.0021 +. + +Li N, Lee R, Tuljapurkar S (2004). +“Using the Lee-Carter Method to Forecast Mortality for Populations with Limited Data\(\ast\).” +Int. Stat. Rev., 72(1), 19--36. +ISSN 0306-7734, doi: 10.1111/j.1751-5823.2004.tb00221.x +.

                    +

                    See also

                    + +

                    lt_abridged

                    + +

                    Examples

                    + 
                    # mortality rates from Sweden, for specific dates. Each sex a group.
+mA_swe$id = c(rep("A",nrow(mA_swe)/2),
+             rep("B",nrow(mA_swe)/2))
+
+# needs mortality rates in this dates: 
+dates_out <- as.Date(paste0(seq(1948,2018,5),"-07-01"))
+
+# apply LC with limited data to extrap/interpolate
+lc_lim_data <- interp_lc_lim_group(input = mA_swe, dates_out = dates_out)
+
+if (FALSE) {
+lc_lim_data[["lt_hat"]] %>% ggplot(aes(Age,nMx,col=factor(round(Date,1)))) +
+  geom_step() + scale_color_viridis_d() + 
+  scale_y_log10() + theme_classic() + facet_wrap(~Sex)
+}
+
+# avoid cross-over between groups
+lc_lim_data <- interp_lc_lim_group(input = mA_swe, dates_out = dates_out,
+                                    prev_divergence = TRUE, weights=list(A=.4,B=.6))
+
+if (FALSE) {
+lc_lim_data[["lt_hat"]] %>% ggplot(aes(Age,nMx,col=factor(round(Date,1)))) +
+  geom_step() + scale_color_viridis_d() + 
+  scale_y_log10() + theme_classic() + facet_wrap(~id)
+}
+
                    +
                    + +
                    + + +
                    +
                    +

                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                    +
                    + +
                    +

                    Site built with pkgdown 1.6.1.

                    +
                    + +
                    +
                    + + + + + + + + diff --git a/docs/reference/interp_lc_lim_kt_min.html b/docs/reference/interp_lc_lim_kt_min.html new file mode 100644 index 000000000..50773c5aa --- /dev/null +++ b/docs/reference/interp_lc_lim_kt_min.html @@ -0,0 +1,235 @@ + + + + + + + + +Optimize k — interp_lc_lim_kt_min • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                    +
                    + + + + +
                    + +
                    +
                    +
                    +

                    Optimize k

                    + Source: R/interp_lc_lim.R + +
                    + +
                    +

                    Optimize estimated k from LC with limited data model, +for fitting given e_0 at same dates

                    +
                    + + 
                    interp_lc_lim_kt_min(k, ax, bx, age, sex, e0_target, ...)
                    + +

                    Arguments

                    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                    k

                    numeric. k parameter from LC model.

                    ax

                    numeric. Vector (same length of age) of parameters from LC model.

                    bx

                    numeric. Vector (same length of age) of parameters from LC model.

                    age

                    numeric.

                    sex

                    numeric.

                    e0_target

                    numeric.

                    ...

                    Other arguments to be passed on to the lt_abridged function.

                    + +

                    Details

                    + +

                    Given LC parameters at some date, change a bit k for replicate already know e_0 values. +This is useful to give some sort of flexibility, and not follow strictly linear model implied in LC model, +but taking advantage of estimated structure (ax) and change by age (bx) for some trustable period.

                    + +
                    + +
                    + + +
                    +
                    +

                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                    +
                    + +
                    +

                    Site built with pkgdown 1.6.1.

                    +
                    + +
                    +
                    + + + + + + + + diff --git a/docs/reference/interpolatePop.html b/docs/reference/interpolatePop.html index a6e2a7317..b43e6cb5a 100644 --- a/docs/reference/interpolatePop.html +++ b/docs/reference/interpolatePop.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                    @@ -88,7 +88,7 @@
                    @@ -88,7 +88,7 @@
                    • - +
                      • @@ -129,13 +129,13 @@
                      • - +
                      • - + github diff --git a/docs/reference/is_age_coherent.html b/docs/reference/is_age_coherent.html index 7a26509da..e0bad8899 100644 --- a/docs/reference/is_age_coherent.html +++ b/docs/reference/is_age_coherent.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                    @@ -88,7 +88,7 @@
                    • - +
                      • @@ -129,13 +129,13 @@
                      • - +
                      • - + github @@ -186,7 +186,7 @@

                        Value

                        logical. TRUE if the arguments are considered consistent.

                        Details

                        -

                        If OAG is TRUE then AgeInt must be coded as NA. If Age is not sorted then we sort both Age and AgeInt, assuming that they are in matched order. This isn't incoherence per se, but a message is returned to the console.

                        +

                        If OAG is TRUE then AgeInt must be coded as NA. If Age is not sorted then we sort both Age and AgeInt, assuming that they are in matched order. This isn't incoherence in itself, but a message is returned to the console.

                        Examples

                        Age    <- 0:99
diff --git a/docs/reference/is_age_redundant.html b/docs/reference/is_age_redundant.html
index 76ac10a80..3301bc30a 100644
--- a/docs/reference/is_age_redundant.html
+++ b/docs/reference/is_age_redundant.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
                        
 
@@ -88,7 +88,7 @@
       
                          
         
                        • 
   
-    
+    
      
   
 
                          • 
@@ -129,13 +129,13 @@
       
                            
         
                          • 
   
-    
+    
      
   
 
                            • 
 
                          • 
   
-    
+    
      
     github
   
diff --git a/docs/reference/is_age_sequential.html b/docs/reference/is_age_sequential.html
index 668b87288..dd92d070f 100644
--- a/docs/reference/is_age_sequential.html
+++ b/docs/reference/is_age_sequential.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
                    @@ -88,7 +88,7 @@
                    • - +
                      • @@ -129,13 +129,13 @@
                      • - +
                      • - + github diff --git a/docs/reference/is_single.html b/docs/reference/is_single.html index 04b5d83d8..656176097 100644 --- a/docs/reference/is_single.html +++ b/docs/reference/is_single.html @@ -81,7 +81,7 @@ DemoTools - 01.13.20 + 01.13.76
                    @@ -89,7 +89,7 @@
                    • - +
                      • @@ -130,13 +130,13 @@
                      • - +
                      • - + github diff --git a/docs/reference/loess_smth1.html b/docs/reference/loess_smth1.html index b01c333c2..a403cd06a 100644 --- a/docs/reference/loess_smth1.html +++ b/docs/reference/loess_smth1.html @@ -81,7 +81,7 @@ DemoTools - 01.13.20 + 01.13.76
                    @@ -89,7 +89,7 @@
                    • - +
                      • @@ -130,13 +130,13 @@
                      • - +
                      • - + github diff --git a/docs/reference/lt_a_closeout.html b/docs/reference/lt_a_closeout.html index 42c680baa..40827119a 100644 --- a/docs/reference/lt_a_closeout.html +++ b/docs/reference/lt_a_closeout.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                    @@ -88,7 +88,7 @@
                    • - +
                      • @@ -129,13 +129,13 @@
                      • - +
                      • - + github diff --git a/docs/reference/lt_a_pas.html b/docs/reference/lt_a_pas.html index 23d6aa918..7c941156b 100644 --- a/docs/reference/lt_a_pas.html +++ b/docs/reference/lt_a_pas.html @@ -81,7 +81,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -89,7 +89,7 @@
                        • - +
                          • @@ -130,13 +130,13 @@
                          • - +
                          • - + github @@ -226,7 +226,7 @@

                            R United States Census Bureau (2017). “Population Analysis System (PAS) Software.” -https://www.census.gov/data/software/pas.html.

                            +https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.

                            Examples

                             Exposures <- c(100958,466275,624134,559559,446736,370653,301862,249409,
@@ -241,10 +241,10 @@ 
                            Examp
  AgeInt <- c(diff(Age), NA)
  nMx <- Deaths/Exposures
  lt_a_pas(nMx = nMx,AgeInt = AgeInt,Sex = 'm',region = 'n',OAG = TRUE)
-
                            #>  [1] 0.299100 1.614836 2.500000 2.500000 2.500000 2.500000 2.500000 2.500000
+
                            #>  [1] 0.299150 1.614835 2.500000 2.500000 2.500000 2.500000 2.500000 2.500000
 #>  [9] 2.500000 2.500000 2.500000 2.500000 2.500000 2.500000 2.500000 2.500000
 #> [17] 2.500000 2.500000 2.500000 2.500000 2.500000 3.333333
                             lt_a_pas(nMx = nMx,AgeInt = AgeInt,Sex = 'm',a0rule = "cd",region = 'n',OAG = TRUE)
-
                            #>  [1] 0.2750276 1.6148357 2.5000000 2.5000000 2.5000000 2.5000000 2.5000000
+
                            #>  [1] 0.2750276 1.6148347 2.5000000 2.5000000 2.5000000 2.5000000 2.5000000
 #>  [8] 2.5000000 2.5000000 2.5000000 2.5000000 2.5000000 2.5000000 2.5000000
 #> [15] 2.5000000 2.5000000 2.5000000 2.5000000 2.5000000 2.5000000 2.5000000
 #> [22] 3.3333333
                            diff --git a/docs/reference/lt_a_un.html b/docs/reference/lt_a_un.html index a12fe52b8..286acf0cb 100644 --- a/docs/reference/lt_a_un.html +++ b/docs/reference/lt_a_un.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                            • - +
                              • @@ -129,13 +129,13 @@
                              • - +
                              • - + github diff --git a/docs/reference/lt_abridged.html b/docs/reference/lt_abridged.html index ebc70c20a..573e832f9 100644 --- a/docs/reference/lt_abridged.html +++ b/docs/reference/lt_abridged.html @@ -81,7 +81,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -89,7 +89,7 @@
                                • - +
                                  • @@ -130,13 +130,13 @@
                                  • - +
                                  • - + github @@ -249,7 +249,7 @@

                                    Arg SRB -

                                    the sex ratio at birth (boys / girls), detault 1.05

                                    +

                                    the sex ratio at birth (boys / girls), default 1.05

                                    OAG @@ -262,7 +262,7 @@

                                    Arg extrapLaw

                                    character. If extrapolating, which parametric mortality law should be invoked? Options include -"Kannisto", "Kannisto_Makeham", "Makeham", "Gompertz", "GGompertz", "Beard", "Beard_Makeham", "Quadratic". Default "Kannisto". See details.

                                    +"Kannisto", "Kannisto_Makeham", "Makeham", "Gompertz", "GGompertz", "Beard", "Beard_Makeham", "Quadratic". Default "Kannisto" if the highest age is at least 90, otherwise "makeham". See details.

                                    extrapFrom @@ -312,7 +312,7 @@

                                    Details is aligned with the other columns in all 5-year age groups, but note the first two values have a slightly different age-interval interpretation: In Age 0, the interpretation is survival from birth until interval 0-4. -In Age 1, it is survival from 0-4 into 5-9. Therafter the age groups align. +In Age 1, it is survival from 0-4 into 5-9. Thereafter the age groups align. This column is required for population projections.

                                    References

                                    @@ -335,7 +335,7 @@

                                    R United States Census Bureau (2017). “Population Analysis System (PAS) Software.” -https://www.census.gov/data/software/pas.html.

                                    +https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.

                                    Examples

                                    # trial code from PAS LTPOPDTH, North, Males, IMR = .1
diff --git a/docs/reference/lt_abridged2single.html b/docs/reference/lt_abridged2single.html
new file mode 100644
index 000000000..eaaa0ddcc
--- /dev/null
+++ b/docs/reference/lt_abridged2single.html
@@ -0,0 +1,348 @@
+
+
+
+  
+  
+
+
+
+create a life table by single year of age by graduating an abridged life table — lt_abridged2single • DemoTools
+
+
+
+
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+    
                                    
+      
                                    
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+      
                                    
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+  
                                    
+    
                                    
+    
                                    create a life table by single year of age by graduating an abridged life table
                                    
+    Source: R/lt_regroup_age.R
+    
+    
                                    
+
+    
                                    
+    
                                    Computes single year of age life table by graduating the mortality schedule of an abridged life table, using the ungroup::pclm() to ungroup binned count data. Returns complete single-age lifetable.
                                    
+    
                                    
+
+    
                                    lt_abridged2single(
+  Deaths = NULL,
+  Exposures = NULL,
+  nMx = NULL,
+  nqx = NULL,
+  lx = NULL,
+  Age,
+  radix = 1e+05,
+  axmethod = "un",
+  a0rule = "ak",
+  Sex = "m",
+  region = "w",
+  IMR = NA,
+  mod = TRUE,
+  SRB = 1.05,
+  OAG = TRUE,
+  OAnew = max(Age),
+  extrapLaw = NULL,
+  extrapFrom = max(Age),
+  extrapFit = NULL,
+  ...
+)
                                    
+
+    
                                    Arguments
                                    
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
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+    
+    
                                    Deaths
                                    numeric. Vector of death counts in abridged age classes.
                                    Exposures
                                    numeric. Vector of population exposures in abridged age classes.
                                    nMx
                                    numeric. Vector of mortality rates in abridged age classes.
                                    nqx
                                    numeric. Vector of conditional death probabilities in abridged age classes.
                                    lx
                                    numeric. Vector of lifetable survivorship at abridged ages.
                                    Age
                                    integer. A vector of ages of the lower integer bound of the age classes.
                                    radix
                                    numeric. Lifetable radix, l0. Default 100000.
                                    axmethod
                                    character. Either "pas" or "un".
                                    a0rule
                                    character. Either "ak" (default) or "cd".
                                    Sex
                                    character. Either male "m", female "f", or both "b".
                                    region
                                    character. North, East, South, or West: code"n", code"e", code"s", code"w". Default code"w".
                                    IMR
                                    numeric. Infant mortality rate q0, in case available and nqx is not specified. Default NA.
                                    mod
                                    logical. If "un" specified for axmethod, whether or not to use Nan Li's modification for ages 5-14. Default TRUE.
                                    SRB
                                    the sex ratio at birth (boys / girls), default 1.05
                                    OAG
                                    logical. Whether or not the last element of nMx (or nqx or lx) is an open age group. Default TRUE.
                                    OAnew
                                    integer. Desired open age group (5-year ages only). Default max(Age). If higher then rates are extrapolated.
                                    extrapLaw
                                    character. If extrapolating, which parametric mortality law should be invoked? Options include
+"Kannisto", "Kannisto_Makeham", "Makeham", "Gompertz", "GGompertz", "Beard",    "Beard_Makeham", "Quadratic". Default "Kannisto" if the highest age is at least 90, otherwise "makeham". See details.
                                    extrapFrom
                                    integer. Age from which to impute extrapolated mortality.
                                    extrapFit
                                    integer vector. Ages to include in model fitting. Defaults to all ages >          =60.
                                    ...
                                    optional arguments passed to pclm(). For example, if you pass an explicit lambda parameter via the control argument, you can speed up estimation
                                    
+
+    
                                    Value
                                    
+
+    
                                    Single-year lifetable in data.frame with columns
                                      
+
                                    • Ageinteger. Lower bound of single year age class,
                                      • 
+
                                    • AgeIntinteger. Age class widths.
                                      • 
+
                                    • nMxnumeric. Age-specific central death rates.
                                      • 
+
                                    • nAxnumeric. Average time spent in interval by those deceased in interval.
                                      • 
+
                                    • nqxnumeric. Age-specific conditional death probabilities.
                                      • 
+
                                    • lxnumeric. Lifetable survivorship
                                      • 
+
                                    • ndxnumeric. Lifetable deaths distribution.
                                      • 
+
                                    • nLxnumeric. Lifetable exposure.
                                      • 
+
                                    • Sxnumeric. Survivor ratios.
                                      • 
+
                                    • Txnumeric. Lifetable total years left to live above age x.
                                      • 
+
                                    • exnumeric. Age-specific remaining life expectancy.
                                      • 
+
                                    
+
+    
                                    Details
                                    
+
+    
                                    Similar to lt_abridged() details, forthcoming.
                                    
+
+    
                                    Examples
                                    
+    
                                     Mx <- c(.23669,.04672,.00982,.00511,.00697,.01036,.01169,
+         .01332,.01528,.01757,.02092,.02517,.03225,.04241,.06056,
+         .08574,.11840,.16226,.23745)
+ Age = c(0,1,seq(5,85,by=5))
+ AgeInt <- inferAgeIntAbr(vec = Mx)
+ LTabr <- lt_abridged(nMx = Mx,
+                      Age = Age, 
+                      axmethod = "un",
+                      Sex = "m",
+                      mod = TRUE)
+ 
+ LT1 <- lt_abridged2single(nMx = Mx,
+                    Age = Age, 
+                    axmethod = "un",
+                    Sex = "m",
+                    mod = TRUE)
+LTabr$ex[1]
+
                                    #> [1] 35.02979
                                    LT1$ex[1]
+
                                    #> [1] 34.50728
                                    if (FALSE) {
+plot(Age, LTabr$nMx,type = 's', log = 'y')
+lines(LT1$Age, LT1$nMx)
+
+plot(Age, LTabr$lx,type='S')
+lines(LT1$Age, LT1$lx)
+}
+
                                    
+  
                                    
+  
+
                                    
+
+
+      
                                    
+      
                                    
+  
                                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                    
+
                                    
+
+
                                    
+  
                                    Site built with pkgdown 1.6.1.
                                    
+
                                    
+
+      
                                    
+   
                                    
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/lt_ambiguous.html b/docs/reference/lt_ambiguous.html
new file mode 100644
index 000000000..f89428550
--- /dev/null
+++ b/docs/reference/lt_ambiguous.html
@@ -0,0 +1,231 @@
+
+
+
+  
+  
+
+
+
+calculate an abridged or single age lifetable from abridged or single age data — lt_ambiguous • DemoTools
+
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+  
                                    
+    
                                    
+    
                                    calculate an abridged or single age lifetable from abridged or single age data
                                    
+    Source: R/lt_regroup_age.R
+    
+    
                                    
+
+    
                                    
+    
                                    This is a wrapper around the other lifetable utilities. We start with either nMx, nqx, or lx in single or abridged ages, and returns a full lifetable in either single or abridged ages. All optional arguments of lt_abridged() or lt_single*() can be passed in, for instance the nax assumptions or the extrapolation arguments.
                                    
+    
                                    
+
+    
                                    lt_ambiguous(
+  nMx_or_nqx_or_lx = NULL,
+  type = "m",
+  Age = NULL,
+  Sex = NULL,
+  Single = FALSE,
+  ...
+)
                                    
+
+    
                                    Arguments
                                    
+    
+    
+    
+      
+      
+    
+    
+      
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+    
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                                    nMx_or_nqx_or_lx
                                    numeric vector of either nMx, nqx, or lx
                                    type
                                    character, which variable is x?, either "m", "q", or "l". Default "m"
                                    Age
                                    integer vector of the lower age bounds of x
                                    Sex
                                    character, "m", "f", or "b".
                                    Single
                                    logical, do we want output in single ages?
                                    ...
                                    optional arguments passed to lt_abridged() or lt_single*()
                                    
+
+
+  
                                    
+  
+
                                    
+
+
+      
                                    
+      
                                    
+  
                                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                    
+
                                    
+
+
                                    
+  
                                    Site built with pkgdown 1.6.1.
                                    
+
                                    
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+      
                                    
+   
                                    
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+  
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diff --git a/docs/reference/lt_id_L_T.html b/docs/reference/lt_id_L_T.html
index c3c8959fc..701a57f39 100644
--- a/docs/reference/lt_id_L_T.html
+++ b/docs/reference/lt_id_L_T.html
@@ -81,7 +81,7 @@
       
       
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diff --git a/docs/reference/lt_id_Ll_S.html b/docs/reference/lt_id_Ll_S.html
index afe510c98..131cea291 100644
--- a/docs/reference/lt_id_Ll_S.html
+++ b/docs/reference/lt_id_Ll_S.html
@@ -47,7 +47,7 @@
 
 
 
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+
 
 
 
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@@ -159,10 +159,10 @@ 
                                            Calculate survivor ratios
                                            
     
 
     
                                            
-    
                                            An extra lifetable column for use in projections, which require uniform time steps both both age and period. Intervals are either single age (N=1) or five-year ages (N=5). Input vectors are assumed to come from either single or standard abridged ages.
                                            
+    
                                            An extra lifetable column for use in projections, which require uniform time steps both both age and period. Intervals are either single age or five-year ages. Input vectors are assumed to come from either single or standard abridged ages. Note that the ages of the output Sx are the ages the population would be after the N-year projection.
                                            
     
                                            
 
-    
                                            lt_id_Ll_S(nLx, lx, AgeInt, N = c(5, 1))
                                            
+    
                                            lt_id_Ll_S(nLx, lx = NULL, Age, AgeInt = NULL, N = 5)
                                            
 
     
                                            Arguments
                                            
     
@@ -173,11 +173,15 @@ 
                                            Arg
     
     
                                            
-      
+      
+    
+    
+      
+      
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+      
diff --git a/docs/reference/lt_id_d_l.html b/docs/reference/lt_id_d_l.html
new file mode 100644
index 000000000..9638e39fc
--- /dev/null
+++ b/docs/reference/lt_id_d_l.html
@@ -0,0 +1,219 @@
+
+
+
+  
+  
+
+
+
+Derive survivorship from lifetable deaths — lt_id_d_l • DemoTools
+
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+    
                                            
+    
                                            Derive survivorship from lifetable deaths
                                            
+    Source: R/lt_id.R
+    
+    
                                            
+
+    
                                            
+    
                                            This lifetable identity is the same no matter what kind of lifetable is required. You can find it in any demography textbook.
                                            
+    
                                            
+
+    
                                            lt_id_d_l(ndx, radix = sum(ndx))
                                            
+
+    
                                            Arguments
                                            
+    
                                            
       lx
                                            numeric. Vector of lifetable survivorship at abridged ages.
                                            numeric vector of lifetable survivors from same lifetable than nLx. Infered radix from nLx in case is NULL.
                                            Age
                                            integer vector of starting ages.
                                            		
     
                                            
     
                                            
       AgeInt
                                            integer. Vector of age class widths. Default inferAgeIntAbr(Age = Age).
                                            integer vector of age intervals.
                                            		
     
                                            
     
                                            
       N
                                            
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
                                            ndx
                                            numeric.  Vector of age-specific lifetable deaths.
                                            radix
                                            numeric.
                                            
+
+    
                                            Value
                                            
+
+    
                                            lx vector of lifetable survivorship
                                            
+    
                                            Details
                                            
+
+    
                                            The vector returned is the same length as dx and it sums to the lifetable radix. If the radix is one then this is the discrete deaths distribution.
                                            
+    
                                            References
                                            
+
+    
                                            Preston S, Heuveline P, Guillot M (2000).
+Demography: measuring and modeling population processes.
+Wiley-Blackwell.
                                            
+
+  
+  
+
+
+
+      
                                            
+      
                                            
+  
                                            Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                            
+
                                            
+
+
                                            
+  
                                            Site built with pkgdown 1.6.1.
                                            
+
                                            
+
+      
                                            
+   
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/lt_id_d_q.html b/docs/reference/lt_id_d_q.html
new file mode 100644
index 000000000..63a6b3841
--- /dev/null
+++ b/docs/reference/lt_id_d_q.html
@@ -0,0 +1,215 @@
+
+
+
+  
+  
+
+
+
+Derive death probabilities from lifetable deaths — lt_id_d_q • DemoTools
+
+
+
+
+
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+    
                                            
+      
                                            
+      
+
+      
+
+      
                                            
+
+
                                            
+  
                                            
+    
                                            
+    
                                            Derive death probabilities from lifetable deaths
                                            
+    Source: R/lt_id.R
+    
+    
                                            
+
+    
                                            
+    
                                            This lifetable identity is the same no matter what kind of lifetable is required.  You can find it in any demography textbook.
                                            
+    
                                            
+
+    
                                            lt_id_d_q(ndx)
                                            
+
+    
                                            Arguments
                                            
+    
+    
+    
+      
+      
+    
+    
                                            ndx
                                            numeric.  Vector of age-specific lifetable survivorship.
                                            
+
+    
                                            Value
                                            
+
+    
                                            nqx vector of lifetable death probabilities.
                                            
+    
                                            Details
                                            
+
+    
                                            The vector returned is the same length as dx.
                                            
+    
                                            References
                                            
+
+    
                                            Preston S, Heuveline P, Guillot M (2000).
+Demography: measuring and modeling population processes.
+Wiley-Blackwell.
                                            
+
+  
                                            
+  
+
                                            
+
+
+      
                                            
+      
                                            
+  
                                            Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                            
+
                                            
+
+
                                            
+  
                                            Site built with pkgdown 1.6.1.
                                            
+
                                            
+
+      
                                            
+   
                                            
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/lt_id_l_d.html b/docs/reference/lt_id_l_d.html
index 0324c0cb2..1dd30538d 100644
--- a/docs/reference/lt_id_l_d.html
+++ b/docs/reference/lt_id_l_d.html
@@ -81,7 +81,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
 
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diff --git a/docs/reference/lt_id_l_q.html b/docs/reference/lt_id_l_q.html
new file mode 100644
index 000000000..567d03c55
--- /dev/null
+++ b/docs/reference/lt_id_l_q.html
@@ -0,0 +1,217 @@
+
+
+
+  
+  
+
+
+
+Derive lifetable death probabilities from survivorship. — lt_id_l_q • DemoTools
+
+
+
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+    
                                                
+      
                                                
+      
+
+      
+
+      
                                                
+
+
                                                
+  
                                                
+    
                                                
+    
                                                Derive lifetable death probabilities from survivorship.
                                                
+    Source: R/lt_id.R
+    
+    
                                                
+
+    
                                                
+    
                                                This lifetable identity is the same no matter what kind of lifetable is required.
+You can find it in any demography textbook.
                                                
+    
                                                
+
+    
                                                lt_id_l_q(lx)
                                                
+
+    
                                                Arguments
                                                
+    
+    
+    
+      
+      
+    
+    
                                                lx
                                                numeric.  Vector of age-specific lifetable survivorship.
                                                
+
+    
                                                Value
                                                
+
+    
                                                qx values of age-specific mortality rates.  The last value is always 1.0
                                                
+    
                                                Details
                                                
+
+    
                                                The vector returned is the same length as lx.
                                                
+    
                                                References
                                                
+
+    
                                                Preston S, Heuveline P, Guillot M (2000).
+Demography: measuring and modeling population processes.
+Wiley-Blackwell.
                                                
+
+  
                                                
+  
+
                                                
+
+
+      
                                                
+      
                                                
+  
                                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                
+
                                                
+
+
                                                
+  
                                                Site built with pkgdown 1.6.1.
                                                
+
                                                
+
+      
                                                
+   
                                                
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/lt_id_lda_L.html b/docs/reference/lt_id_lda_L.html
index 1368ef067..fcb463d22 100644
--- a/docs/reference/lt_id_lda_L.html
+++ b/docs/reference/lt_id_lda_L.html
@@ -82,7 +82,7 @@
       
       
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-        01.13.20
+        01.13.76
       
     
 
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diff --git a/docs/reference/lt_id_ma_q.html b/docs/reference/lt_id_ma_q.html
index 59162d15d..15d11f4c8 100644
--- a/docs/reference/lt_id_ma_q.html
+++ b/docs/reference/lt_id_ma_q.html
@@ -80,7 +80,7 @@
       
       
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-        01.13.20
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diff --git a/docs/reference/lt_id_morq_a.html b/docs/reference/lt_id_morq_a.html
index f430ed95f..4cb3e2fac 100644
--- a/docs/reference/lt_id_morq_a.html
+++ b/docs/reference/lt_id_morq_a.html
@@ -80,7 +80,7 @@
       
       
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-        01.13.20
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@@ -279,7 +279,7 @@ 
                                                            R
 
 United States Census Bureau (2017).
 “Population Analysis System (PAS) Software.”
-https://www.census.gov/data/software/pas.html.
                                                            
+https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.
                                                            
 
   
   
 
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@@ -310,7 +310,7 @@ 
                                                                Examp
                       AgeInt = AgeInt, 
                       Sex = 'f',
                       region = 'w')
-
                                                                #>  [1] 0.314100 1.361000 1.971728 2.427513 2.635291 2.604932 2.567799 2.557820
+
                                                                #>  [1] 0.314110 1.361000 1.971728 2.427513 2.635291 2.604932 2.567799 2.557820
 #>  [9] 2.544030 2.547217 2.570674 2.596676 2.612155 2.608886 2.584268 2.540573
 #> [17] 2.541591 2.419688 3.537193
                                                                lt_id_morq_a_greville(nqx = nqx, 
                       Age = Age, 
@@ -344,7 +344,7 @@ 
                                                                Examp
                       Age = Age, 
                       AgeInt = AgeInt, 
                       region = 'w')
-
                                                                #>  [1] 0.314100 1.361000 1.971728 2.427513 2.635291 2.604932 2.567799 2.557820
+
                                                                #>  [1] 0.314110 1.361000 1.971728 2.427513 2.635291 2.604932 2.567799 2.557820
 #>  [9] 2.544030 2.547217 2.570674 2.596676 2.612155 2.608886 2.584268 2.540573
 #> [17] 2.541591 2.419688 3.537193
                                                                
 # example of transitivity. UN ax method is iterative
diff --git a/docs/reference/lt_id_q_l.html b/docs/reference/lt_id_q_l.html
index 964932e69..287b70e96 100644
--- a/docs/reference/lt_id_q_l.html
+++ b/docs/reference/lt_id_q_l.html
@@ -81,7 +81,7 @@
       
       
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diff --git a/docs/reference/lt_id_qa_m.html b/docs/reference/lt_id_qa_m.html
index ecd96d6a0..8083fe4a5 100644
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diff --git a/docs/reference/lt_id_qm_a.html b/docs/reference/lt_id_qm_a.html
index a42c12b13..13bc0c4cc 100644
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diff --git a/docs/reference/lt_model_lq.html b/docs/reference/lt_model_lq.html
index f542f03ed..1823363bb 100644
--- a/docs/reference/lt_model_lq.html
+++ b/docs/reference/lt_model_lq.html
@@ -47,7 +47,7 @@
 
 
 
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@@ -162,7 +162,7 @@ 
                                                                                Estimate Wilmoth Model Life Table
                                                                                
     
 
     
                                                                                
-    
                                                                                Construct model life tables based on the Log-Quadratic (wilmoth) estimates
+    
                                                                                Construct model life tables based on the Log-Quadratic (Wilmoth) estimates
 with various choices of 2 input parameters:
 q0_5, q0_1, q15_45, q15_35 and e0. There are 8 possible
 combinations (see examples below).
                                                                                
@@ -268,7 +268,7 @@ 
                                                                                Arg
     
     
       SRB
-      
                                                                                the sex ratio at birth (boys / girls), detault 1.05
                                                                                
+      
                                                                                the sex ratio at birth (boys / girls), default 1.05
                                                                                
     
     
 
@@ -286,7 +286,7 @@ 
                                                                                Details
 integers. For example 45q15 is represented as q45_15.
                                                                                
     
                                                                                Note
                                                                                
 
-    
                                                                                This function is ported from MortalityEstimate::wilmothLT experimental package by Marius Pascariu. The package is no longe maintained. The latest version can be found here: https://github.com/mpascariu/MortalityEstimate
                                                                                
+    
                                                                                This function is ported from MortalityEstimate::wilmothLT experimental package by Marius Pascariu. The package is no longer maintained. The latest version can be found here: https://github.com/mpascariu/MortalityEstimate
                                                                                
 
     
                                                                                Examples
                                                                                
     
                                                                                
@@ -295,60 +295,60 @@ 
                                                                                Examp
 L1 <- lt_model_lq(Sex = "b", q0_5 = 0.05, e0 = 65)
 L1
 
                                                                                #> $lt
-#>     Age AgeInt          nMx       nAx         nqx           lx          ndx
-#> 0     0      1 0.0410512359 0.1896953 0.039729668 1.000000e+05 3.972967e+03
-#> 1     1      4 0.0026918261 1.4972530 0.010695251 9.602703e+04 1.027033e+03
-#> 5     5      5 0.0011225901 2.5000000 0.005597242 9.500000e+04 5.317380e+02
-#> 10   10      5 0.0008954053 2.5000000 0.004467027 9.446826e+04 4.219923e+02
-#> 15   15      5 0.0017669921 2.5000000 0.008796104 9.404627e+04 8.272408e+02
-#> 20   20      5 0.0024186771 2.5000000 0.012020700 9.321903e+04 1.120558e+03
-#> 25   25      5 0.0025645864 2.5000000 0.012741242 9.209847e+04 1.173449e+03
-#> 30   30      5 0.0029684958 2.5000000 0.014733141 9.092502e+04 1.339611e+03
-#> 35   35      5 0.0038331499 2.5000000 0.018983830 8.958541e+04 1.700674e+03
-#> 40   40      5 0.0052139916 2.5000000 0.025734509 8.788474e+04 2.261671e+03
-#> 45   45      5 0.0074292174 2.5000000 0.036468751 8.562307e+04 3.122566e+03
-#> 50   50      5 0.0108459016 2.5000000 0.052797906 8.250050e+04 4.355854e+03
-#> 55   55      5 0.0159273998 2.5000000 0.076587404 7.814465e+04 5.984896e+03
-#> 60   60      5 0.0243032257 2.5000000 0.114555932 7.215975e+04 8.266327e+03
-#> 65   65      5 0.0373150554 2.5000000 0.170655252 6.389342e+04 1.090375e+04
-#> 70   70      5 0.0590143815 2.5000000 0.257135218 5.298968e+04 1.362551e+04
-#> 75   75      5 0.0932246607 2.5000000 0.378021085 3.936416e+04 1.488048e+04
-#> 80   80      5 0.1439135402 2.5000000 0.529178002 2.448368e+04 1.295622e+04
-#> 85   85      5 0.2168418692 2.5000000 0.703071176 1.152745e+04 8.104621e+03
-#> 90   90      5 0.3080565236 2.5000000 0.870146700 3.422834e+03 2.978367e+03
-#> 95   95      5 0.4244002008 2.5000000 0.880208315 4.444662e+02 3.912229e+02
-#> 100 100      5 0.5495844154 2.5000000 0.935939164 5.324336e+01 4.983255e+01
-#> 105 105      5 0.6700558318 2.5000000 0.964925439 3.410814e+00 3.291181e+00
-#> 110 110     NA 0.7610553490 2.6139387 1.000000000 1.196328e-01 1.196328e-01
+#>     Age AgeInt          nMx      nAx         nqx           lx          ndx
+#> 0     0      1 0.0410512359 0.183536 0.039719948 1.000000e+05 3.971995e+03
+#> 1     1      4 0.0026943632 1.497275 0.010705264 9.602801e+04 1.028005e+03
+#> 5     5      5 0.0011225675 2.500000 0.005597130 9.500000e+04 5.317273e+02
+#> 10   10      5 0.0008953863 2.500000 0.004466932 9.446827e+04 4.219834e+02
+#> 15   15      5 0.0017669443 2.500000 0.008795867 9.404629e+04 8.272187e+02
+#> 20   20      5 0.0024185992 2.500000 0.012020315 9.321907e+04 1.120523e+03
+#> 25   25      5 0.0025644970 2.500000 0.012740800 9.209855e+04 1.173409e+03
+#> 30   30      5 0.0029683912 2.500000 0.014732626 9.092514e+04 1.339566e+03
+#> 35   35      5 0.0038330218 2.500000 0.018983201 8.958557e+04 1.700621e+03
+#> 40   40      5 0.0052138343 2.500000 0.025733743 8.788495e+04 2.261609e+03
+#> 45   45      5 0.0074290244 2.500000 0.036467821 8.562334e+04 3.122497e+03
+#> 50   50      5 0.0108456672 2.500000 0.052796795 8.250085e+04 4.355780e+03
+#> 55   55      5 0.0159271191 2.500000 0.076586106 7.814507e+04 5.984826e+03
+#> 60   60      5 0.0243028860 2.500000 0.114554422 7.216024e+04 8.266275e+03
+#> 65   65      5 0.0373146602 2.500000 0.170653598 6.389397e+04 1.090374e+04
+#> 70   70      5 0.0590139447 2.500000 0.257133559 5.299023e+04 1.362557e+04
+#> 75   75      5 0.0932241868 2.500000 0.378019527 3.936466e+04 1.488061e+04
+#> 80   80      5 0.1439130103 2.500000 0.529176569 2.448405e+04 1.295639e+04
+#> 85   85      5 0.2168414376 2.500000 0.703070268 1.152767e+04 8.104759e+03
+#> 90   90      5 0.3080561894 2.500000 0.870146166 3.422907e+03 2.978429e+03
+#> 95   95      5 0.4244002008 2.500000 0.880208315 4.444775e+02 3.912328e+02
+#> 100 100      5 0.5495844154 2.500000 0.935939164 5.324471e+01 4.983381e+01
+#> 105 105      5 0.6700558318 2.500000 0.964925439 3.410901e+00 3.291265e+00
+#> 110 110     NA 0.7610553490 2.613939 1.000000000 1.196359e-01 1.196359e-01
 #>              nLx         Sx           Tx        ex
-#> 0   9.678069e+04 0.95663683 6.500007e+06 65.000066
-#> 1   3.815377e+05 0.99028313 6.403226e+06 66.681493
-#> 5   4.736707e+05 0.99496628 6.021688e+06 63.386191
-#> 10  4.712863e+05 0.99337328 5.548018e+06 58.728904
-#> 15  4.681632e+05 0.98959872 5.076731e+06 53.981208
-#> 20  4.632938e+05 0.98762121 4.608568e+06 49.438060
-#> 25  4.575587e+05 0.98626919 4.145274e+06 45.009153
-#> 30  4.512761e+05 0.98315729 3.687715e+06 40.557763
-#> 35  4.436754e+05 0.97767318 3.236439e+06 36.126858
-#> 40  4.337695e+05 0.96896833 2.792764e+06 31.777577
-#> 45  4.203089e+05 0.95551831 2.358995e+06 27.550923
-#> 50  4.016129e+05 0.93562987 1.938686e+06 23.499077
-#> 55  3.757610e+05 0.90518426 1.537073e+06 19.669585
-#> 60  3.401329e+05 0.85909865 1.161312e+06 16.093622
-#> 65  2.922077e+05 0.79013852 8.211788e+05 12.852321
-#> 70  2.308846e+05 0.69133935 5.289711e+05  9.982531
-#> 75  1.596196e+05 0.56401490 2.980865e+05  7.572534
-#> 80  9.002784e+04 0.41515739 1.384669e+05  5.655476
-#> 85  3.737572e+04 0.25867727 4.843902e+04  4.202057
-#> 90  9.668250e+03 0.12869693 1.106330e+04  3.232204
-#> 95  1.244274e+03 0.11382978 1.395048e+03  3.138705
-#> 100 1.416354e+02 0.06231574 1.507743e+02  2.831795
-#> 105 8.826118e+00 0.03421804 9.138830e+00  2.679369
-#> 110 3.127128e-01 0.00000000 3.127128e-01  2.613939
+#> 0   9.675701e+04 0.95659243 6.500007e+06 65.000066
+#> 1   3.815392e+05 0.99032914 6.403250e+06 66.681064
+#> 5   4.736707e+05 0.99496638 6.021710e+06 63.386425
+#> 10  4.712864e+05 0.99337345 5.548040e+06 58.729133
+#> 15  4.681634e+05 0.98959903 5.076753e+06 53.981431
+#> 20  4.632940e+05 0.98762162 4.608590e+06 49.438273
+#> 25  4.575592e+05 0.98626967 4.145296e+06 45.009351
+#> 30  4.512768e+05 0.98315786 3.687737e+06 40.557943
+#> 35  4.436763e+05 0.97767387 3.236460e+06 36.127021
+#> 40  4.337707e+05 0.96896918 2.792784e+06 31.777722
+#> 45  4.203105e+05 0.95551933 2.359013e+06 27.551048
+#> 50  4.016148e+05 0.93563106 1.938702e+06 23.499181
+#> 55  3.757633e+05 0.90518564 1.537088e+06 19.669669
+#> 60  3.401355e+05 0.85910020 1.161324e+06 16.093687
+#> 65  2.922105e+05 0.79014013 8.211888e+05 12.852368
+#> 70  2.308872e+05 0.69134090 5.289783e+05  9.982563
+#> 75  1.596218e+05 0.56401632 2.980911e+05  7.572554
+#> 80  9.002929e+04 0.41515854 1.384693e+05  5.655488
+#> 85  3.737643e+04 0.25867800 4.843997e+04  4.202063
+#> 90  9.668460e+03 0.12869739 1.106354e+04  3.232207
+#> 95  1.244306e+03 0.11382978 1.395084e+03  3.138705
+#> 100 1.416390e+02 0.06231574 1.507781e+02  2.831795
+#> 105 8.826342e+00 0.03421804 9.139063e+00  2.679369
+#> 110 3.127208e-01 0.00000000 3.127208e-01  2.613939
 #> 
 #> $values
 #>          k       q0_1 q0_5    q15_35    q15_45       e0
-#>  0.5545641 0.03972967 0.05 0.1227669 0.2327208 65.00007
+#>  0.5544684 0.03971995 0.05 0.1227634 0.2327157 65.00007
 #> 
 #> attr(,"class")
 #> [1] "lt_model_lq"
                                                                                ls(L1)
diff --git a/docs/reference/lt_rule_1a0.html b/docs/reference/lt_rule_1a0.html
new file mode 100644
index 000000000..70cb57f87
--- /dev/null
+++ b/docs/reference/lt_rule_1a0.html
@@ -0,0 +1,243 @@
+
+
+
+  
+  
+
+
+
+calculate a0 in different ways — lt_rule_1a0 • DemoTools
+
+
+
+
+
+
+
+
+
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+    
                                                                                
+      
                                                                                
+      
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+      
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+      
                                                                                
+
+
                                                                                
+  
                                                                                
+    
                                                                                
+    
                                                                                calculate a0 in different ways
                                                                                
+    Source: R/lt_rule.R
+    
+    
                                                                                
+
+    
                                                                                
+    
                                                                                This function wraps the Coale-Demeny and Andreev-Kingkade approximations for a0, which can come from M0, q0, or IMR.
                                                                                
+    
                                                                                
+
+    
                                                                                lt_rule_1a0(
+  rule = "ak",
+  M0 = NULL,
+  q0 = NULL,
+  Sex = "m",
+  IMR = NA,
+  region = "w",
+  SRB = 1.05
+)
                                                                                
+
+    
                                                                                Arguments
                                                                                
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
                                                                                rule
                                                                                character. Either "ak" (Andreev-Kingkade) or "cd" (Coale-Demeny).
                                                                                M0
                                                                                numeric. Event exposure infant mortality rate.
                                                                                q0
                                                                                a value or vector of values of m0, the death risk in the first year of life.
                                                                                Sex
                                                                                character, either "m", "f", or "b"
                                                                                IMR
                                                                                numeric. Optional. q0, the death probability in first year of life, in case available separately.
                                                                                region
                                                                                character. "n", "e", "s" or "w" for North, East, South, or West.
                                                                                SRB
                                                                                the sex ratio at birth (boys / girls), default 1.05
                                                                                
+
+    
                                                                                Value
                                                                                
+
+    
                                                                                a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values.
                                                                                
+    
                                                                                Details
                                                                                
+
+    
                                                                                If sex is given as both, "b", then we calculate the male and female results separately, then weight them together using SRB. This is bad in theory, but the leverage is trivial, and it's better than using male or female coefs for the total population.
                                                                                
+
                                                                                Neither Coale-Demeny nor Andreev-Kingkade have explicit a0 rules for both-sexes combined. There's not a good way to arrive at a both-sex a0 estimate without increasing data requirements (you'd need data from each sex, which are not always available). It's more convenient to blend sex-specific a0 estimates based on something. Here we use SRB to do this, for no other reason than it has an easy well-known default value. This is bad because it assumes no sex differences in infant mortality, but this choice has a trivial impact on results.
                                                                                
+
+  
                                                                                
+  
+
                                                                                
+
+
+      
                                                                                
+      
                                                                                
+  
                                                                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                
+
                                                                                
+
+
                                                                                
+  
                                                                                Site built with pkgdown 1.6.1.
                                                                                
+
                                                                                
+
+      
                                                                                
+   
                                                                                
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/lt_rule_1a0_ak.html b/docs/reference/lt_rule_1a0_ak.html
new file mode 100644
index 000000000..a254a8ac3
--- /dev/null
+++ b/docs/reference/lt_rule_1a0_ak.html
@@ -0,0 +1,215 @@
+
+
+
+  
+  
+
+
+
+Andreev-Kingkade approximation for a0 — lt_rule_1a0_ak • DemoTools
+
+
+
+
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+  
+
+  
+    
                                                                                
+      
                                                                                
+      
+
+      
+
+      
                                                                                
+
+
                                                                                
+  
                                                                                
+    
                                                                                
+    
                                                                                Andreev-Kingkade approximation for a0
                                                                                
+    Source: R/lt_rule.R
+    
+    
                                                                                
+
+    
                                                                                
+    
                                                                                This function wraps the two approximations for a0 based on either q0 (IMR) or m0.
                                                                                
+    
                                                                                
+
+    
                                                                                lt_rule_1a0_ak(M0 = NULL, q0 = NULL, Sex)
                                                                                
+
+    
                                                                                Arguments
                                                                                
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
                                                                                M0
                                                                                a value or vector of values of `1m0``, the death risk in the first year of life.
                                                                                q0
                                                                                a value or vector of values of `1q0``, the death probability in the first year of life, sometimes approximated with IMR.
                                                                                Sex
                                                                                either "m" or "f"
                                                                                
+
+    
                                                                                Value
                                                                                
+
+    
                                                                                a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values.
                                                                                
+
+  
                                                                                
+  
+
                                                                                
+
+
+      
                                                                                
+      
                                                                                
+  
                                                                                Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                
+
                                                                                
+
+
                                                                                
+  
                                                                                Site built with pkgdown 1.6.1.
                                                                                
+
                                                                                
+
+      
                                                                                
+   
                                                                                
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/lt_rule_1a0_cd.html b/docs/reference/lt_rule_1a0_cd.html
index 85feda895..2d02a802a 100644
--- a/docs/reference/lt_rule_1a0_cd.html
+++ b/docs/reference/lt_rule_1a0_cd.html
@@ -81,7 +81,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
                                                                                
 
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                                                                                • 
   
-    
+    
      
   
 
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                                                                                  • 
   
-    
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                                                                                    • 
 
                                                                                  • 
   
-    
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     github
   
@@ -207,7 +207,7 @@ 
                                                                                    R
 
 United States Census Bureau (2017).
 “Population Analysis System (PAS) Software.”
-https://www.census.gov/data/software/pas.html.
                                                                                    
+https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.
                                                                                    
 
     
                                                                                    Examples
                                                                                    
     
                                                                                    m0 <- seq(.001, .2, by = .001)
diff --git a/docs/reference/lt_rule_4a1_cd.html b/docs/reference/lt_rule_4a1_cd.html
index d5eaca822..6ee4c825d 100644
--- a/docs/reference/lt_rule_4a1_cd.html
+++ b/docs/reference/lt_rule_4a1_cd.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
                                                                                    
 
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     github
   
@@ -214,7 +214,7 @@ 
                                                                                        R
 
 United States Census Bureau (2017).
 “Population Analysis System (PAS) Software.”
-https://www.census.gov/data/software/pas.html.
                                                                                        
+https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.
                                                                                        
 
     
                                                                                        Examples
                                                                                        
     
                                                                                        m0 <- seq(.001,.2,by =.001)
diff --git a/docs/reference/lt_rule_4m0_D0.html b/docs/reference/lt_rule_4m0_D0.html
index ce72824bd..123872b65 100644
--- a/docs/reference/lt_rule_4m0_D0.html
+++ b/docs/reference/lt_rule_4m0_D0.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
                                                                                        
 
@@ -88,7 +88,7 @@
       
                                                                                          
         
                                                                                        • 
   
-    
+    
      
   
 
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                                                                                          • 
   
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     github
   
diff --git a/docs/reference/lt_rule_4m0_m0.html b/docs/reference/lt_rule_4m0_m0.html
index 68a767d69..c7c9a3d4d 100644
--- a/docs/reference/lt_rule_4m0_m0.html
+++ b/docs/reference/lt_rule_4m0_m0.html
@@ -80,7 +80,7 @@
       
       
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-        01.13.20
+        01.13.76
       
     
                                                                                
 
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diff --git a/docs/reference/lt_rule_ak.html b/docs/reference/lt_rule_ak.html
new file mode 100644
index 000000000..6ad7e8fd0
--- /dev/null
+++ b/docs/reference/lt_rule_ak.html
@@ -0,0 +1,215 @@
+
+
+
+  
+  
+
+
+
+Andreev-Kingkade approximation for a0 — lt_rule_ak • DemoTools
+
+
+
+
+
+
+
+
+
+
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+  
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+    
                                                                                    
+      
                                                                                    
+      
+
+      
+
+      
                                                                                    
+
+
                                                                                    
+  
                                                                                    
+    
                                                                                    
+    
                                                                                    Andreev-Kingkade approximation for a0
                                                                                    
+    Source: R/lt_rule.R
+    
+    
                                                                                    
+
+    
                                                                                    
+    
                                                                                    This function wraps the two approximations for a0 based on either q0 (IMR) or m0.
                                                                                    
+    
                                                                                    
+
+    
                                                                                    lt_rule_ak(m0 = NULL, q0 = NULL, Sex)
                                                                                    
+
+    
                                                                                    Arguments
                                                                                    
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
                                                                                    m0
                                                                                    a value or vector of values of m0, the death probability in the first year of life.
                                                                                    q0
                                                                                    a value or vector of values of m0, the death risk in the first year of life.
                                                                                    Sex
                                                                                    either "m" or "f"
                                                                                    
+
+    
                                                                                    Value
                                                                                    
+
+    
                                                                                    a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values.
                                                                                    
+
+  
                                                                                    
+  
+
                                                                                    
+
+
+      
                                                                                    
+      
                                                                                    
+  
                                                                                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sean Fennell.
                                                                                    
+
                                                                                    
+
+
                                                                                    
+  
                                                                                    Site built with pkgdown 1.5.1.
                                                                                    
+
                                                                                    
+
+      
                                                                                    
+   
                                                                                    
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/lt_rule_ak_m0_a0.html b/docs/reference/lt_rule_ak_m0_a0.html
new file mode 100644
index 000000000..b9fe05b83
--- /dev/null
+++ b/docs/reference/lt_rule_ak_m0_a0.html
@@ -0,0 +1,211 @@
+
+
+
+  
+  
+
+
+
+estimates a0 using the Andreev-Kingkade rule of thumb starting with an event exposure rate — lt_rule_ak_m0_a0 • DemoTools
+
+
+
+
+
+
+
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+  
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+    
                                                                                    
+      
                                                                                    
+      
+
+      
+
+      
                                                                                    
+
+
                                                                                    
+  
                                                                                    
+    
                                                                                    
+    
                                                                                    estimates a0 using the Andreev-Kingkade rule of thumb starting with an event exposure rate
                                                                                    
+    Source: R/lt_rule.R
+    
+    
                                                                                    
+
+    
                                                                                    
+    
                                                                                    These formulas and cutpoints are based on a supplementary analysis from Andreev & Kingkade. The original formulation was in terms of IMR. There is also an analytic path to convert M0 to q0 and then use the original q0 cutpoints. Code ported from HMD code base.
                                                                                    
+    
                                                                                    
+
+    
                                                                                    lt_rule_ak_m0_a0(M0, Sex)
                                                                                    
+
+    
                                                                                    Arguments
                                                                                    
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
                                                                                    M0
                                                                                    a value or vector of values of m0, the death risk in the first year of life.
                                                                                    Sex
                                                                                    either "m" or "f"
                                                                                    
+
+    
                                                                                    Value
                                                                                    
+
+    
                                                                                    a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values.
                                                                                    
+
+  
                                                                                    
+  
+
                                                                                    
+
+
+      
                                                                                    
+      
                                                                                    
+  
                                                                                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                    
+
                                                                                    
+
+
                                                                                    
+  
                                                                                    Site built with pkgdown 1.6.1.
                                                                                    
+
                                                                                    
+
+      
                                                                                    
+   
                                                                                    
+
+  
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+
+  
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+
diff --git a/docs/reference/lt_rule_ak_q0_a0.html b/docs/reference/lt_rule_ak_q0_a0.html
new file mode 100644
index 000000000..e496ca28c
--- /dev/null
+++ b/docs/reference/lt_rule_ak_q0_a0.html
@@ -0,0 +1,211 @@
+
+
+
+  
+  
+
+
+
+estimates a0 using the Andreev-Kingkade rule of thumb starting with IMR — lt_rule_ak_q0_a0 • DemoTools
+
+
+
+
+
+
+
+
+
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+  
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+    
                                                                                    
+      
                                                                                    
+      
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+      
                                                                                    
+
+
                                                                                    
+  
                                                                                    
+    
                                                                                    
+    
                                                                                    estimates a0 using the Andreev-Kingkade rule of thumb starting with IMR
                                                                                    
+    Source: R/lt_rule.R
+    
+    
                                                                                    
+
+    
                                                                                    
+    
                                                                                    AKq02a0 Andreev Kingkade a0 method. This version has a 3-part segmented linear model, based on cut points in q0. Code ported from HMDLifeTables.
                                                                                    
+    
                                                                                    
+
+    
                                                                                    lt_rule_ak_q0_a0(q0, Sex)
                                                                                    
+
+    
                                                                                    Arguments
                                                                                    
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
                                                                                    q0
                                                                                    a value or vector of values of q0, the death probability in the first year of life.
                                                                                    Sex
                                                                                    either "m" or "f"
                                                                                    
+
+    
                                                                                    Value
                                                                                    
+
+    
                                                                                    a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of a_0 values.
                                                                                    
+
+  
                                                                                    
+  
+
                                                                                    
+
+
+      
                                                                                    
+      
                                                                                    
+  
                                                                                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                    
+
                                                                                    
+
+
                                                                                    
+  
                                                                                    Site built with pkgdown 1.6.1.
                                                                                    
+
                                                                                    
+
+      
                                                                                    
+   
                                                                                    
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/lt_rule_m_extrapolate.html b/docs/reference/lt_rule_m_extrapolate.html
index f5e313b49..0b6e2bae9 100644
--- a/docs/reference/lt_rule_m_extrapolate.html
+++ b/docs/reference/lt_rule_m_extrapolate.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
 
@@ -88,7 +88,7 @@
       
                                                                                      
         
                                                                                    • 
   
-    
+    
      
   
 
                                                                                      • 
@@ -129,13 +129,13 @@
       
                                                                                        
         
                                                                                      • 
   
-    
+    
      
   
 
                                                                                        • 
 
                                                                                      • 
   
-    
+    
      
     github
   
@@ -222,10 +222,13 @@ 
                                                                                        Value
                                                                                        
 
     
                                                                                        An object of class lt_rule_m_extrapolate with the following components:
                                                                                        
 
                                                                                        input
                                                                                        List with arguments provided in input. Saved for convenience.
                                                                                        
-
                                                                                        call
                                                                                        An unevaluated function call, that is, an unevaluated expressionwhich consists of the named function applied to the given arguments.
                                                                                        
+
                                                                                        call
                                                                                        An unevaluated function call, that is, an unevaluated expression that consists of the named function applied to the given arguments.
                                                                                        
 
                                                                                        fitted.model
                                                                                        An object of class MortalityLaw. Here one can find fitted values, residuals, goodness of fit measures etc.
                                                                                        
 
                                                                                        values
                                                                                        A vector or matrix containing the complete mortality data, that is the modified input data following the extrapolation procedure.
                                                                                        
 
+    
                                                                                        Details
                                                                                        
+
+    
                                                                                        If fitting fails to converge, then we refit assuming Gompertz mortality with explicit starting parameters of parS = c(A = 0.005, B = 0.13) and a warning is issued.
                                                                                        
     
                                                                                        See also
                                                                                        
 
     
                                                                                        MortalityLaw
@@ -253,7 +256,9 @@ 
                                                                                        Examp
 f3 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "gompertz")
 f4 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "ggompertz")
 f5 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "makeham")
-f6 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "beard")
+
                                                                                        #> Warning: Extrapolation failed to converge
+#> Falling back to Gompertz with starting parameters:
+#>  parS = c(A = 0.005, B = 0.13))
                                                                                        f6 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "beard")
 f7 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "beard_makeham")
 f8 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "quadratic")
 
@@ -301,7 +306,9 @@ 
                                                                                        Examp
 g5 <- lt_rule_m_extrapolate(mx1, x1, x_fit, x_extr, law = "makeham")
 g6 <- lt_rule_m_extrapolate(mx1, x1, x_fit, x_extr, law = "beard")
 g7 <- lt_rule_m_extrapolate(mx1, x1, x_fit, x_extr, law = "beard_makeham")
-g8 <- lt_rule_m_extrapolate(mx1, x1, x_fit, x_extr, law = "quadratic")
+
                                                                                        #> Warning: Extrapolation failed to converge
+#> Falling back to Gompertz with starting parameters:
+#>  parS = c(A = 0.005, B = 0.13))
                                                                                        g8 <- lt_rule_m_extrapolate(mx1, x1, x_fit, x_extr, law = "quadratic")
 
 # Plot
 if (FALSE) {
@@ -331,8 +338,10 @@ 
                                                                                        Examp
 mx_matrix <- matrix(rep(mx1, 3), ncol = 3) %*% diag(c(1, 1.05, 1.1))
 dimnames(mx_matrix) <- list(age = x1, year = c("year1", "year2", "year3"))
 
-F1 <- lt_rule_m_extrapolate(mx_matrix, x = x1, x_fit, x_extr, law = "kannisto")
-
                                                                                        #> Warning: the condition has length > 1 and only the first element will be used
                                                                                        F1
+# TR: temporary warning suppression until case handling is fixed
+# in MortalityLaws package
+F1 <- suppressWarnings(lt_rule_m_extrapolate(mx_matrix, x = x1, x_fit, x_extr, law = "kannisto"))
+F1
 
                                                                                        #> $input
 #> $input$opt.choices
 #> [1] "poissonL"  "LF2"       "LF1"       "LF3"       "LF4"       "LF5"      
@@ -342,6 +351,9 @@ 
                                                                                        Examp
 #> [1] "kannisto"         "kannisto_makeham" "makeham"          "gompertz"        
 #> [5] "ggompertz"        "beard"            "beard_makeham"    "quadratic"       
 #> 
+#> $input$dm
+#> [1] 37  3
+#> 
 #> $input$mx
 #>      year
 #> age    year1    year2   year3
diff --git a/docs/reference/lt_single2abridged.html b/docs/reference/lt_single2abridged.html
new file mode 100644
index 000000000..7829fb06b
--- /dev/null
+++ b/docs/reference/lt_single2abridged.html
@@ -0,0 +1,245 @@
+
+
+
+  
+  
+
+
+
+calculate an abridged life table that is consistent with a life table by single year of age — lt_single2abridged • DemoTools
+
+
+
+
+
+
+
+
+
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+
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+  
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+  
+    
                                                                                        
+      
                                                                                        
+      
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+      
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+      
                                                                                        
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+
                                                                                        
+  
                                                                                        
+    
                                                                                        
+    
                                                                                        calculate an abridged life table that is consistent with a life table by single year of age
                                                                                        
+    Source: R/lt_regroup_age.R
+    
+    
                                                                                        
+
+    
                                                                                        
+    
                                                                                        Computes abridged life table columns based on the lx, nLx , and ex values from
+a single year life table, in accordance with step 2.2 of the Human Life Table Protocol
+https://www.lifetable.de/methodology.pdf. Output abridged life table has same open age group
+as input single age life table
                                                                                        
+    
                                                                                        
+
+    
                                                                                        lt_single2abridged(lx, nLx, ex, Age = 1:length(lx) - 1, ...)
                                                                                        
+
+    
                                                                                        Arguments
                                                                                        
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
                                                                                        lx
                                                                                        numeric. Vector of lifetable survivorship at single ages.
                                                                                        nLx
                                                                                        numeric. Vector of lifetable exposure at single ages.
                                                                                        ex
                                                                                        numeric. Vector of Age-specific remaining life expectancy at single ages.
                                                                                        Age
                                                                                        integer. Lower bounds of single ages.
                                                                                        ...
                                                                                        optional args, not currently used.
                                                                                        
+
+    
                                                                                        Value
                                                                                        
+
+    
                                                                                        Abridged lifetable in data.frame with columns
                                                                                          
+
                                                                                        • Ageinteger. Lower bound of abridged age class,
                                                                                          • 
+
                                                                                        • AgeIntinteger. Age class widths.
                                                                                          • 
+
                                                                                        • nMxnumeric. Age-specific central death rates.
                                                                                          • 
+
                                                                                        • nAxnumeric. Average time spent in interval by those deceased in interval.
                                                                                          • 
+
                                                                                        • nqxnumeric. Age-specific conditional death probabilities.
                                                                                          • 
+
                                                                                        • lxnumeric. Lifetable survivorship
                                                                                          • 
+
                                                                                        • ndxnumeric. Lifetable deaths distribution.
                                                                                          • 
+
                                                                                        • nLxnumeric. Lifetable exposure.
                                                                                          • 
+
                                                                                        • Sxnumeric. Survivor ratios in uniform 5-year age groups.
                                                                                          • 
+
                                                                                        • Txnumeric. Lifetable total years left to live above age x.
                                                                                          • 
+
                                                                                        • exnumeric. Age-specific remaining life expectancy.
                                                                                          • 
+
                                                                                        
+
+    
                                                                                        Details
                                                                                        
+
+    
                                                                                        Similar to lt_abridged() details, forthcoming
                                                                                        
+
+  
                                                                                        
+  
+
                                                                                        
+
+
+      
                                                                                        
+      
                                                                                        
+  
                                                                                        Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                        
+
                                                                                        
+
+
                                                                                        
+  
                                                                                        Site built with pkgdown 1.6.1.
                                                                                        
+
                                                                                        
+
+      
                                                                                        
+   
                                                                                        
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/lt_single_mx.html b/docs/reference/lt_single_mx.html
index f22d0e7ac..e2ced3851 100644
--- a/docs/reference/lt_single_mx.html
+++ b/docs/reference/lt_single_mx.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
                                                                                        
 
@@ -88,7 +88,7 @@
       
                                                                                          
         
                                                                                        • 
   
-    
+    
      
   
 
                                                                                          • 
@@ -129,13 +129,13 @@
       
                                                                                            
         
                                                                                          • 
   
-    
+    
      
   
 
                                                                                            • 
 
                                                                                          • 
   
-    
+    
      
     github
   
@@ -174,9 +174,9 @@ 
                                                                                            calculate a single age lifetable
                                                                                            
   SRB = 1.05,
   OAG = TRUE,
   OAnew = max(Age),
-  extrapLaw = "kannisto",
+  extrapLaw = NULL,
   extrapFrom = max(Age),
-  extrapFit = Age[Age >= 60],
+  extrapFit = NULL,
   ...
 )
                                    @@ -217,7 +217,7 @@

                                    Arg SRB -

                                    the sex ratio at birth (boys / girls), detault 1.05

                                    +

                                    the sex ratio at birth (boys / girls), default 1.05

                                    OAG @@ -230,7 +230,7 @@

                                    Arg extrapLaw

                                    character. If extrapolating, which parametric mortality law should be invoked? Options include -"Kannisto", "Kannisto_Makeham", "Makeham", "Gompertz", "GGompertz", "Beard", "Beard_Makeham", "Quadratic". Default "Kannisto". See details.

                                    +"Kannisto", "Kannisto_Makeham", "Makeham", "Gompertz", "GGompertz", "Beard", "Beard_Makeham", "Quadratic". Default "Kannisto" if the highest age is at least 90, otherwise "makeham". See details.

                                    extrapFrom diff --git a/docs/reference/lt_single_qx.html b/docs/reference/lt_single_qx.html new file mode 100644 index 000000000..bc52fdbc1 --- /dev/null +++ b/docs/reference/lt_single_qx.html @@ -0,0 +1,299 @@ + + + + + + + + +calculate a single age lifetable from qx — lt_single_qx • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                    +
                                    + + + + +
                                    + +
                                    +
                                    +
                                    +

                                    calculate a single age lifetable from qx

                                    + Source: R/lt_single_qx.R + +
                                    + +
                                    +

                                    Computes lifetable columns from single age qx by first computing 1ax, then computing +1mx from 1qx and 1ax, and finally passing the 1mx to the lt_single_mx() function

                                    +
                                    + + 
                                    lt_single_qx(
+  nqx,
+  Age = 1:length(nqx) - 1,
+  radix = 1e+05,
+  a0rule = "ak",
+  Sex = "m",
+  region = "w",
+  IMR = NA,
+  mod = TRUE,
+  SRB = 1.05,
+  OAG = TRUE,
+  OAnew = max(Age),
+  extrapLaw = NULL,
+  extrapFrom = max(Age),
+  extrapFit = NULL,
+  ...
+)
                                    + +

                                    Arguments

                                    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                    nqx

                                    numeric. Vector of conditional death probabilities in abridged age classes.

                                    Age

                                    integer. A vector of ages of the lower integer bound of the age classes.

                                    radix

                                    numeric. Lifetable radix, l0. Default 100000.

                                    a0rule

                                    character. Either "ak" (default) or "cd".

                                    Sex

                                    character. Either male "m", female "f", or both "b".

                                    region

                                    character. North, East, South, or West: code"n", code"e", code"s", code"w". Default code"w".

                                    IMR

                                    numeric. Infant mortality rate q0, in case available and nqx is not specified. Default NA.

                                    mod

                                    logical. If "un" specified for axmethod, whether or not to use Nan Li's modification for ages 5-14. Default TRUE.

                                    SRB

                                    the sex ratio at birth (boys / girls), default 1.05

                                    OAG

                                    logical. Whether or not the last element of nMx (or nqx or lx) is an open age group. Default TRUE.

                                    OAnew

                                    integer. Desired open age group (5-year ages only). Default max(Age). If higher then rates are extrapolated.

                                    extrapLaw

                                    character. If extrapolating, which parametric mortality law should be invoked? Options include +"Kannisto", "Kannisto_Makeham", "Makeham", "Gompertz", "GGompertz", "Beard", "Beard_Makeham", "Quadratic". Default "Kannisto" if the highest age is at least 90, otherwise "makeham". See details.

                                    extrapFrom

                                    integer. Age from which to impute extrapolated mortality.

                                    extrapFit

                                    integer vector. Ages to include in model fitting. Defaults to all ages > =60.

                                    ...

                                    Other arguments to be passed on to the +MortalityLaw function.

                                    + +

                                    Value

                                    + +

                                    Lifetable in data.frame with columns

                                      +

                                    • Ageinteger. Lower bound of abridged age class,

                                      • +

                                    • AgeIntinteger. Age class widths.

                                      • +

                                    • nMxnumeric. Age-specific central death rates.

                                      • +

                                    • nAxnumeric. Average time spent in interval by those deceased in interval.

                                      • +

                                    • nqxnumeric. Age-specific conditional death probabilities.

                                      • +

                                    • lxnumeric. Lifetable survivorship

                                      • +

                                    • ndxnumeric. Lifetable deaths distribution.

                                      • +

                                    • nLxnumeric. Lifetable exposure.

                                      • +

                                    • Sxnumeric. Survivor ratios in uniform single-year age groups.

                                      • +

                                    • Txnumeric. Lifetable total years left to live above age x.

                                      • +

                                    • exnumeric. Age-specific remaining life expectancy.

                                      • +
                                    + +

                                    Details

                                    + +

                                    Similar to lt_abridged() details, forthcoming

                                    + +
                                    + +
                                    + + +
                                    +
                                    +

                                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                    +
                                    + +
                                    +

                                    Site built with pkgdown 1.6.1.

                                    +
                                    + +
                                    +
                                    + + + + + + + + diff --git a/docs/reference/lt_smooth_ambiguous.html b/docs/reference/lt_smooth_ambiguous.html new file mode 100644 index 000000000..ccc24ab77 --- /dev/null +++ b/docs/reference/lt_smooth_ambiguous.html @@ -0,0 +1,215 @@ + + + + + + + + +Smooth and apply lt_ambiguous — lt_smooth_ambiguous • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                    +
                                    + + + + +
                                    + +
                                    +
                                    +
                                    +

                                    Smooth and apply lt_ambiguous

                                    + Source: R/interp_lc_lim.R + +
                                    + +
                                    +

                                    Considering different mortality input for each sex/year data, +smooth older ages with makeham or kannisto in case no law was specified, +and return a data.frame with standard LT.

                                    +
                                    + + 
                                    lt_smooth_ambiguous(input, ...)
                                    + +

                                    Arguments

                                    + + + + + + + + + + +
                                    input

                                    data.frame. with cols: Date, Sex, Age, nMx (opt), nqx (opt), lx (opt)

                                    ...

                                    Other arguments to be passed on to the lt_abridged function.

                                    + +

                                    Details

                                    + +

                                    Makeham is chosen if last age is less than 90. Else Kannisto.

                                    + +
                                    + +
                                    + + +
                                    +
                                    +

                                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                    +
                                    + +
                                    +

                                    Site built with pkgdown 1.6.1.

                                    +
                                    + +
                                    +
                                    + + + + + + + + diff --git a/docs/reference/lthat.logquad.html b/docs/reference/lthat.logquad.html index b52dce612..233ea87bb 100644 --- a/docs/reference/lthat.logquad.html +++ b/docs/reference/lthat.logquad.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                    • - +
                                      • @@ -129,13 +129,13 @@
                                      • - +
                                      • - + github @@ -227,7 +227,7 @@

                                        Arg SRB -

                                        the sex ratio at birth (boys / girls), detault 1.05

                                        +

                                        the sex ratio at birth (boys / girls), default 1.05

                                        diff --git a/docs/reference/mA_swe.html b/docs/reference/mA_swe.html new file mode 100644 index 000000000..b4637a34a --- /dev/null +++ b/docs/reference/mA_swe.html @@ -0,0 +1,209 @@ + + + + + + + + +Swedish abridged mortality rates — mA_swe • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                        +
                                        + + + + +
                                        + +
                                        +
                                        +
                                        +

                                        Swedish abridged mortality rates

                                        + Source: R/data.R + +
                                        + +
                                        +

                                        Mortality rates in tidy format for each sex in dates 1990-07-01, 2000-07-01, 2010-07-01

                                        +
                                        + + 
                                        mA_swe
                                        + + +

                                        Format

                                        + +

                                        A data frame with:

                                        +
                                        Date

                                        Reference time for the rates estimate.

                                        +
                                        Age

                                        Inferior age for abridged groups. Careful: last age 100 is not an OAG

                                        +
                                        Sex

                                        Male m and female m.

                                        +
                                        nMx

                                        Mortality rates.

                                        + +
                                        + +

                                        Source

                                        + +

                                        Human Mortality Database. Retrieved 2021-20-01, from https://mortality.org

                                        + +
                                        + +
                                        + + +
                                        +
                                        +

                                        Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                        +
                                        + +
                                        +

                                        Site built with pkgdown 1.6.1.

                                        +
                                        + +
                                        +
                                        + + + + + + + + diff --git a/docs/reference/ma.html b/docs/reference/ma.html index 39a841e33..5d9b6f9b2 100644 --- a/docs/reference/ma.html +++ b/docs/reference/ma.html @@ -81,7 +81,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -89,7 +89,7 @@
                                        • - +
                                          • @@ -130,13 +130,13 @@
                                          • - +
                                          • - + github diff --git a/docs/reference/mav.html b/docs/reference/mav.html index c5f4b7f03..d112d608a 100644 --- a/docs/reference/mav.html +++ b/docs/reference/mav.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                            • - +
                                              • @@ -129,13 +129,13 @@
                                              • - +
                                              • - + github @@ -162,7 +162,7 @@

                                                Calculate the moving average (mav) over 3 or 5 years.

                                                This arithmetic smoothing technique aims to eliminate irregularities of the population pyramid by averaging values in a moving window of user-defined width.

                                                - 
                                                mav(Value, Age, n = 3, OAG = TRUE)
                                                + 
                                                mav(Value, Age, n = 3, OAG = TRUE, tails = FALSE)

                                                Arguments

                                                @@ -183,6 +183,12 @@

                                                Arg

                                                + + + +
                                                OAG

                                                logical. Whether or not the top age group is open. Default TRUE.
                                                tails

                                                logical. If set to TRUE, smaller-n moving averages are applied on both tails +such that all values are non-NA. If FALSE (default), tails are set to NA +due to the lag of moving averages.

                                                Value

                                                @@ -190,16 +196,13 @@

                                                Value

                                                Vector with the smoothed demographic counts.

                                                Details

                                                -

                                                The moving window is applied symmetrically. Data endpoints are imputed with NAs in output: the is nothing under 0 or over the highest closed age group to average with. The open age group is imputed with NA prior to calculations, since it cannot be averaged into the next lowest group. For example, for n=3, age 0 will be NA, as will the open age group and next lowest age. Age intervals are assumed uniform. This function could be used with either single or 5-year age groups.

                                                -

                                                Ages may be single or grouped, but all age intervals are assumed equal.

                                                +

                                                The moving window is applied symmetrically. By default (tails = FALSE) data endpoints are imputed with NAs in output: the is nothing under 0 or over the highest closed age group to average with. The open age group is not used in averaging, and it is returned as-is. Age intervals are assumed uniform. This function could be used with either single or 5-year age groups.

                                                +

                                                If tails is set to TRUE, then tails have been imputed using moving averages with successively smaller values of n, the cascade method.

                                                References

                                                Roger G, Waltisperger D, Corbille-Guitton C (1981). Les structures par sexe et âge en Afrique. GDA, Paris, France.

                                                -

                                                Author

                                                - -

                                                Juan Galeano

                                                Examples

                                                Pop  <-c(303583,390782,523903,458546,517996,400630,485606,325423,471481,189710,
@@ -233,8 +236,15 @@ 
                                                Examp
 #>         63         64         65         66         67         68         69 
 #>  10421.000  35938.667  36190.000  35550.667   8359.000   6344.333         NA 
 #>         70 
-#>         NA 
                                                if (FALSE) {
-nwindows <- sapply(seq(3, 11, by = 2),mav, Value = Pop, Age = Age)
+#> 160609.000 
                                                
+if (FALSE) {
+ odds <- seq(3, 11, by = 2)
+nwindows <- sapply(odds,
+                   mav, 
+                   Value = Pop, 
+                   Age = Age,
+                   OAG = TRUE,
+                   tails = FALSE)
 cols     <- gray(seq(.8, 0, length = 5))
 lwds     <- seq(3, 1, length = 5)
 plot(Age,Pop, col = "red", xlab = "Age", ylab = "The counts", pch=16,
@@ -246,6 +256,47 @@ 
                                                Examp
     lwd = lwds,
     legend = paste0("n=",seq(3,11,by=2)))
 }
+
+# For cascading smoothing on the tails:
+mav(Pop, Age, tails = TRUE)
+
                                                #>         0         1         2         3         4         5         6         7 
+#> 303583.00 402262.50 457743.67 500148.33 459057.33 468077.33 403886.33 427503.33 
+#>         8         9        10        11        12        13        14        15 
+#> 328871.33 348877.67 239452.33 266512.33 186400.00 184691.00 146875.67 149519.00 
+#>        16        17        18        19        20        21        22        23 
+#> 134015.00 163920.33 137280.33 234832.33 180296.00 222109.33 131151.00 146584.33 
+#>        24        25        26        27        28        29        30        31 
+#> 239569.33 252211.00 242532.00 142850.33 109987.67 268772.00 226576.33 242277.67 
+#>        32        33        34        35        36        37        38        39 
+#>  68403.33  70447.67 168918.33 178753.00 176459.67  72171.67  54237.67 188760.00 
+#>        40        41        42        43        44        45        46        47 
+#> 173602.67 180457.33  39273.33  36524.67  95494.00  97946.67  96946.00  34318.67 
+#>        48        49        50        51        52        53        54        55 
+#>  26680.33 131861.00 125365.33 129168.00  21978.67  20700.00  51622.00  53326.67 
+#>        56        57        58        59        60        61        62        63 
+#>  51601.00  17558.33  12818.67 116461.00 113761.00 115568.00  10834.67  10421.00 
+#>        64        65        66        67        68        69        70 
+#>  35938.67  36190.00  35550.67   8359.00   6979.25   8884.00 160609.00 
                                                
+if (FALSE) {
+# Compare
+nwindows_tails <- sapply(odds,
+                         mav, 
+                         Value = Pop, 
+                         Age = Age, 
+                         OAG = TRUE, 
+                         tails = TRUE)
+
+colnames(nwindows)        <- odds
+colnamaes(nwindows_tails) <- odds
+
+# NA triangles are completed with
+# successively smaller ns.
+head(nwindows)
+head(nwindows_tails)
+
+tail(nwindows)
+tail(nwindows_tails)
+}
                                                @@ -88,7 +88,7 @@
                                                • - +
                                                  • @@ -129,13 +129,13 @@
                                                  • - +
                                                  • - + github diff --git a/docs/reference/mig_beta.html b/docs/reference/mig_beta.html new file mode 100644 index 000000000..7e31d3c30 --- /dev/null +++ b/docs/reference/mig_beta.html @@ -0,0 +1,362 @@ + + + + + + + + +Estimate intercensal migration by comparing census population, by age and +sex, to the results of a RUP projection. — mig_beta • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                    +
                                                    + + + + +
                                                    + +
                                                    +
                                                    +
                                                    +

                                                    Estimate intercensal migration by comparing census population, by age and +sex, to the results of a RUP projection.

                                                    + Source: R/mig_beta.R + +
                                                    + +
                                                    +

                                                    This methods projects population from the first starting point to next census +without migration and computes the "Net Census Error" (NCE) which is +Census - Estimate by age from projection. It then distributes the NCE over +the cohort parallelogram assuming uniform distribution assuming it is all +migration. It finalizes by summing the estimate by age groups across the entire +intercensal period to have a total migration during the entire period. +Alternatively, a child adjustment and an old age adjustment can be applied.

                                                    +
                                                    + + 
                                                    mig_beta(
+  c1,
+  c2,
+  date1,
+  date2,
+  age1 = 1:length(c1) - 1,
+  age2 = 1:length(c2) - 1,
+  dates_out = NULL,
+  lxMat = NULL,
+  age_lx = NULL,
+  dates_lx = NULL,
+  births = NULL,
+  years_births = NULL,
+  location = NULL,
+  sex = "both",
+  midyear = FALSE,
+  verbose = TRUE,
+  child_adjust = c("none", "cwr", "constant"),
+  childage_max = NULL,
+  cwr_factor = 0.3,
+  oldage_adjust = c("none", "beers", "mav"),
+  oldage_min = 65,
+  ...
+)
                                                    + +

                                                    Arguments

                                                    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                    c1

                                                    numeric vector. The first (left) census in single age groups

                                                    c2

                                                    numeric vector. The second (right) census in single age groups

                                                    date1

                                                    reference date of c1`. Either a Date class object or an unambiguous character string in the format "YYYY-MM-DD".

                                                    date2

                                                    reference date of c2`. Either a Date class object or an unambiguous character string in the format "YYYY-MM-DD".

                                                    age1

                                                    integer vector. single ages of c1

                                                    age2

                                                    integer vector. single ages of c2

                                                    dates_out

                                                    vector of desired output dates coercible to numeric using dec.date()

                                                    lxMat

                                                    numeric matrix containing lifetable survivorship, l(x). Each row is an age group and each column a time point. At least two intercensal time points needed.

                                                    age_lx

                                                    integer vector. Age classes in lxMat

                                                    dates_lx

                                                    date, character, or numeric vector of the column time points for lxMat. If these are calendar-year estimates, then you can choose mid-year time points

                                                    births

                                                    integer vector. Raw birth counts for the corresponding (sub)-population, one value per each year of the intercensal period including both census years. The first and last years should include all births in the given year; don't discount them in advance.

                                                    years_births

                                                    numeric vector of calendar years of births.

                                                    location

                                                    country name or LocID

                                                    sex

                                                    character string, either "male", "female", or "both"

                                                    midyear

                                                    logical. FALSE means all Jan 1 dates between date1 and date2 are returned. TRUE means all July 1 intercensal dates are returned.

                                                    verbose

                                                    logical. Shall we send informative messages to the console?

                                                    child_adjust

                                                    The method with which to adjust the youngest age groups. +If "none", no adjustment is applied (default). If +child-woman ratio ("cwr") is chosen, the first cohorts reflecting the +difference between date2 - date1 are adjusted (plus age 0). If +child constant ratio ("constant") is chosen, the first 15 age groups +are adjusted.

                                                    childage_max

                                                    The maximum age from which to apply child_adjust. +By default, set to NULL, which gets translated into all the cohorts +between date2 and date1. If date2 is 2010 and +date1 is 2002, the first 8 cohorts are adjusted. Otherwise, the user +can supply an integer.

                                                    cwr_factor

                                                    A numeric between 0 and 1 to which adjust the CWR method +for the young ages from child_adjust. This is only used +when child_adjust is "cwr".

                                                    oldage_adjust

                                                    The type of adjustment to apply to ages at and above +oldage_min. 'beers' applies a beers graduation method +while 'mav' applies a moving average with cascading on the tails. +For more information see ?mav and ?graduation_beers.

                                                    oldage_min

                                                    The minimum age from which to apply oldage_adjust. +By default, set to 65, so any adjustment from oldage_adjust will be +applied for 65+.

                                                    ...

                                                    optional arguments passed to lt_single_qx

                                                    + +

                                                    Value

                                                    + +

                                                    a numeric vector of the total migration in the intercensal period +for each age. Ages are set as names of each migration estimate.

                                                    + +

                                                    Examples

                                                    + 
                                                    
+if (FALSE) {
+
+mig_beta(
+  location = "Russian Federation",
+  sex = "male",
+  c1 = pop1m_rus2002,
+  c2 = pop1m_rus2010,
+  date1 = "2002-10-16",
+  date2 = "2010-10-25",
+  age1 = 0:100,
+  births = c(719511L, 760934L, 772973L, 749554L, 760831L, 828772L, 880543L, 905380L, 919639L)
+)
+}
+
                                                    +
                                                    + +
                                                    + + +
                                                    +
                                                    +

                                                    Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                                    +
                                                    + +
                                                    +

                                                    Site built with pkgdown 1.6.1.

                                                    +
                                                    + +
                                                    +
                                                    + + + + + + + + diff --git a/docs/reference/mig_calculate_rc.html b/docs/reference/mig_calculate_rc.html index 4a757a7e9..46a79b470 100644 --- a/docs/reference/mig_calculate_rc.html +++ b/docs/reference/mig_calculate_rc.html @@ -81,7 +81,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -89,7 +89,7 @@
                                                    • - +
                                                      • @@ -130,13 +130,13 @@
                                                      • - +
                                                      • - + github @@ -182,7 +182,7 @@

                                                        Arg

                                                        Details

                                                        In the full 13 parameter model, the migration rate at age x, \(m(x)\) is defined as -$$m(x) = a1*exp(-1*alpha1*x) + a2*exp(-1*alpha2*(x - mu2) - exp(-1*lambda2*(x - mu2))) + a3*exp(-1*alpha3*(x - 3) - exp(-1*lambda3*(x - mu3))) + a4*exp(lambda4*x) + c$$

                                                        +$$m(x) = a1*exp(-1*alpha1*x) + a2*exp(-1*alpha2*(x - mu2) - exp(-1*lambda2*(x - mu2))) + a3*exp(-1*alpha3*(x - mu3) - exp(-1*lambda3*(x - mu3))) + a4*exp(lambda4*x) + c$$

                                                        The first, second, third and fourth pieces of the equation represent pre-working age, working age, retirement and post-retirement age patterns, respectively. Models with less parameters gradually remove terms at the older ages. Parameters in each family are:

                                                        • pre-working age: a1, alpha1

                                                          • diff --git a/docs/reference/mig_estimate_rc.html b/docs/reference/mig_estimate_rc.html index 84b3ce3f4..27a422965 100644 --- a/docs/reference/mig_estimate_rc.html +++ b/docs/reference/mig_estimate_rc.html @@ -81,7 +81,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -89,7 +89,7 @@
                                                          • - +
                                                            • @@ -130,13 +130,13 @@
                                                            • - +
                                                            • - + github diff --git a/docs/reference/mig_resid.html b/docs/reference/mig_resid.html new file mode 100644 index 000000000..ae050780c --- /dev/null +++ b/docs/reference/mig_resid.html @@ -0,0 +1,1082 @@ + + + + + + + + +Estimate net migration using residual methods: stock change, +time even flow and cohort even flow — mig_resid • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                              +
                                                              + + + + +
                                                              + +
                                                              +
                                                              +
                                                              +

                                                              Estimate net migration using residual methods: stock change, +time even flow and cohort even flow

                                                              + Source: R/mig_resid.R + +
                                                              + +
                                                              +

                                                              Estimate net migration using residual methods: stock change, +time even flow and cohort even flow

                                                              +
                                                              + + 
                                                              mig_resid(
+  pop_m_mat,
+  pop_f_mat,
+  sr_m_mat,
+  sr_f_mat,
+  asfr_mat,
+  srb_vec,
+  ages = NULL,
+  ages_asfr = NULL,
+  years_pop = NULL,
+  years_sr = NULL,
+  years_asfr = NULL,
+  years_srb = NULL,
+  verbose = TRUE,
+  method = c("stock", "cohort", "time")
+)
+
+mig_resid_stock(
+  pop_m_mat,
+  pop_f_mat,
+  sr_m_mat,
+  sr_f_mat,
+  asfr_mat,
+  srb_vec,
+  ages = NULL,
+  ages_asfr = NULL,
+  years_pop = NULL,
+  years_sr = NULL,
+  years_asfr = NULL,
+  years_srb = NULL,
+  verbose = TRUE
+)
+
+mig_resid_cohort(
+  pop_m_mat,
+  pop_f_mat,
+  sr_m_mat,
+  sr_f_mat,
+  asfr_mat,
+  srb_vec,
+  ages = NULL,
+  ages_asfr = NULL,
+  years_pop = NULL,
+  years_sr = NULL,
+  years_asfr = NULL,
+  years_srb = NULL,
+  verbose = TRUE
+)
+
+mig_resid_time(
+  pop_m_mat,
+  pop_f_mat,
+  sr_m_mat,
+  sr_f_mat,
+  asfr_mat,
+  srb_vec,
+  ages = NULL,
+  ages_asfr = NULL,
+  years_pop = NULL,
+  years_sr = NULL,
+  years_asfr = NULL,
+  years_srb = NULL,
+  verbose = TRUE
+)
                                                              + +

                                                              Arguments

                                                              + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                              pop_m_mat

                                                              A numeric matrix with population counts. Rows should +be ages and columns should be years. Only five year age groups are supported. +See examples.

                                                              pop_f_mat

                                                              A numeric matrix with population counts. Rows should +be ages and columns should be years. Only five year age groups are supported. +See examples.

                                                              sr_m_mat

                                                              A numeric matrix with survival rates for males. Rows +should be ages and columns should be years. ** This matrix should have +one column less than pop_m_mat and pop_f_mat. For example, +if the last year in these matrices is 2050, then the last year in +sr_m_mat should be 2045. **

                                                              sr_f_mat

                                                              A numeric matrix with survival rates for females. Rows +should be ages and columns should be years. ** This matrix should have +one column less than pop_m_mat and pop_f_mat. For example, +if the last year in these matrices is 2050, then the last year in +sr_f_mat should be 2045. **.

                                                              asfr_mat

                                                              A numeric matrix with age specific fertility rates. +Rows should be ages and columns should be years. ** This matrix should have +one column less than pop_m_mat and pop_f_mat. For example, +if the last year in these matrices is 2050, then the last year in +asfr_mat should be 2045**. This row will usually have fewer age groups +(rows) than in the population matrices or survival matrices, so the user +needs to supply the specific ages in the ages_asfr argument.

                                                              srb_vec

                                                              A numeric vector of sex ratios at birth for every year. +The years should be the same as the years in sr_m_mat, +sr_f_mat, and asfr_mat.

                                                              ages

                                                              A numeric vector of ages used in the rows in +pop_m_mat, pop_f_mat, sr_m_mat, sr_f_mat.

                                                              ages_asfr

                                                              A numeric vector of ages used in the rows in +asfr_mat.

                                                              years_pop

                                                              Years used in the column names of population. If +pop_m_mat or pop_f_mat doesn't have column names, these +names are used. Otherwise ignored.

                                                              years_sr

                                                              Years used in the column names of survival rates. If +sr_r_mat doesn't have column names, these names are used. Otherwise +ignored.

                                                              years_asfr

                                                              Years used in the column names of age-specific fertility +rate. If codeasfr_r_mat doesn't have column names, these names are used. +Otherwise ignored.

                                                              years_srb

                                                              Years used in the column names of sex-ratio at birth. If +srb_r_mat is not named, these names are used. Otherwise ignored.

                                                              verbose

                                                              Default set to TRUE. If TRUE, the function prints important +operations carried out in the function like if years were trimmed from the +data.

                                                              method

                                                              which residual migration method to use. This only works when +using mig_resid and the possible options are 'stock', 'cohort' and +'time', with 'stock' being the default.

                                                              + +

                                                              Value

                                                              + +

                                                              A list with two matrices. One is for males (called mig_m) and the +other for females (called mig_f). Both matrices contain net migration +estimates by age/period using one of the three methods.

                                                              +

                                                              Details

                                                              + + +
                                                                +

                                                              1. The stock method (mig_resid_stock) is the difference in stocks that +survive between t and t+5, and the first age group is based on the difference +with the surviving births by sex. It provides net migrants by lexis cohort +parallelograms, and basically such info gets used as end-period migration +since the migrants don't get exposed to mortality within the period.

                                                                2. +

                                                              2. The time even flow (mig_resid_time) method uses the result from +the first option, but splits it back into lexis period squares and assumes +that half of the net migrants get exposed to the mortality risk during this +period. Such info can get used as evenly distributed migration by period, +but the assumptions lead to zig-zag age patterns that are highly implausible.

                                                                3. +

                                                              3. The cohort even flow (mig_resid_cohort) method provides the most +meaningful pattern of net migration by age consistent by cohort and assumes +an evenly distribution within the 5-year period, and half of the migrants +get exposed both fertility and mortality within this period.

                                                                4. +
                                                              + +

                                                              mig_resid is a general function able to call the three methods only by +specifying the method argument. By default it is set to the +stock method. See the examples section.

                                                              + +

                                                              Examples

                                                              + 
                                                              
+library(DemoTools)
+
+# The data is loaded with DemoTools
+
+################ Stock change method #####################
+
+# Generic mig_resid method which allows to choose either stock,
+# cohort or time method for five year ages groups
+
+mig_res <-
+ mig_resid(
+   pop_m_mat = pop_m_mat_five,
+   pop_f_mat = pop_f_mat_five,
+   sr_m_mat = sr_m_mat_five,
+   sr_f_mat = sr_f_mat_five,
+   asfr_mat = asfr_mat_five,
+   srb_vec = srb_vec_five,
+   ages = ages_five,
+   ages_asfr = ages_asfr_five,
+   # With the stock method
+   method = "stock"
+ )
+
                                                              #> Using stock residual migration method
                                                              
+# For single ages
+
+mig_res <-
+ mig_resid(
+   pop_m_mat = pop_m_mat_single,
+   pop_f_mat = pop_f_mat_single,
+   sr_m_mat = sr_m_mat_single,
+   sr_f_mat = sr_f_mat_single,
+   asfr_mat = asfr_mat_single,
+   srb_vec = srb_vec_single,
+   ages = ages_single,
+   ages_asfr = ages_asfr_single,
+   # With the stock method
+   method = "stock"
+ )
+
                                                              #> Using stock residual migration method
                                                              
+# Or directly the mid_resid_stock function
+# (works for both single and five year age groups)
+
+mig_res <-
+ mig_resid_stock(
+   pop_m_mat = pop_m_mat_five,
+   pop_f_mat = pop_f_mat_five,
+   sr_m_mat = sr_m_mat_five,
+   sr_f_mat = sr_f_mat_five,
+   asfr_mat = asfr_mat_five,
+   srb_vec = srb_vec_five,
+   ages = ages_five,
+   ages_asfr = ages_asfr_five
+ )
+
+# Net migration for males using stock change method
+mig_res$mig_m
+
                                                              #>              1950          1955          1960          1965          1970
+#> 0    1.7899322803  3.919021e+00 -5.8543069648 12.9092374640  2.921119e+01
+#> 5   51.6027045000  5.385321e+01 22.2235544078 22.9335351570  3.470664e+01
+#> 10  29.1541335600  2.840815e+01  8.0306470720 19.3499225033  3.062147e+01
+#> 15   7.3711371748  4.381831e+00 -5.0199593568 15.8034252562  3.131827e+01
+#> 20  21.6150524800  1.566065e+01 -2.0994925197 40.1160914300  5.180611e+01
+#> 25  47.8647752800  6.026311e+01 23.3854103582 84.5010463454  8.870085e+01
+#> 30  37.0250555000  4.779935e+01 15.9943014370 54.4810744403  5.262150e+01
+#> 35  37.0572080800  3.955049e+01  9.4550107496 26.9198029920  2.392796e+01
+#> 40  26.6996119446  1.316362e+01  0.9599276814  8.9668772707  1.721723e+01
+#> 45  19.3238383059  8.535687e+00 -5.7535831006  7.5537018836  4.394683e+00
+#> 50  10.8706376948  5.802709e+00  0.6788420915  0.5444807024  1.053791e+01
+#> 55  -0.4710838276  2.382475e+00 -3.2403068092  6.2785759004 -7.931068e-01
+#> 60  -2.6739240018 -5.165887e-01  0.4397308595  6.8077825892  9.082899e+00
+#> 65   8.2682526723  9.950218e+00  4.2594462540  9.7035767336  8.412578e+00
+#> 70   5.1050824363  5.938504e+00  5.7341530320  1.5588869257  6.650109e+00
+#> 75  -0.5308153600  3.023748e-01 -0.9342732742 -1.0616547642  1.687302e-01
+#> 80   2.6458591680  2.923921e+00  2.3863401360  3.0909371135 -6.546424e-01
+#> 85  -0.0002322246  6.833100e-05 -0.0016322983  0.0003876803 -2.982705e-04
+#> 90  -0.0007090603 -4.370166e-04  0.0007138143  0.0007593676  4.629872e-05
+#> 95  -0.0004184120  8.371022e-05 -0.0002732981 -0.0013148423 -9.538320e-05
+#> 100  0.0003631314 -3.905702e-04  0.0004240167 -0.0001150535  1.791806e-04
+#>              1975          1980          1985          1990          1995
+#> 0   14.4819380766 15.0050326629 30.6160862966  2.279134e+01 33.4094386250
+#> 5   24.2440961290 14.9144048249 39.4586215291  3.366381e+01 27.6300354900
+#> 10  19.8909763056 12.9976172834 31.3140753898  3.926617e+01 35.2017492389
+#> 15  34.5412167950 31.1408714290 53.7835608961  5.806982e+01 56.4028387060
+#> 20  35.7746574910 48.8141184045 46.6091830651  3.355813e+01 46.6090142025
+#> 25  12.2320674097 15.7490655447 36.0248896021  2.994211e+01 35.2341965234
+#> 30  15.2768180385 15.1454438950 48.9912652841  2.939288e+01 43.0299390765
+#> 35   3.4133017642  2.6512718093 35.7081057973  2.377502e+01 38.0465234759
+#> 40  11.3648881127  0.9157143211 26.0002282136  1.815304e+01 25.2147939559
+#> 45  -0.9025899903 -6.1414794104  9.4697258728  9.669273e+00 15.3801489305
+#> 50   4.1228516807  1.9180957545 11.4602048293  7.420501e-01  9.1744221357
+#> 55   2.8324768102  8.0029005255 13.9249297918  9.461663e+00  7.0314765200
+#> 60   5.9587573287  6.1901407708 12.8552522823  1.254064e+01  5.9398053623
+#> 65   5.0265223258  6.1334063238 15.3105496329  6.693441e+00  6.2767061297
+#> 70   3.0667904029  2.1040520686  8.0829480782  9.051541e-01  4.9118127171
+#> 75   1.1085509966  0.4440585403  4.6902499048 -1.740263e+00  1.6956094777
+#> 80  -0.3545171554 -1.0788234380 -0.8912981728 -3.259234e+00  1.0782154117
+#> 85  -0.0007250264 -0.0003707507  0.0009264276 -1.502028e-03  0.0006504005
+#> 90  -0.0010096296 -0.0013578629 -0.0001364608 -1.536187e-03  0.0002672308
+#> 95  -0.0005597711  0.0002593028  0.0006724129  4.940180e-06  0.0000220428
+#> 100 -0.0002663077  0.0002379727  0.0001025889 -1.467865e-04 -0.0004672003
+#>              2000          2005          2010          2015          2020
+#> 0   35.2063149299  2.574073e+01  1.686618e+01  3.726188e+01  3.596241e+01
+#> 5   39.7815636351  5.198246e+01  3.545773e+01  1.621746e+01  1.572724e+01
+#> 10  43.6134320305  4.087724e+01  2.529691e+01  1.243047e+01  1.205461e+01
+#> 15  69.4862261899  6.151655e+01  1.053807e+02  6.337355e+01  6.148319e+01
+#> 20  61.4795683305  1.081416e+02  1.462701e+02  1.140761e+02  1.106660e+02
+#> 25  40.9816130294  9.212637e+01  2.026667e+01  1.096321e+02  1.063574e+02
+#> 30  54.2000383773  6.484557e+01  5.491794e+01  8.303648e+01  8.055508e+01
+#> 35  54.3621106693  6.210615e+01  4.419383e+01  5.759037e+01  5.586794e+01
+#> 40  46.1009769761  4.050389e+01  2.708597e+01  3.866853e+01  3.751901e+01
+#> 45  24.3645150956  3.725019e+01  8.129041e+00  2.565676e+01  2.489228e+01
+#> 50  19.0808001159  1.891404e+01  1.625201e+01  1.695107e+01  1.644513e+01
+#> 55   0.6483630675  1.268029e+01  3.792549e+01  1.118371e+01  1.085143e+01
+#> 60  15.0435641770  1.036964e+01  1.188830e+01  7.376952e+00  7.159785e+00
+#> 65   6.2874481579  8.037788e+00  3.314290e+01  4.860257e+00  4.720100e+00
+#> 70   1.6845365642  1.544022e+00  1.088919e+00  3.201408e+00  3.110899e+00
+#> 75   1.7851618553  2.240903e+00  2.708415e+00  2.114225e+00  2.050721e+00
+#> 80   4.4904038435  1.151357e+01 -2.168209e+00  1.393847e+00  1.351674e+00
+#> 85   0.0003877412 -3.414889e-04  1.892320e-03  1.947218e-03 -5.815492e-05
+#> 90  -0.0002732069  3.774664e-04 -1.778366e-03 -4.196944e-04  6.462397e-04
+#> 95   0.0003672265 -2.385004e-04 -1.187608e-03  9.547959e-04 -4.276800e-04
+#> 100  0.0004257843  1.591630e-04 -5.068183e-05 -3.603256e-04 -1.501552e-04
+#>             2025          2030          2035          2040          2045
+#> 0   3.713937e+01  3.872296e+01  38.710244991  3.875942e+01  3.884919e+01
+#> 5   1.624346e+01  1.692868e+01  16.922542527  1.694990e+01  1.698616e+01
+#> 10  1.245485e+01  1.298092e+01  12.972254791  1.299493e+01  1.303085e+01
+#> 15  6.348393e+01  6.617035e+01  66.152791816  6.626290e+01  6.641678e+01
+#> 20  1.142679e+02  1.191112e+02 119.075586720  1.192639e+02  1.195424e+02
+#> 25  1.098159e+02  1.144742e+02 114.442603943  1.146180e+02  1.148904e+02
+#> 30  8.317144e+01  8.669744e+01  86.667686177  8.681930e+01  8.701090e+01
+#> 35  5.769732e+01  6.012598e+01  60.113368482  6.020745e+01  6.034099e+01
+#> 40  3.874399e+01  4.037813e+01  40.365452745  4.042687e+01  4.052742e+01
+#> 45  2.570411e+01  2.679773e+01  26.784212118  2.683642e+01  2.688482e+01
+#> 50  1.699112e+01  1.771062e+01  17.697703877  1.773515e+01  1.777363e+01
+#> 55  1.120040e+01  1.167745e+01  11.672268085  1.169656e+01  1.172089e+01
+#> 60  7.386918e+00  7.707266e+00   7.697176849  7.715443e+00  7.730054e+00
+#> 65  4.878769e+00  5.078950e+00   5.072805619  5.084954e+00  5.100982e+00
+#> 70  3.212492e+00  3.348749e+00   3.347115246  3.356025e+00  3.363517e+00
+#> 75  2.116043e+00  2.206720e+00   2.205534484  2.206645e+00  2.212107e+00
+#> 80  1.392679e+00  1.453848e+00   1.454611333  1.461609e+00  1.459596e+00
+#> 85  5.095878e-04  1.592002e-03  -0.003113428 -1.789957e-04 -1.280066e-03
+#> 90  6.580606e-04 -6.258823e-04   0.001700642 -2.324909e-03  1.481708e-03
+#> 95  5.002212e-05 -3.570606e-04  -0.001343537  1.088385e-03  9.059035e-05
+#> 100 6.132923e-05 -2.312750e-04   0.000230856 -1.040497e-04  4.127133e-05
                                                              
+# Net migration for females using stock change method
+mig_res$mig_f
+
                                                              #>             1950         1955        1960        1965       1970       1975
+#> 0   113.17293508 114.89636101 90.23263311 87.45872642 90.0432974 70.4587935
+#> 5    95.34564459  97.41671484 70.19452018 65.87207075 59.8263941 44.5359312
+#> 10   39.38203524  37.98494056 21.59118455 30.06125339 40.8511939 25.2266777
+#> 15   18.93727890  15.46635355 15.01716094 35.73093675 43.5519113 44.4185849
+#> 20   35.06866853  38.29910784 32.49304247 61.96567298 71.3823189 60.4114357
+#> 25   46.52931657  51.15055184 29.78558329 69.40305410 73.3726655 28.8506481
+#> 30   49.26659651  53.64077560 28.11913980 41.28897946 46.1446383 30.6183323
+#> 35   42.94237789  47.62602471 24.65857528 25.85740478 24.8366719 12.9039601
+#> 40   29.40340471  20.97115764 13.49470393 11.16104258 23.5821805 19.5062463
+#> 45   18.57086996  17.19342125  7.68481801 16.01699993 16.0862520 12.4982334
+#> 50   13.71191120  17.11939842 13.92770203 13.62686866 27.4480823 18.0019021
+#> 55   10.00669977  14.47415466 10.65372584 22.55101461 15.3631445 21.7322432
+#> 60   11.50849438  12.18200068 18.50430067 27.64042225 28.9167882 29.1498623
+#> 65   22.50025175  25.23334367 26.44347204 35.00252644 34.6956380 36.6317503
+#> 70   29.00954957  31.29857322 32.48545915 32.31809795 32.5289233 38.7470346
+#> 75   16.82217097  18.04473806 21.47476384 30.18757231 33.0546994 39.0628759
+#> 80   19.08659895  21.79087700 26.53310358 31.16165091 30.7052393 36.2665503
+#> 85    7.00799770   9.63018174 11.75234533 16.01617288 19.8479281 24.8705826
+#> 90    2.73784736   3.09952968  4.37523028  5.83026818  8.0543360 11.6835028
+#> 95    0.47757027   0.63464332  0.73783617  1.16624771  1.6228540  2.8761175
+#> 100   0.03766896   0.04934962  0.06275325  0.08664575  0.1430509  0.2940806
+#>           1980       1985       1990       1995       2000        2005
+#> 0   65.4905895 76.3891331 68.7130184 70.2702199 57.8078009  48.4244763
+#> 5   33.0373278 54.3669316 47.8767793 41.8410746 46.4490916  53.0134297
+#> 10  23.5971948 35.5482798 42.9332129 37.8361399 48.9140770  41.0945558
+#> 15  30.0666391 49.2268809 54.1049977 53.0111697 61.1189534  59.3054565
+#> 20  51.8729917 66.5234561 53.3486767 57.1899226 80.8186413 108.2549385
+#> 25  24.8247651 59.2788922 49.3469215 48.0041198 67.9264170 111.1587027
+#> 30  30.4020995 63.2339790 44.9045881 54.5849296 74.0834107 103.0106675
+#> 35  13.7500933 46.1317183 42.2221569 54.2424859 64.2324081  79.7542022
+#> 40  16.8177810 42.4617981 35.6323975 44.6059232 63.2572127  59.0680211
+#> 45  10.6764505 24.0128959 31.6798027 36.3022661 41.1520887  57.4674939
+#> 50  19.1201073 21.6856843 25.6122443 28.1799534 37.8505654  39.2408961
+#> 55  27.1662997 25.9759242 32.3966763 25.4397052 35.6465904  39.5720843
+#> 60  31.4169027 32.3333678 33.6616631 29.4462690 42.3024418  45.1001209
+#> 65  38.5246016 46.0765985 37.8977766 37.4361944 40.0309377  44.4626225
+#> 70  41.3330306 45.5233111 45.3688341 47.3005896 48.0556069  45.6277274
+#> 75  43.8517132 52.2627443 48.9690557 59.9854503 61.9408546  63.0543494
+#> 80  39.1634375 44.2990904 50.6981503 60.8224943 76.3761824  86.1956876
+#> 85  30.2454966 34.3783368 40.9716493 47.5660261 54.0094551  70.3597063
+#> 90  15.3906445 17.5112915 20.6083492 22.8002233 27.7876959  34.3398055
+#> 95   4.4186387  5.1672347  5.9996563  6.0220990  6.9467713   9.5562259
+#> 100  0.5528349  0.7229901  0.8275142  0.6994879  0.6407134   0.8698355
+#>           2010        2015          2020          2025          2030
+#> 0    40.893862  54.2482009  3.639646e+01  3.756888e+01  3.915000e+01
+#> 5    44.012591  21.6549536  1.571378e+01  1.622971e+01  1.691831e+01
+#> 10   28.495198  15.8682358  1.285681e+01  1.327584e+01  1.383276e+01
+#> 15   93.817691  72.3096374  6.796569e+01  7.018092e+01  7.315433e+01
+#> 20  150.599940 125.3306506  1.182762e+02  1.221251e+02  1.272877e+02
+#> 25   82.384536 118.5424184  1.095158e+02  1.130743e+02  1.178537e+02
+#> 30  100.055532  89.6012339  7.987071e+01  8.246815e+01  8.595619e+01
+#> 35   66.203624  64.3219840  5.333906e+01  5.508070e+01  5.740907e+01
+#> 40   51.479253  46.0958958  3.449321e+01  3.560806e+01  3.711957e+01
+#> 45   25.465817  34.5174228  2.203458e+01  2.275099e+01  2.371591e+01
+#> 50   36.408361  29.2195403  1.402299e+01  1.447923e+01  1.509269e+01
+#> 55   46.657383  32.4093781  8.899044e+00  9.189698e+00  9.580706e+00
+#> 60   37.500438  38.5073520  5.651936e+00  5.833683e+00  6.079587e+00
+#> 65   53.185508  47.8148003  3.591765e+00  3.699831e+00  3.864664e+00
+#> 70   55.705684  62.3853696  2.279530e+00  2.350295e+00  2.453483e+00
+#> 75   65.979261  71.5828801  1.447269e+00  1.494431e+00  1.554029e+00
+#> 80   75.191382  78.4085394  9.191045e-01  9.422448e-01  9.851964e-01
+#> 85   75.580026  73.6692924  2.023718e-03 -1.309560e-03 -3.205581e-03
+#> 90   44.131329  45.7727592 -4.575784e-05 -1.760394e-03  5.530703e-05
+#> 95   11.032250  13.0486004 -6.680146e-04  1.115547e-03  2.261978e-04
+#> 100   0.919266   0.8355002 -3.823113e-06 -6.037463e-04  1.228691e-05
+#>              2035          2040          2045
+#> 0    3.915065e+01  3.921944e+01  3.931634e+01
+#> 5    1.691457e+01  1.693440e+01  1.697581e+01
+#> 10   1.383380e+01  1.385223e+01  1.388592e+01
+#> 15   7.313045e+01  7.324795e+01  7.341229e+01
+#> 20   1.272581e+02  1.274533e+02  1.277532e+02
+#> 25   1.178278e+02  1.180086e+02  1.182870e+02
+#> 30   8.594408e+01  8.607590e+01  8.627359e+01
+#> 35   5.739172e+01  5.748318e+01  5.762230e+01
+#> 40   3.711233e+01  3.716508e+01  3.724629e+01
+#> 45   2.371210e+01  2.374261e+01  2.380269e+01
+#> 50   1.509005e+01  1.510669e+01  1.514986e+01
+#> 55   9.583997e+00  9.598664e+00  9.619318e+00
+#> 60   6.081444e+00  6.084633e+00  6.102345e+00
+#> 65   3.859534e+00  3.866878e+00  3.879600e+00
+#> 70   2.451742e+00  2.453048e+00  2.460003e+00
+#> 75   1.554556e+00  1.562182e+00  1.559817e+00
+#> 80   9.835399e-01  9.827943e-01  9.950495e-01
+#> 85   2.799884e-03  3.560486e-03 -4.231530e-03
+#> 90  -7.542806e-04  2.292086e-03 -1.102522e-03
+#> 95   7.982875e-04  2.063124e-03 -4.913987e-04
+#> 100 -5.497503e-04 -2.754639e-04 -2.854814e-05
                                                              
+
+################ cohort even flow method  #####################
+
+# We reuse the same data from before
+# Either use the generic mig_resid choosing 'cohort'
+
+# Five year age groups
+mig_res <-
+ mig_resid(
+   pop_m_mat = pop_m_mat_five,
+   pop_f_mat = pop_f_mat_five,
+   sr_m_mat = sr_m_mat_five,
+   sr_f_mat = sr_f_mat_five,
+   asfr_mat = asfr_mat_five,
+   srb_vec = srb_vec_five,
+   ages = ages_five,
+   ages_asfr = ages_asfr_five,
+   # With the cohort method
+   method = "cohort"
+ )
+
                                                              #> Using cohort residual migration method
                                                              
+# Single ages
+mig_res <-
+ mig_resid(
+   pop_m_mat = pop_m_mat_single,
+   pop_f_mat = pop_f_mat_single,
+   sr_m_mat = sr_m_mat_single,
+   sr_f_mat = sr_f_mat_single,
+   asfr_mat = asfr_mat_single,
+   srb_vec = srb_vec_single,
+   ages = ages_single,
+   ages_asfr = ages_asfr_single,
+   # With the stock method
+   method = "cohort"
+ )
+
                                                              #> Using cohort residual migration method
                                                              
+# Or directly the mid_resid_cohort function
+
+mig_res <-
+  mig_resid_cohort(
+    pop_m_mat = pop_m_mat_five,
+    pop_f_mat = pop_f_mat_five,
+    sr_m_mat = sr_m_mat_five,
+    sr_f_mat = sr_f_mat_five,
+    asfr_mat = asfr_mat_five,
+    srb_vec = srb_vec_five,
+    ages = ages_five,
+    ages_asfr = ages_asfr_five
+  )
+
+# Net migration for males using the cohort even flow method
+mig_res$mig_m
+
                                                              #>              1950          1955          1960          1965          1970
+#> 0   27.7345854720 31.0019371482  5.1942053540 24.5624659219  4.688525e+01
+#> 5   40.5102116592 41.2343357598 15.1606536999 21.1793513869  3.271567e+01
+#> 10  18.3013151966 16.4222220410  1.5058793365 17.6053516384  3.102412e+01
+#> 15  14.5468170638 10.0564010777 -3.5687147753 28.0614898551  4.172147e+01
+#> 20  34.8888314231 38.1140374410 10.6856647744 62.5658663403  7.055654e+01
+#> 25  42.6329532877 54.2516150063 19.7676547862 69.7666525943  7.093797e+01
+#> 30  37.2248714152 43.8717655890 12.7781665228 40.8677327075  3.842664e+01
+#> 35  32.0814535293 26.5020660760  5.2333131522 18.0380639382  2.068785e+01
+#> 40  23.2334561570 10.9459192982 -2.4258656763  8.3347645192  1.089106e+01
+#> 45  15.3265554535  7.2748524613 -2.5633574346  4.0973563278  7.587504e+00
+#> 50   5.3028125043  4.1864884603 -1.3245914367  3.5133073257  4.957193e+00
+#> 55  -1.6476700692  0.9576589788 -1.4395096532  6.8074763188  4.347471e+00
+#> 60   3.0476956367  5.0897985122  2.5236522427  8.7981552818  9.271884e+00
+#> 65   7.2975977560  8.6761911905  5.4904630231  6.0964521738  8.217822e+00
+#> 70   2.5311742841  3.4940993902  2.6452918966  0.2448806986  3.800742e+00
+#> 75   1.3981785084  2.0660658863  0.9794162929  1.3492354084 -3.171552e-01
+#> 80   1.7122472632  1.8870772095  1.5306732316  1.9744450576 -4.164296e-01
+#> 85  -0.0008180112 -0.0003418182 -0.0005657794  0.0009453795 -1.747616e-04
+#> 90  -0.0011282773 -0.0002951569  0.0003245206 -0.0008184162 -6.416606e-05
+#> 95  -0.0009690561  0.0001945259 -0.0006136342 -0.0029657259 -2.079006e-04
+#> 100  0.0003631314 -0.0003905702  0.0004240167 -0.0001150535  1.791806e-04
+#>              1975          1980          1985          1990          1995
+#> 0   26.7290464322 22.5514135124 50.5005203859  3.971947e+01  4.733699e+01
+#> 5   22.0962653344 13.9694166410 35.4141109601  3.648698e+01  3.143147e+01
+#> 10  27.2613524622 22.0997423995 42.5971117509  4.871322e+01  4.583846e+01
+#> 15  35.2720807678 40.0886284255 50.3067733763  4.589406e+01  5.158882e+01
+#> 20  24.1056789950 32.3931085097 41.4420067581  3.183571e+01  4.101680e+01
+#> 25  13.8048985375 15.4971051546 42.6358411330  2.975410e+01  3.922889e+01
+#> 30   9.3790926968  8.9270470232 42.4876305010  2.667472e+01  4.065530e+01
+#> 35   7.4318376300  1.7905172492 30.9784476453  2.105322e+01  3.174649e+01
+#> 40   5.2620681370 -2.6361050610 17.8294359966  1.398769e+01  2.039907e+01
+#> 45   1.6409798784 -2.1235510680 10.5680414394  5.242261e+00  1.236784e+01
+#> 50   3.5528032225  5.0697335208 12.9059761124  5.190501e+00  8.201545e+00
+#> 55   4.5669024604  7.3126011011 13.7574122211  1.127484e+01  6.618932e+00
+#> 60   5.7964848148  6.4725021367 14.7392106038  9.973541e+00  6.324542e+00
+#> 65   4.3823473498  4.4122945905 12.4941899906  4.003111e+00  5.912938e+00
+#> 70   2.3424884610  1.4118350206  7.0917545044 -4.958813e-01  3.587037e+00
+#> 75   0.4223807796 -0.4160797352  2.1344354343 -2.976618e+00  1.602920e+00
+#> 80  -0.2237692930 -0.6727819681 -0.5508956693 -1.992855e+00  6.527169e-01
+#> 85  -0.0013472520 -0.0013649720  0.0005310532 -2.288892e-03  6.637464e-04
+#> 90  -0.0014153765 -0.0008447875  0.0005782918 -1.248544e-03  2.421791e-04
+#> 95  -0.0011599514  0.0005239992  0.0013807440  1.016482e-05  4.658246e-05
+#> 100 -0.0002663077  0.0002379727  0.0001025889 -1.467865e-04 -4.672003e-04
+#>             2000          2005          2010          2015          2020
+#> 0   5.521348e+01  5.181794e+01  3.464569e+01  4.546868e+01  4.391070e+01
+#> 5   4.171498e+01  4.644763e+01  3.038654e+01  1.432799e+01  1.389523e+01
+#> 10  5.658804e+01  5.122663e+01  6.537600e+01  3.792246e+01  3.678382e+01
+#> 15  6.557516e+01  8.495273e+01  1.259713e+02  8.882648e+01  8.614305e+01
+#> 20  5.133396e+01  1.003256e+02  8.339817e+01  1.120286e+02  1.086440e+02
+#> 25  4.769132e+01  7.864393e+01  3.765750e+01  9.649354e+01  9.359811e+01
+#> 30  5.441186e+01  6.361715e+01  4.964878e+01  7.043648e+01  6.832703e+01
+#> 35  5.038857e+01  5.144900e+01  3.572532e+01  4.823902e+01  4.679176e+01
+#> 40  3.538553e+01  3.904144e+01  1.766614e+01  3.227328e+01  3.130150e+01
+#> 45  2.187529e+01  2.825565e+01  1.226915e+01  2.142061e+01  2.076887e+01
+#> 50  9.953746e+00  1.595878e+01  2.736801e+01  1.418917e+01  1.375317e+01
+#> 55  8.024718e+00  1.171475e+01  2.523316e+01  9.404806e+00  9.113674e+00
+#> 60  1.095858e+01  9.441914e+00  2.309601e+01  6.246623e+00  6.051291e+00
+#> 65  4.157159e+00  4.963837e+00  1.761464e+01  4.165787e+00  4.032837e+00
+#> 70  1.879553e+00  2.032313e+00  2.029089e+00  2.804870e+00  2.709220e+00
+#> 75  3.633292e+00  7.856819e+00  2.269679e-01  1.921612e+00  1.849407e+00
+#> 80  2.648544e+00  6.637323e+00 -1.231256e+00  7.883184e-01  7.562531e-01
+#> 85  4.033675e-05  7.105796e-05 -1.498956e-04  9.007669e-04  4.340415e-04
+#> 90  1.617833e-04  5.104875e-05 -2.514495e-03  6.246262e-04  5.782513e-05
+#> 95  7.570578e-04 -4.775804e-04 -2.347887e-03  1.876386e-03 -8.238661e-04
+#> 100 4.257843e-04  1.591630e-04 -5.068183e-05 -3.603256e-04 -1.501552e-04
+#>             2025          2030          2035          2040         2045
+#> 0   4.533636e+01  4.725714e+01  4.723415e+01  4.729110e+01 4.739403e+01
+#> 5   1.435297e+01  1.495834e+01  1.495057e+01  1.497529e+01 1.501112e+01
+#> 10  3.798272e+01  3.958804e+01  3.957368e+01  3.963905e+01 3.973308e+01
+#> 15  8.893813e+01  9.269935e+01  9.266739e+01  9.281208e+01 9.302437e+01
+#> 20  1.121642e+02  1.169091e+02  1.168658e+02  1.170394e+02 1.173074e+02
+#> 25  9.662625e+01  1.007130e+02  1.006725e+02  1.008273e+02 1.010516e+02
+#> 30  7.054235e+01  7.351530e+01  7.348607e+01  7.360189e+01 7.375815e+01
+#> 35  4.831215e+01  5.033959e+01  5.031996e+01  5.039165e+01 5.050329e+01
+#> 40  3.231319e+01  3.367316e+01  3.365320e+01  3.370409e+01 3.377327e+01
+#> 45  2.144079e+01  2.234327e+01  2.232287e+01  2.236153e+01 2.239942e+01
+#> 50  1.419322e+01  1.478717e+01  1.477057e+01  1.479497e+01 1.482056e+01
+#> 55  9.393798e+00  9.787974e+00  9.772460e+00  9.787043e+00 9.800488e+00
+#> 60  6.235955e+00  6.491396e+00  6.475070e+00  6.483358e+00 6.492494e+00
+#> 65  4.154458e+00  4.317637e+00  4.305202e+00  4.308499e+00 4.313789e+00
+#> 70  2.783655e+00  2.892001e+00  2.881492e+00  2.878664e+00 2.878182e+00
+#> 75  1.892981e+00  1.963852e+00  1.953794e+00  1.949436e+00 1.943300e+00
+#> 80  7.721803e-01  8.009601e-01  7.934284e-01  7.943901e-01 7.884978e-01
+#> 85  7.786621e-04  5.130995e-04 -6.648082e-04 -1.703315e-03 2.604560e-04
+#> 90  5.174199e-04 -7.749870e-04 -5.907210e-05 -6.034064e-04 1.089173e-03
+#> 95  9.482005e-05 -6.681631e-04 -2.483761e-03  1.989245e-03 1.637868e-04
+#> 100 6.132923e-05 -2.312750e-04  2.308560e-04 -1.040497e-04 4.127133e-05
                                                              
+# Net migration for females using the cohort even flow method
+mig_res$mig_f
+
                                                              #>             1950         1955         1960         1965        1970       1975
+#> 0   171.72131816 173.14456142 131.77820968 125.77017105 124.6968682 95.9520589
+#> 5    69.20081450  69.28238640  46.84641331  48.75618851  50.9922442 35.2853877
+#> 10   29.46477771  26.96618336  18.44416874  33.10558630  42.4374326 34.9937120
+#> 15   27.29600628  27.13733711  23.95068047  49.19164496  57.8183162 52.7039012
+#> 20   41.33304378  45.22976783  31.44274248  66.24574140  72.9147253 44.9214309
+#> 25   48.61860483  53.07861527  29.28367497  55.88984313  60.2716586 29.9682458
+#> 30   46.88561716  51.38378241  26.73500957  33.95470778  35.8411804 21.9525937
+#> 35   36.86298992  34.86859624  19.36135344  18.75110168  24.4968027 16.3825468
+#> 40   24.51040044  19.45753274  10.77528557  13.81136654  20.1137017 16.2070083
+#> 45   16.56535817  17.57029029  11.05335164  15.11774766  22.1790338 15.5127441
+#> 50   12.26265055  16.29327790  12.64429657  18.59839574  21.9134313 20.3315864
+#> 55   11.30080079  13.92818445  15.23128103  26.10027153  22.9939702 26.3244627
+#> 60   18.40400698  20.13441348  24.01142703  33.27825496  33.6423294 34.6656953
+#> 65   29.00224834  31.55144157  32.65724529  36.93668833  36.6712748 40.9309041
+#> 70   27.17509206  28.94143981  31.42783859  36.27914530  37.7960682 44.4445436
+#> 75   23.83314481  26.11712003  31.07521196  39.00073414  39.9437017 46.6140330
+#> 80   19.13202079  22.82358621  27.36889447  33.37685139  35.5223684 42.3677161
+#> 85    8.57065143  10.90765432  13.64065673  18.13800515  22.8559186 29.5426976
+#> 90    3.41815884   3.91939256   5.21967120   7.05826177   9.5714802 14.1771134
+#> 95    1.25221623   1.63514702   1.86297785   2.89364187   3.9438894  6.8026913
+#> 100   0.03766896   0.04934962   0.06275325   0.08664575   0.1430509  0.2940806
+#>           1980        1985       1990       1995       2000        2005
+#> 0   84.4975756 106.1761668 94.7745340 93.0428964 82.3545273  75.8669558
+#> 5   28.5730084  45.3124335 45.7159530 40.0648036 47.8985128  47.2331783
+#> 10  26.9396863  42.5369562 48.6845584 45.5573823 55.1597026  50.3105897
+#> 15  41.1581199  58.1083629 53.9313215 55.2936585 71.1989878  84.0183980
+#> 20  38.5564169  63.2154839 51.5985008 52.8430913 74.7031986 110.1303313
+#> 25  27.7977659  61.6234186 47.4051198 51.6083434 71.4428640 107.6456040
+#> 30  22.2422255  55.0624099 43.8731207 54.8247021 69.7022819  92.0023679
+#> 35  15.4282339  44.6721699 39.2611120 49.8762677 64.3662482  69.9977501
+#> 40  13.9022608  33.5791503 34.0132172 40.9028124 52.7987748  58.8675964
+#> 45  15.1324013  23.1657203 29.0358476 32.6897767 40.0602448  48.9595289
+#> 50  23.6430427  24.3027282 29.5679655 27.3167444 37.4326840  40.0787725
+#> 55  30.2152519  30.0197155 33.9584308 28.2084960 40.0225780  43.3773939
+#> 60  36.7180278  41.0735925 37.3701400 34.8975556 42.8133296  46.4391888
+#> 65  43.1354913  49.2244096 44.6705493 45.3497331 46.9441002  47.7359675
+#> 70  48.3036273  55.1229691 52.8406941 59.9426380 61.0737010  59.9554207
+#> 75  50.7362876  58.3363476 59.9609448 72.1377381 82.0927869  87.8106758
+#> 80  47.5944714  53.1999714 61.2981433 71.6760979 84.6800893 100.6440966
+#> 85  36.3664993  40.6306969 47.4201296 53.3764469 60.9562720  76.3506288
+#> 90  19.0369669  21.3787584 24.5943500 26.1127004 30.5702757  37.8712336
+#> 95  10.2343876  11.7002979 13.3059286 13.1155117 14.6560891  19.6212289
+#> 100  0.5528349   0.7229901  0.8275142  0.6994879  0.6407134   0.8698355
+#>           2010        2015          2020          2025          2030
+#> 0    63.536304  65.7260833  4.433063e+01  4.575479e+01  4.767406e+01
+#> 5    36.364455  18.8075067  1.428867e+01  1.475587e+01  1.537836e+01
+#> 10   61.269592  44.1589114  4.042136e+01  4.173772e+01  4.350211e+01
+#> 15  122.497446  99.0230306  9.315949e+01  9.618904e+01  1.002545e+02
+#> 20  116.841872 122.2617185  1.139634e+02  1.176635e+02  1.226309e+02
+#> 25   91.599610 104.4228958  9.476476e+01  9.783964e+01  1.019700e+02
+#> 30   83.556553  77.2882500  6.666796e+01  6.883477e+01  7.174004e+01
+#> 35   59.226427  55.5069429  4.397429e+01  4.539983e+01  4.731691e+01
+#> 40   38.779598  40.5871463  2.832219e+01  2.923507e+01  3.047038e+01
+#> 45   31.279570  32.1673624  1.808882e+01  1.867232e+01  1.945852e+01
+#> 50   42.163635  31.2312923  1.152124e+01  1.189181e+01  1.239102e+01
+#> 55   42.973366  36.1762133  7.334320e+00  7.567618e+00  7.883037e+00
+#> 60   46.928486  44.5379457  4.679762e+00  4.821689e+00  5.024070e+00
+#> 65   57.430282  57.9859587  2.994345e+00  3.080767e+00  3.211853e+00
+#> 70   66.529195  72.8098599  1.925118e+00  1.981168e+00  2.059526e+00
+#> 75   81.908880  86.2100493  1.251706e+00  1.283795e+00  1.332189e+00
+#> 80   96.146695  95.5267726  4.973036e-01  5.053310e-01  5.244282e-01
+#> 85   86.396271  84.8470594  1.139063e-03 -1.900901e-03 -1.787040e-03
+#> 90   46.461630  48.6423262 -5.916063e-04 -2.214464e-04  2.219107e-04
+#> 95   22.183238  25.5167066 -1.122828e-03  1.857040e-03  3.723792e-04
+#> 100   0.919266   0.8355002 -3.823113e-06 -6.037463e-04  1.228691e-05
+#>              2035          2040          2045
+#> 0    4.766587e+01  4.773854e+01  4.785075e+01
+#> 5    1.537671e+01  1.539557e+01  1.543288e+01
+#> 10   4.348980e+01  4.355698e+01  4.365529e+01
+#> 15   1.002246e+02  1.003781e+02  1.006076e+02
+#> 20   1.225980e+02  1.227812e+02  1.230660e+02
+#> 25   1.019459e+02  1.020974e+02  1.023309e+02
+#> 30   7.172074e+01  7.182816e+01  7.199256e+01
+#> 35   4.730032e+01  4.736846e+01  4.747491e+01
+#> 40   3.046052e+01  3.049816e+01  3.056507e+01
+#> 45   1.945084e+01  1.947030e+01  1.951809e+01
+#> 50   1.238686e+01  1.239838e+01  1.242642e+01
+#> 55   7.881202e+00  7.886047e+00  7.901435e+00
+#> 60   5.018025e+00  5.019264e+00  5.030757e+00
+#> 65   3.203963e+00  3.204237e+00  3.210327e+00
+#> 70   2.054404e+00  2.054788e+00  2.053114e+00
+#> 75   1.326888e+00  1.326015e+00  1.327060e+00
+#> 80   5.245498e-01  5.223082e-01  5.222588e-01
+#> 85   1.098362e-03  3.448556e-03 -3.048916e-03
+#> 90   1.682787e-04  3.117486e-03 -1.084673e-03
+#> 95   1.301420e-03  3.330889e-03 -7.856163e-04
+#> 100 -5.497503e-04 -2.754639e-04 -2.854814e-05
                                                              
+################ time even flow method  #####################
+
+# We reuse the same data from before
+# Either use the generic mig_resid with the 'time' method
+
+# For five year age groups
+mig_res <-
+ mig_resid(
+   pop_m_mat = pop_m_mat_five,
+   pop_f_mat = pop_f_mat_five,
+   sr_m_mat = sr_m_mat_five,
+   sr_f_mat = sr_f_mat_five,
+   asfr_mat = asfr_mat_five,
+   srb_vec = srb_vec_five,
+   ages = ages_five,
+   ages_asfr = ages_asfr_five,
+   # With the time method
+   method = "time"
+ )
+
                                                              #> Using time residual migration method
                                                              
+# For single ages
+mig_res <-
+ mig_resid(
+   pop_m_mat = pop_m_mat_single,
+   pop_f_mat = pop_f_mat_single,
+   sr_m_mat = sr_m_mat_single,
+   sr_f_mat = sr_f_mat_single,
+   asfr_mat = asfr_mat_single,
+   srb_vec = srb_vec_single,
+   ages = ages_single,
+   ages_asfr = ages_asfr_single,
+   # With the stock method
+   method = "stock"
+ )
+
                                                              #> Using stock residual migration method
                                                              
+# Or directly the mid_resid_time function
+# (works for both five and single year ages)
+
+mig_res <-
+  mig_resid_time(
+    pop_m_mat = pop_m_mat_five,
+    pop_f_mat = pop_f_mat_five,
+    sr_m_mat = sr_m_mat_five,
+    sr_f_mat = sr_f_mat_five,
+    asfr_mat = asfr_mat_five,
+    srb_vec = srb_vec_five,
+    ages = ages_five,
+    ages_asfr = ages_asfr_five
+  )
+
+# Net migration for males using the time even flow method
+mig_res$mig_m
+
                                                              #>             1950         1955          1960         1965         1970
+#> 0     3.57986456   7.83804281 -11.708613930  25.81847493  58.42237710
+#> 5    99.65372764  99.91545521  56.096782636  20.16120060  11.21457792
+#> 10  -40.94162774 -42.78430686 -39.882708344  18.59078430  50.05512859
+#> 15   55.48348586  51.36112198  29.686148299  13.09179503  12.81047107
+#> 20  -11.79757463 -19.64383338 -33.657655806  67.25140324  90.92287133
+#> 25  107.42501258 140.01220683  80.160934509 102.29400345  87.21131258
+#> 30  -32.40904222 -43.26549135 -47.553510864   7.44391246  18.67159066
+#> 35  106.17661163 121.93971456  66.014834078  46.46533877  29.35435108
+#> 40  -51.15631402 -93.89843855 -63.174578114 -27.88927758   5.48775806
+#> 45   88.57046150 108.82847303  50.267264262  42.37000677   3.42290940
+#> 50  -63.32934115 -93.13047934 -47.057179615 -39.73615654  17.77493878
+#> 55   58.46883834  92.26143114  37.785755257  49.95450413 -18.35527636
+#> 60  -58.32986589 -84.64085443 -33.370638099 -31.75995974  34.90416044
+#> 65   66.83013145  92.79117747  37.313414670  46.77483495 -13.45378292
+#> 70  -43.57046784 -62.56497815 -18.527162169 -34.44289291  24.15556419
+#> 75   30.01886407  45.21821705  11.378053697  22.72777192 -17.18309176
+#> 80  -12.62450034 -21.29332857  -2.130173274  -7.74682943   9.31779578
+#> 85    5.60441174   9.70724986   0.986839238   3.68891047  -4.50288791
+#> 90   -1.70123799  -3.01062248  -0.313951952  -1.20302297   1.52466156
+#> 95    0.31632108   0.55768479   0.061728916   0.23383116  -0.32111739
+#> 100  -0.02811154  -0.05411331  -0.005447638  -0.02448547   0.03644719
+#>             1975         1980          1985         1990        1995
+#> 0    28.96387615  30.01006533  61.232172593  45.58267324  66.8188773
+#> 5    19.61458145  -0.11384016  17.798839229  21.81186415 -11.4789958
+#> 10   20.20595285  26.10889922  44.850683328  56.74184507  81.8729514
+#> 15   48.95919987  36.25809415  62.845864529  59.53667644  31.0999449
+#> 20   23.01801705  61.62328797  30.756451497   7.89400063  62.2618417
+#> 25    1.62298038 -29.72231189  41.475065426  52.03339643   8.4963877
+#> 30   28.94199434  59.82581388  56.757960851   7.07303243  77.6072932
+#> 35  -21.87239151 -54.10090181  15.060691363  40.53058132  -1.0157575
+#> 40   44.33117340  55.38825229  37.079303424  -3.83519751  51.4366294
+#> 45  -45.24366939 -66.76114668 -17.615872177  23.12250876 -20.0512376
+#> 50   51.97198921  68.73233751  40.118018493 -21.15026980  38.0260225
+#> 55  -43.51296896 -49.55790353 -10.657202225  39.33641676 -22.7965318
+#> 60   51.82676172  58.29271858  35.664479258 -11.98652737  33.5229966
+#> 65  -35.33901983 -39.41621873  -1.325981088  24.25817390 -18.2014075
+#> 70   34.99220822  36.88719713  17.281657343 -18.97573621  25.6182594
+#> 75  -23.57247670 -26.76288480  -3.793379475  11.33350586 -16.9839990
+#> 80   14.15059062  15.05938901   0.694028281 -14.10486407  13.7584471
+#> 85   -7.09942975  -7.76410472  -0.358176585   7.41969081  -7.3510661
+#> 90    2.59035597   2.91884576   0.132464878  -2.78840546   2.7397113
+#> 95   -0.60437431  -0.71424802  -0.030070852   0.65864048  -0.6134260
+#> 100   0.07855023   0.09881163   0.004110768  -0.08173701   0.0688248
+#>            2000         2005          2010        2015        2020        2025
+#> 0    70.4126299  51.48145831   33.73236083  74.5237684  71.9248264  74.2787331
+#> 5     9.2223352  52.52998713   37.20561118 -42.0424248 -40.4151595 -41.7427286
+#> 10   78.0107215  29.25548089   13.40764281  66.8830873  64.5071365  66.6372408
+#> 15   61.1002538  93.82266121  197.37105948  59.9439586  58.5164254  60.3817971
+#> 20   62.1021505 122.80643192   95.77213448 168.3817044 162.9313907 168.2596094
+#> 25   20.1148330  61.93362372  -54.90300355  51.4450008  50.2569949  51.8140624
+#> 30   88.3718114  68.01002222  164.54991665 114.7947184 111.0135390 114.6788458
+#> 35   20.8205517  56.52957864  -75.48650810   0.8280661   1.1274237   1.0935709
+#> 40   71.5336598  24.86139074  129.21554946  76.5135937  73.9162539  76.3993357
+#> 45  -22.0122498  49.89176029 -111.77480201 -24.5285913 -23.5568882 -24.4557117
+#> 50   59.7886258 -11.24947223  142.64920256  58.0869502  56.1548785  58.1644701
+#> 55  -56.8405940  36.31951173  -63.55416721 -34.4574904 -33.3626521 -34.7478059
+#> 60   84.3838898 -14.13565389   85.10506293  48.0687406  46.6862333  48.5890735
+#> 65  -65.7712511  29.32707902  -14.15647172 -35.8655365 -35.0816217 -36.8046551
+#> 70   61.6773024 -23.35409488   15.10886419  39.3393827  38.7196217  40.7791816
+#> 75  -47.0754634  24.20349394   -7.59220972 -29.9265975 -29.9667998 -32.0866992
+#> 80   42.8165215   4.82076390    1.53813111  26.2495781  26.6336803  28.8995750
+#> 85  -24.3487769  -2.93691216   -0.96750858 -16.8699992 -17.6523987 -19.6928288
+#> 90    9.6710926   1.24605503    0.42211034   7.5546157   8.3531350   9.6521764
+#> 95   -2.2748115  -0.31123505   -0.11037382  -1.9541809  -2.2518481  -2.6861664
+#> 100   0.2597073   0.03683747    0.01288714   0.2285256   0.2509391   0.3042474
+#>            2030        2035        2040        2045
+#> 0    77.4459176  77.4204900  77.5188470  77.6983814
+#> 5   -43.5429804 -43.5345068 -43.5819780 -43.6922370
+#> 10   69.4908108  69.4665111  69.5605646  69.7436785
+#> 15   62.8975960  62.8819987  63.0042277  63.1255567
+#> 20  175.4243967 175.3598544 175.6067308 176.0358055
+#> 25   53.9474549  53.9155099  53.9903843  54.0801592
+#> 30  119.5914528 119.5528521 119.7715458 120.0562085
+#> 35    1.0225270   1.0072712   0.9519375   0.9121235
+#> 40   79.7379684  79.7274719  79.9051533  80.1456864
+#> 45  -25.6296697 -25.6871280 -25.7956559 -25.9708380
+#> 50   60.7878345  60.8398404  61.0409161  61.3084604
+#> 55  -36.4591281 -36.5987410 -36.8176156 -37.0954370
+#> 60   50.9769737  51.1657131  51.4807825  51.8403704
+#> 65  -38.8691838 -39.2202149 -39.6397621 -40.0819810
+#> 70   43.1850851  43.6969678  44.2773104  44.8632888
+#> 75  -34.3995882 -35.1814702 -35.9946429 -36.7846096
+#> 80   31.3094023  32.3339309  33.3781480  34.3711942
+#> 85  -21.7940080 -22.9599143 -24.1187034 -25.2468632
+#> 90   10.9779695  11.8694772  12.7578752  13.6605184
+#> 95   -3.1357372  -3.4757375  -3.8169784  -4.1788256
+#> 100   0.3588661   0.4034127   0.4472692   0.4953120
                                                              
+# Net migration for females using the time even flow method
+mig_res$mig_f
+
                                                              #>            1950         1955         1960         1965         1970
+#> 0   226.3458702 229.79272201 180.46526622 174.91745284  180.0865949
+#> 5   -21.0374290 -22.28540657 -31.50143500 -36.12133305  -54.2299461
+#> 10   99.3415777  97.83858796  74.17190806  95.74522542  135.2772394
+#> 15  -59.6809765 -65.39419993 -43.13633166 -23.17129337  -46.8001148
+#> 20  128.4354061 140.68621033 107.36582549 146.74972311  188.9421056
+#> 25  -31.8378149 -35.02737104 -45.53762715  -5.25186295  -39.1701555
+#> 30  129.3780748 141.35666443 100.68019100  87.71909797  130.7375337
+#> 35  -38.9436452 -41.71922721 -48.58209382 -33.86767281  -78.2687759
+#> 40   96.1969156  82.17618397  74.01404694  55.22193698  123.4478722
+#> 45  -54.6355206 -44.39140208 -55.84845122 -21.29545414  -87.4661601
+#> 50   78.9618389  76.33323073  81.03814346  47.61263555  138.8417828
+#> 55  -52.9859019 -42.06729052 -54.48681576   0.34775936 -100.3924019
+#> 60   70.2110143  62.17078749  86.34367902  54.96362841  150.0050752
+#> 65  -13.6705663  -2.18847224 -21.04847767  22.41355071  -61.7595508
+#> 70   68.3375982  64.28074915  81.42847971  46.82844725  114.8253826
+#> 75  -10.3770787  -6.38475405 -12.10171449  27.99107016  -14.8924516
+#> 80   43.4733361  46.94240803  59.62805297  46.69267660   69.9571248
+#> 85   -1.9687420   1.39813765  -0.03423644  12.94953562   10.0953495
+#> 90    5.9489557   5.85004073   8.75937070   8.16573505   13.2737132
+#> 95    0.0898606   0.38802533   0.10170185   1.00605627    0.9981967
+#> 100   0.0679131   0.06587289   0.11661638   0.08288009    0.1932348
+#>             1975         1980        1985        1990        1995        2000
+#> 0    140.9175869  130.9811791 152.7782663 137.4260367 140.5404398 115.6156018
+#> 5    -47.6765502  -61.7171348 -40.8585367 -39.0325437 -54.6180620 -21.2182395
+#> 10    97.6260741  108.3826166 111.6492954 124.6180639 129.9601873 118.9342766
+#> 15    -7.8818342  -47.4337354 -12.4509483 -15.6031808 -23.1929547   3.9108808
+#> 20   128.6090432  150.7026598 145.3857926 122.1649032 137.3844478 157.7556344
+#> 25   -69.0487765  -99.2216052 -25.2362233 -22.1547875 -39.9084905 -20.2548668
+#> 30   129.1419899  158.6280625 151.3826146 111.6798611 148.5439952 168.1412151
+#> 35  -100.8450048 -128.4887081 -56.8328512 -25.5163741 -37.6094921 -36.7292738
+#> 40   137.5336842  159.5245635 140.7046297  96.3026480 126.0717377 162.4750899
+#> 45  -108.6421302 -134.1399364 -89.3958210 -30.7009674 -50.4245702 -76.1732663
+#> 50   140.6039363  167.8574350 129.9678704  80.9786683 105.2077827 149.5078659
+#> 55   -89.8706856 -105.5535968 -72.2499150 -12.6897624 -49.8245648 -71.9891197
+#> 60   141.3008408  160.8936779 132.1033687  79.1982021 105.5948969 152.2723407
+#> 65   -51.4025974  -66.1123963 -26.1991832   4.4994582 -20.5531042 -58.3050739
+#> 70   119.4994680  137.3914275 112.9664642  86.9423211 112.0615845 146.1102282
+#> 75    -7.9495626  -13.1711402  20.1620016  32.1240324  34.2024895  10.3023640
+#> 80    77.2260167   86.3128305  76.0691914  81.0085017  99.5705861 145.9411473
+#> 85    15.8341860   21.3140279  33.0870227  42.8214335  45.8964765  33.1931354
+#> 90    18.7134443   24.2671827  24.5037172  27.0921147  30.0018912  43.7139432
+#> 95     2.4071631    4.3137979   5.5552771   6.4911688   5.7190028   4.0726763
+#> 100    0.3523143    0.6684041   0.8605413   0.9424119   0.7500321   0.7860324
+#>            2005        2010        2015        2020        2025        2030
+#> 0    96.8489527   81.787724 108.4964018  72.7929143  75.1377635  78.2999917
+#> 5    10.1456148    6.861041 -64.5147999 -41.3212737 -42.6378073 -44.4263380
+#> 10   72.0885342   50.154505  96.0526852  67.0220607  69.1777814  72.0812494
+#> 15   46.8365412  137.666319  48.8726280  68.9452677  71.2172872  74.2579199
+#> 20  169.9719435  164.268004 202.0142957 167.6757076 173.0972445 180.3776182
+#> 25   53.8651176    1.674869  36.2921853  51.5879497  53.2730104  55.5402431
+#> 30  152.8004253  198.451595 143.1858115 108.2401113 111.7463469 116.4513446
+#> 35    9.0499008  -63.722429 -13.1662853  -1.3229319  -1.3567847  -1.4166120
+#> 40  109.2538323  165.752322 105.1968799  70.3049707  72.5687612  75.6518060
+#> 45    8.0918478 -111.804908 -34.4986470 -25.8589758 -26.7076872 -27.8794694
+#> 50   70.6184057  181.907767  92.1907107  53.6822324  55.4537780  57.8631744
+#> 55   11.2247424  -82.427027 -24.5262744 -35.1714449 -36.3953427 -38.0578218
+#> 60   79.5974807  153.293244 100.4054475  45.7645298  47.3851820  49.5739605
+#> 65   16.0694181  -35.018895   2.4904511 -37.1421185 -38.6125234 -40.5402789
+#> 70   77.3204513  142.125455 122.5674937  39.8336501  41.5180415  43.7294736
+#> 75   64.9764201   17.669656  43.2746269 -33.6428689 -35.3367518 -37.5389842
+#> 80  128.3596517  138.115219 126.1406102  30.7027237  32.5078219  34.8617924
+#> 85   72.5951224   75.405603  75.9497393 -22.9890882 -24.7183961 -26.9616805
+#> 90   41.4996635   58.915419  60.6511414  13.2008917  14.4778586  16.1584514
+#> 95    9.2686019    7.492899  10.2366742  -4.6738266  -5.2221854  -5.9617379
+#> 100   0.5589578    0.851188   0.2535397   0.7421335   0.8358793   0.9663258
+#>           2035       2040       2045
+#> 0    78.301291  78.438885  78.632681
+#> 5   -44.439072 -44.540444 -44.654494
+#> 10   72.097267  72.236517  72.418842
+#> 15   74.190986  74.283958  74.427831
+#> 20  180.379735 180.672096 181.123362
+#> 25   55.469495  55.523010  55.613918
+#> 30  116.491593 116.695956 116.994920
+#> 35   -1.508978  -1.546569  -1.582514
+#> 40   75.729792  75.873104  76.071716
+#> 45  -27.992601 -28.100488 -28.203044
+#> 50   57.986739  58.142769  58.345844
+#> 55  -38.226599 -38.401601 -38.608806
+#> 60   49.797401  50.026525  50.314056
+#> 65  -40.876810 -41.187768 -41.540384
+#> 70   44.186892  44.619462  45.098744
+#> 75  -38.197735 -38.810642 -39.481595
+#> 80   35.743188  36.582545  37.499323
+#> 85  -28.026998 -29.079131 -30.223302
+#> 90   17.126684  18.119018  19.185104
+#> 95   -6.442425  -6.947161  -7.507029
+#> 100   1.053518   1.148182   1.254126
                                                              
+
                                                              +
                                                              + +
                                                              + + +
                                                              +
                                                              +

                                                              Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                                              +
                                                              + +
                                                              +

                                                              Site built with pkgdown 1.6.1.

                                                              +
                                                              + +
                                                              +
                                                              + + + + + + + + diff --git a/docs/reference/mig_resid_stock.html b/docs/reference/mig_resid_stock.html new file mode 100644 index 000000000..961b86e76 --- /dev/null +++ b/docs/reference/mig_resid_stock.html @@ -0,0 +1,1304 @@ + + + + + + + + +Estimate net migration using residual methods: stock change, +time even flow and cohort even flow — mig_resid_stock • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                              +
                                                              + + + + +
                                                              + +
                                                              +
                                                              +
                                                              +

                                                              Estimate net migration using residual methods: stock change, +time even flow and cohort even flow

                                                              + Source: R/mig_resid.R + +
                                                              + +
                                                              +

                                                              Estimate net migration using residual methods: stock change, +time even flow and cohort even flow

                                                              +
                                                              + + 
                                                              mig_resid_stock(
+  pop_m_mat,
+  pop_f_mat,
+  sr_m_mat,
+  sr_f_mat,
+  asfr_mat,
+  srb_vec,
+  ages,
+  ages_fertility
+)
+
+mig_resid_cohort(
+  pop_m_mat,
+  pop_f_mat,
+  sr_m_mat,
+  sr_f_mat,
+  asfr_mat,
+  srb_vec,
+  ages,
+  ages_fertility
+)
+
+mig_resid_time(
+  pop_m_mat,
+  pop_f_mat,
+  sr_m_mat,
+  sr_f_mat,
+  asfr_mat,
+  srb_vec,
+  ages,
+  ages_fertility
+)
                                                              + +

                                                              Arguments

                                                              + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                              pop_m_mat

                                                              A numeric matrix with population counts. Rows should +be ages and columns should be years. Only five year age groups are supported. +See examples.

                                                              pop_f_mat

                                                              A numeric matrix with population counts. Rows should +be ages and columns should be years. Only five year age groups are supported. +See examples.

                                                              sr_m_mat

                                                              A numeric matrix with survival rates for males. Rows +should be ages and columns should be years. ** This matrix should have +one column less than pop_m_mat and pop_f_mat. For example, +if the last year in these matrices is 2050, then the last year in +sr_m_mat should be 2045. **

                                                              sr_f_mat

                                                              A numeric matrix with survival rates for females. Rows +should be ages and columns should be years. ** This matrix should have +one column less than pop_m_mat and pop_f_mat. For example, +if the last year in these matrices is 2050, then the last year in +sr_f_mat should be 2045. **.

                                                              asfr_mat

                                                              A numeric matrix with age specific fertility rates. +Rows should be ages and columns should be years. ** This matrix should have +one column less than pop_m_mat and pop_f_mat. For example, +if the last year in these matrices is 2050, then the last year in +asfr_mat should be 2045**. This row will usually have fewer age groups +(rows) than in the population matrices or survival matrices, so the user +needs to supply the specific ages in the ages_fertility argument.

                                                              srb_vec

                                                              A numeric vector of sex ratios at birth for every year. +The years should be the same as the years in sr_m_mat, +sr_f_mat, and asfr_mat.

                                                              ages

                                                              A numeric vector of ages used in the rows in +pop_m_mat, pop_f_mat, sr_m_mat, sr_f_mat.

                                                              ages_fertility

                                                              A numeric vector of ages used in the rows in +asfr_mat.

                                                              + +

                                                              Value

                                                              + +

                                                              A list with two matrices. One is for males (called mig_m) and the +other for females (called mig_f). Both matrices contain net migration +estimates by age/period using one of the three methods.

                                                              +

                                                              Details

                                                              + + +
                                                                +

                                                              1. The stock method (mig_resid_stock) is the difference in stocks that +survive between t and t+5, and the first age group is based on the difference +with the surviving births by sex. It provides net migrants by lexis cohort +parallelograms, and basically such info gets used as end-period migration +since the migrants don't get exposed to mortality within the period.

                                                                2. +

                                                              2. The time even flow (mig_resid_time) method uses the result from +the first option, but splits it back into lexis period squares and assumes +that half of the net migrants get exposed to the mortality risk during this +period. Such info can get used as evenly distributed migration by period, +but the assumptions lead to zig-zag age patterns that are highly implausible.

                                                                3. +

                                                              3. The cohort even flow (mig_resid_cohort) method provides the most +meaningful pattern of net migration by age consistent by cohort and assumes +an evenly distribution within the 5-year period, and half of the migrants +get exposed both fertility and mortality within this period.

                                                                4. +
                                                              + + +

                                                              Examples

                                                              + 
                                                              
+################ Stock change method #####################
+
+# Vector of population for males
+pop_m <-
+  c(835, 671, 570.999, 544, 552, 550, 513, 487.998, 432.002,
+    378.001, 338.001, 295.999, 263.001, 220.999, 156, 92.001, 42.204,
+    18.001, 4.331, 0.639, 0.07, 989.592, 880.029, 697.435, 575.575,
+    561.146, 595.087, 582.08, 544.567, 507.247, 440.909, 373.935,
+    316.617, 265.548, 235.035, 182.951, 110.75, 57.555, 18.737, 5.459,
+    0.807, 0.065, 1133.424, 1037.502, 905.664, 698.771, 586.798,
+    616.9, 638.007, 615.889, 550.076, 504.215, 430.131, 353.696,
+    286.403, 238.634, 194.496, 130.76, 69.399, 26.238, 5.809, 1.011,
+    0.083, 1149.519, 1149.942, 1042.707, 897.087, 691.317, 605.519,
+    628.132, 641.442, 608.262, 532.131, 486.313, 401.381, 321.04,
+    251.388, 197.567, 138.127, 81.716, 32.255, 8.386, 1.152, 0.112,
+    959.81, 1167.439, 1166.318, 1054.263, 929.596, 770.233, 655.408,
+    649.175, 641.542, 602.148, 513.273, 463.968, 371.397, 286.345,
+    203.426, 141.486, 87.742, 38.904, 10.533, 1.647, 0.131, 904.577,
+    990.842, 1195.275, 1192.299, 1096.101, 1010.808, 817.203, 673.368,
+    657.377, 631.756, 591.218, 483.434, 432.18, 330.595, 237.691,
+    147.718, 86.849, 42.396, 13.172, 2.217, 0.2, 914.965, 926.002,
+    1008.784, 1224.923, 1217.653, 1099.911, 1019.023, 813.755, 676.39,
+    643.237, 614.691, 562.266, 449.355, 383.548, 273.038, 176.289,
+    92.764, 43.563, 15.48, 3.067, 0.316, 943.289, 927.824, 937.572,
+    1036.631, 1265.185, 1225.442, 1108.122, 1014.48, 806.487, 659.135,
+    627.186, 594.356, 527.096, 404.537, 320.095, 205.116, 112.331,
+    47.821, 16.391, 3.791, 0.466, 982.718, 980.995, 958.024, 988.65,
+    1076.907, 1293.734, 1267.032, 1135.973, 1031.081, 804.56, 654.945,
+    615.926, 567.992, 487.466, 348.485, 248.7, 133.025, 58.273, 17.722,
+    3.888, 0.553, 1012.228, 1014.939, 1019.3, 1013.749, 1016.987,
+    1100.959, 1315.154, 1281.21, 1143.215, 1026.976, 788.317, 641.579,
+    592.945, 521.839, 418.599, 270.316, 163.215, 70.003, 21.874,
+    4.186, 0.549, 914.063, 1038.649, 1049.297, 1073.621, 1055.672,
+    1047.487, 1138.313, 1344.753, 1295.431, 1144.702, 1016.992, 771.165,
+    615.065, 550.259, 457.748, 334.624, 185.735, 87.221, 26.085,
+    4.898, 0.538, 879.05, 952.912, 1081.565, 1116.92, 1130.826, 1092.34,
+    1097.179, 1186.645, 1381.02, 1305.446, 1143.75, 989.552, 751.696,
+    577.344, 489.506, 377.661, 245.003, 105.627, 34.645, 6.138, 0.619,
+    967.471, 930.238, 993.227, 1141.416, 1220.943, 1218.465, 1152.732,
+    1154.005, 1219.105, 1404.229, 1303.056, 1126.891, 960.563, 712.717,
+    522.094, 415.611, 295.598, 149.226, 44.788, 8.64, 0.793, 996.168,
+    1002.283, 955.049, 1097.332, 1284.199, 1236.929, 1269.189, 1192.192,
+    1174.32, 1216.076, 1400.008, 1311.348, 1099.315, 941.078, 652.11,
+    452.242, 319.415, 186.665, 65.652, 11.458, 1.11, 1020.925, 1011.765,
+    1014.23, 1017.281, 1208.233, 1389.541, 1315.956, 1321.892, 1224.232,
+    1189.671, 1215.983, 1380.774, 1275.239, 1047.397, 867.426, 568.286,
+    355.943, 205.33, 83.6, 17, 1.474, 1028.191, 1035.869, 1023.388,
+    1074.814, 1125.935, 1311.078, 1465.662, 1367.022, 1352.787, 1239.604,
+    1191.356, 1203.245, 1346.713, 1220.838, 973.366, 765.273, 455.163,
+    235.912, 97.148, 22.528, 2.061, 1035.917, 1043.755, 1047.947,
+    1086.086, 1187.203, 1232.794, 1390.453, 1518.531, 1399.596, 1369.011,
+    1242.733, 1181.749, 1178.339, 1295.414, 1142.767, 869.016, 624.221,
+    310.175, 115.614, 27.037, 2.784, 1045.967, 1052.236, 1056.4,
+    1113.398, 1203.477, 1298.811, 1316.2, 1446.37, 1552.621, 1417.392,
+    1372.669, 1234.502, 1160.392, 1138.346, 1219.39, 1029.28, 718.949,
+    434.576, 156.257, 33.016, 3.417, 1068.682, 1062.337, 1064.906,
+    1121.9, 1230.868, 1315.242, 1382.275, 1372.643, 1481.225, 1570.215,
+    1421.698, 1364.113, 1214.291, 1124.645, 1077.332, 1107.059, 862.313,
+    510.375, 224.598, 45.72, 4.224, 1102.822, 1085.121, 1075.057,
+    1130.572, 1239.683, 1342.955, 1399.057, 1438.921, 1408.243, 1499.967,
+    1574.252, 1414.059, 1343.384, 1179.959, 1069.144, 985.391, 938.14,
+    623.089, 270.065, 67.236, 5.855, 1140.8, 1119.328, 1097.897,
+    1140.924, 1248.743, 1352.213, 1427.121, 1456.061, 1474.768, 1428.008,
+    1505.633, 1566.169, 1394.527, 1308.152, 1126.043, 984.266, 844,
+    689.026, 337.068, 82.618, 8.662)
+
+# Vector of population for females
+pop_f <-
+  c(801, 645, 554.001, 534, 557.999, 564, 521.001, 478.001,
+    410.999, 352.999, 318, 276, 239, 196.998, 147, 92, 49.354, 23.001,
+    6.5, 1.164, 0.135, 948.057, 844.618, 670.281, 562.978, 556.695,
+    589.153, 595.677, 545.622, 488.336, 410.687, 346.698, 303.994,
+    257.339, 222.218, 177.703, 111.516, 66.076, 25.155, 8.267, 1.423,
+    0.145, 1082.85, 993.185, 866.81, 675.391, 590.032, 594.559, 626.777,
+    624.823, 547.167, 485.337, 406.556, 337.02, 285.388, 243.185,
+    202.252, 135.464, 80.488, 34.773, 9.379, 1.88, 0.182, 1091.999,
+    1101.593, 998.637, 870.126, 696.038, 607.414, 608.372, 634.123,
+    618.287, 534.182, 476.099, 390.902, 323.659, 270.819, 222.629,
+    158.211, 99.985, 43.526, 13.425, 2.209, 0.243, 916.285, 1113.846,
+    1116.448, 1022.769, 918.839, 752.674, 635.897, 619.411, 627.163,
+    613.115, 524.316, 470.068, 384.21, 315.25, 247.486, 184.146,
+    119.509, 56.879, 17.577, 3.347, 0.307, 858.616, 944.546, 1141.242,
+    1148.666, 1080.544, 977.493, 784.947, 647.137, 627.282, 623.897,
+    615.885, 510.514, 460.492, 370.614, 286.566, 207.64, 136.385,
+    70.415, 24.027, 4.599, 0.483, 868.926, 877.749, 959.791, 1175.057,
+    1195.136, 1093.776, 991.924, 782.722, 651.731, 622.015, 618.687,
+    605.778, 500.641, 442.912, 341.607, 245.476, 158.844, 84.752,
+    32.378, 7.171, 0.792, 895.173, 880.805, 893.824, 982.635, 1215.112,
+    1205.436, 1108.77, 989.171, 783.703, 645.936, 620.163, 616.473,
+    594.188, 483.99, 407.959, 294.664, 188.001, 102.344, 41.293,
+    10.454, 1.36, 936.591, 930.873, 909.761, 937.09, 1040.314, 1261.087,
+    1253.31, 1138.156, 1013.326, 789.43, 647.395, 618.625, 607.737,
+    578.414, 450.459, 356.927, 227.408, 122.534, 50.048, 13.221,
+    1.968, 962.734, 966.476, 967.107, 957.99, 982.293, 1078.452,
+    1289.82, 1276.239, 1152.444, 1021.414, 790.697, 651.846, 612.561,
+    584.997, 533.268, 389.96, 277.225, 150.795, 61.026, 16.175, 2.495,
+    872.149, 989.229, 998.47, 1014.575, 1007.4, 1019.803, 1118.597,
+    1322.793, 1295.408, 1160.933, 1017.656, 782.288, 640.445, 591.004,
+    544.271, 468.132, 312.504, 184.648, 74.05, 18.888, 2.818, 831.614,
+    907.289, 1032.916, 1054.491, 1087.81, 1064.803, 1079.765, 1163.223,
+    1358.369, 1304.687, 1162.716, 1010.927, 777.628, 621.991, 554.866,
+    485.032, 385.873, 214.234, 93.771, 23.583, 3.281, 918.838, 876.318,
+    944.356, 1087.72, 1156.023, 1189.243, 1155.079, 1142.97, 1200.737,
+    1385.864, 1307.092, 1157.85, 1000.01, 756.228, 585.003, 501.751,
+    414.881, 275.154, 114.55, 31.799, 4.292, 950.252, 955.845, 901.601,
+    1034.682, 1232.517, 1230.147, 1278.363, 1207.768, 1177.793, 1204.356,
+    1388.633, 1309.444, 1137.271, 975.544, 718.972, 536.404, 423.543,
+    303.137, 151.219, 39.364, 5.675, 972.207, 966.024, 968.771, 971.038,
+    1155.236, 1343.606, 1310.409, 1330.404, 1239.077, 1193.687, 1207.497,
+    1378.179, 1287.326, 1102.782, 925.427, 657.537, 458.654, 313.372,
+    169.081, 52.593, 7.072, 976.405, 987.332, 978.581, 1036.217,
+    1088.349, 1263.153, 1421.22, 1360.854, 1360.488, 1254.47, 1197.429,
+    1200.365, 1355.979, 1250.443, 1049.613, 850.295, 565.065, 343.486,
+    179.947, 59.846, 9.474, 983.61, 992.108, 1000.337, 1048.292,
+    1157.406, 1200.03, 1343.652, 1473.399, 1392.3, 1376.507, 1258.974,
+    1191.956, 1183.853, 1320.389, 1194.661, 970.4, 737.82, 429.619,
+    201.231, 64.936, 11.111, 993.232, 1000.063, 1005.704, 1073.068,
+    1174.732, 1273.909, 1284.275, 1398.562, 1506.421, 1409.749, 1381.642,
+    1254.544, 1177.896, 1156.569, 1266.895, 1112.005, 851.243, 570.482,
+    257.474, 74.251, 12.326, 1014.748, 1009.727, 1013.685, 1078.453,
+    1199.539, 1291.299, 1358.178, 1339.471, 1432.104, 1523.907, 1415.474,
+    1377.117, 1241.194, 1153.335, 1113.937, 1185.875, 984.267, 667.614,
+    348.639, 96.877, 14.172, 1047.114, 1031.299, 1023.389, 1086.588,
+    1205.189, 1316.366, 1375.813, 1413.531, 1373.503, 1450.422, 1529.735,
+    1411.833, 1363.681, 1217.645, 1114.502, 1048.481, 1058.718, 782.58,
+    415.883, 133.756, 18.362, 1083.119, 1063.736, 1045.012, 1096.489,
+    1213.687, 1322.39, 1401.18, 1431.462, 1447.757, 1392.552, 1457.502,
+    1526.287, 1399.672, 1340.065, 1180.192, 1054.342, 943.985, 853.051,
+    496.821, 162.711, 25.414)
+
+# Vector of age-specific fertility rates
+asfr <- c(50.369, 202.131, 206.141, 149.211, 87.253, 31.052,
+          2.843, 57.919, 226.709, 222.516, 148.992, 87.888, 29.736, 2.64,
+          54.096, 223.587, 211.46, 140.311, 76.881, 26.533, 2.132, 45.049,
+          159.679, 156.131, 93.96, 50.059, 15.713, 1.409, 37.188, 119.39,
+          132.748, 70.029, 28.02, 7.311, 0.514, 30.209, 101.658, 125.692,
+          65.483, 19.804, 3.711, 0.243, 24.9, 88.815, 121.231, 68.621,
+          20.031, 3.039, 0.163, 23.238, 78.247, 118.743, 75.403, 24.014,
+          3.426, 0.129, 25.141, 75.764, 118.592, 85.555, 29.309, 4.303,
+          0.136, 20.117, 64.41, 104.081, 85.589, 32.737, 5.247, 0.219,
+          14.645, 53.484, 98.176, 92.658, 37.567, 6.397, 0.273, 13.677,
+          51.37, 100.418, 104.868, 48.196, 8.278, 0.393, 11.494, 43.287,
+          93.809, 106.904, 53.5, 10.662, 0.544, 8.387, 37.053, 86.307,
+          106.038, 55.169, 11.345, 0.701, 6.625, 31.576, 80.064, 106.128,
+          58.423, 13.087, 0.917, 5.468, 27.869, 76.196, 107.843, 62.296,
+          15.036, 1.172, 4.686, 25.34, 73.943, 110.575, 66.487, 17.107,
+          1.462, 4.134, 23.539, 72.551, 113.398, 70.423, 19.099, 1.756,
+          3.732, 22.206, 71.53, 115.597, 73.588, 20.803, 2.024, 3.467,
+          21.39, 71.244, 117.758, 76.268, 22.224, 2.249)
+
+# Vector of survival rates for males
+sr_m <- c(0.95557549, 0.9921273, 0.99594764, 0.99510483, 0.99178483,
+          0.99134461, 0.99100899, 0.98929784, 0.98473229, 0.97588706,
+          0.96048519, 0.93812765, 0.90615821, 0.8622277, 0.8047363,
+          0.71333856, 0.596832, 0.44396816, 0.30330032, 0.18642771,
+          0.0911662462413327, 0.96275471, 0.99399428, 0.9968488, 0.99563281,
+          0.99229006, 0.99196446, 0.99180061, 0.99013625, 0.98594365,
+          0.97719516, 0.96239426, 0.93950426, 0.90620399, 0.86117682,
+          0.80225284, 0.71307413, 0.60022645, 0.4558758, 0.31005161,
+          0.18518342, 0.0956313878791117, 0.96951141, 0.99496609, 0.99727649,
+          0.99607245, 0.99233725, 0.99205108, 0.99228027, 0.99056435,
+          0.98605767, 0.97783685, 0.96314897, 0.9406932, 0.90642888,
+          0.86286999, 0.80387894, 0.71498269, 0.6066814, 0.46479967,
+          0.31958557, 0.19836001, 0.101989015830425, 0.97545992, 0.99563858,
+          0.99741385, 0.99592654, 0.99152023, 0.99192115, 0.9924163,
+          0.990644, 0.98617665, 0.97752991, 0.96353815, 0.94114166,
+          0.90833701, 0.86170391, 0.80301014, 0.72151551, 0.6128495,
+          0.47608317, 0.32653048, 0.19655555, 0.103730263806538, 0.98060776,
+          0.99617149, 0.99761403, 0.99542383, 0.99054495, 0.99194397,
+          0.99266261, 0.99089428, 0.98611279, 0.97789594, 0.96434779,
+          0.94341044, 0.91191009, 0.86748795, 0.80686197, 0.72532159,
+          0.61846149, 0.48319275, 0.33857582, 0.2104904, 0.112385162790671,
+          0.98550309, 0.99688352, 0.99803301, 0.9959062,
+          0.99126003, 0.99231634, 0.99301369, 0.99160392, 0.98761021,
+          0.97986329, 0.96646197, 0.94623899, 0.91718051, 0.87584219,
+          0.81662218, 0.73700918, 0.6303803, 0.50160307, 0.3651526,
+          0.23288489, 0.130850768617506, 0.98931819, 0.99775357, 0.9984583,
+          0.99673481, 0.99301824, 0.99346278, 0.99369545, 0.99294003,
+          0.98994327, 0.98356936, 0.97206458, 0.95389895, 0.92644026,
+          0.88661213, 0.82907732, 0.74960973, 0.64331764, 0.51551648,
+          0.37629084, 0.24487989, 0.137677217644374, 0.99112504, 0.99814201,
+          0.99879926, 0.99711429, 0.99389061, 0.99409107, 0.99396033,
+          0.99290953, 0.99073493, 0.98586868, 0.97625645, 0.95984456,
+          0.93401387, 0.89576747, 0.84146086, 0.76230416, 0.6528808,
+          0.51875327, 0.37059318, 0.23716232, 0.129879589178461, 0.99268153,
+          0.99853182, 0.99902021, 0.99755244, 0.99471893, 0.99453053,
+          0.99383731, 0.99242559, 0.99039498, 0.98664094, 0.97888902,
+          0.96514568, 0.94232807, 0.90695918, 0.85686765, 0.78068285,
+          0.6693777, 0.52625072, 0.37539746, 0.23620331, 0.123653858706926,
+          0.99378702, 0.99880557, 0.99916867, 0.99795758, 0.99537754,
+          0.99534488, 0.99484455, 0.99357678, 0.99141921, 0.9878473,
+          0.98134482, 0.96932265, 0.94941573, 0.91742454, 0.86776992,
+          0.79533967, 0.68311452, 0.5343893, 0.37262307, 0.2239178,
+          0.113720633638293, 0.99399248, 0.99897976, 0.99932852, 0.99822431,
+          0.99601855, 0.99591387, 0.99569633, 0.99470259, 0.99268715,
+          0.98892298, 0.98249955, 0.97238094, 0.9552462, 0.92844911,
+          0.88653064, 0.82114141, 0.71875477, 0.56869525, 0.39721252,
+          0.23529357, 0.113792166251756, 0.99425766, 0.99909623, 0.99940998,
+          0.99846006, 0.99631257, 0.99603178, 0.99592291, 0.99518752,
+          0.99322132, 0.98983274, 0.98368064, 0.9741733, 0.9602258,
+          0.93745239, 0.9016288, 0.8444638, 0.75222073, 0.60907965,
+          0.4240168, 0.24939352, 0.117336219766853, 0.99471736, 0.99933256,
+          0.99947765, 0.99871556, 0.99694493, 0.99649396, 0.99655801,
+          0.99589338, 0.99413263, 0.99084735, 0.98542046, 0.97725847,
+          0.96497949, 0.9452114, 0.9134356, 0.8610204, 0.7737601, 0.63147622,
+          0.43996206, 0.25585397, 0.117677375365884, 0.99489165, 0.99937715,
+          0.99951763, 0.99880472, 0.99710652, 0.99665932, 0.99675852,
+          0.99614922, 0.99444005, 0.99122406, 0.98598437, 0.97827319,
+          0.96683874, 0.94835124, 0.91833471, 0.86821514, 0.78398104,
+          0.64282533, 0.44786339, 0.25892654, 0.117310656081295, 0.99547488,
+          0.99923281, 0.99957341, 0.99911343, 0.99802214, 0.99709297,
+          0.99680896, 0.99635099, 0.99498899, 0.99222347, 0.9875931,
+          0.98060053, 0.97014661, 0.9536392, 0.92634894, 0.87987019,
+          0.79856151, 0.66278044, 0.47312791, 0.2694788, 0.111570323438865,
+          0.99610316, 0.99933917, 0.9996362, 0.99923203, 0.99825186,
+          0.99737383, 0.99710434, 0.99670571, 0.99548655, 0.99299216,
+          0.9888173, 0.98253469, 0.97316181, 0.95828527, 0.93341992,
+          0.89062075, 0.81386423, 0.68145805, 0.49006978, 0.27830681,
+          0.113218864970809, 0.99653042, 0.99941146, 0.99967816, 0.99931356,
+          0.99841616, 0.99758578, 0.9973301, 0.99697294, 0.99585907,
+          0.99356834, 0.9897352, 0.98398011, 0.97540572, 0.96174959,
+          0.93872789, 0.89876001, 0.8256409, 0.69618678, 0.50377247,
+          0.28557404, 0.114591438080939, 0.99688677, 0.99947174, 0.99971275,
+          0.99938206, 0.99855794, 0.99777511, 0.99753337, 0.99721139,
+          0.99619015, 0.99408084, 0.99055187, 0.98526355, 0.97739317,
+          0.9648224, 0.94346085, 0.90607063, 0.83636949, 0.70989474,
+          0.51681708, 0.29260349, 0.115932510195963, 0.99718332, 0.99952193,
+          0.9997412, 0.99943948, 0.99868001, 0.99794372, 0.99771578,
+          0.99742349, 0.99648352, 0.99453532, 0.99127626, 0.98639967,
+          0.97914803, 0.96753912, 0.94766613, 0.91261037, 0.84609618,
+          0.7225789, 0.52915469, 0.29935668, 0.117233382382913, 0.99743526,
+          0.99956461, 0.99976514, 0.9994886, 0.99878699, 0.99809597,
+          0.99788161, 0.99761483, 0.99674727, 0.99494418, 0.99192807,
+          0.98742013, 0.98072071, 0.96997658, 0.95145635, 0.91854221,
+          0.85503156, 0.73446104, 0.54096047, 0.30591861,
+          0.118509238191645)
+
+# Vector of survival rates for females
+sr_f <- c(0.854489854276296, 0.935421167801612, 0.97813792986728,
+          0.982021189677661, 0.976828336081795, 0.97244561985297,
+          0.968812772150047, 0.96483427499772, 0.96010802339363,
+          0.954056165687121, 0.943306039954761, 0.92448836548943,
+          0.890690237758345, 0.835639114030282, 0.754796751406155,
+          0.644175707707241, 0.510754359186887, 0.367690608641792,
+          0.24038748937665, 0.145450728453873, 0.0826258994519641,
+          0.872081445760557, 0.944846444000478, 0.981301676540409,
+          0.98454923599414, 0.980025670920247, 0.976133157582757,
+          0.972813894527646, 0.968976434023376, 0.964396307993652,
+          0.958650557701456, 0.948256461919103, 0.930336619590153,
+          0.898721683064412, 0.846943744756808, 0.7693050373115,
+          0.660761281137989, 0.526356065457763, 0.380513624627523,
+          0.249631099810745, 0.150641910916079, 0.0845984581684562,
+          0.886848633625797, 0.952485090106336, 0.983750072193038,
+          0.986500893000163, 0.982460467395807, 0.978978117640805,
+          0.975938233552867, 0.972378413253193, 0.967941794833695,
+          0.962223931611845, 0.952268831689409, 0.935291261627555,
+          0.905449822961756, 0.856292233575997, 0.781888442354377,
+          0.676068647825169, 0.542224475987347, 0.394762631381521,
+          0.260252774164775, 0.156857215747202, 0.0874135544568921,
+          0.900784558263659, 0.9596839642243, 0.986196123803518,
+          0.988385232322206, 0.984769248387878, 0.981657532920333,
+          0.978917213857464, 0.975642526638743, 0.971423457946261,
+          0.965729507599766, 0.956020853088256, 0.939966236835617,
+          0.912171280137383, 0.865872642393594, 0.794508147678775,
+          0.691547047753295, 0.558414706244368, 0.408689574652693,
+          0.269878505194327, 0.1624396495176, 0.0898671492416105,
+          0.912633835108388, 0.965550681132028, 0.987920059015778,
+          0.989848240747598, 0.986695608763104, 0.983981235542121,
+          0.981570190639542, 0.97861812228183, 0.974635289736998,
+          0.9691431860219, 0.959749667932423, 0.944374872196883,
+          0.918112298204692, 0.874309263183862, 0.80582736469469,
+          0.705435057343639, 0.573891155573389, 0.423123546270893,
+          0.280818298136084, 0.169320472983824, 0.0930347806338448,
+          0.922791200429312, 0.970414095250172, 0.989432301168788,
+          0.990708732285749, 0.987862933459543, 0.98554556950358,
+          0.983439950663867, 0.980726138018158, 0.976956585172812,
+          0.971679032024458, 0.962795297767379, 0.948303265698793,
+          0.923561621562827, 0.882274284142424, 0.817184362828982,
+          0.720298723984997, 0.590336398050365, 0.439061608307152,
+          0.293893306863672, 0.178752339638971, 0.0979770657163587,
+          0.933750711567667, 0.975650023350237, 0.991430129981753,
+          0.99247477931302, 0.989942622594004, 0.987846767986695,
+          0.98591292962169, 0.983362542545618, 0.979767042495056,
+          0.974726611965329, 0.96628359885727, 0.95251185213316,
+          0.929005505885616, 0.889790085932051, 0.827762556372604,
+          0.734212960407364, 0.606322257595734, 0.453895037584414,
+          0.305625301261282, 0.186403154530675, 0.101364444633525,
+          0.942022185331379, 0.979147123918558, 0.992515619501369,
+          0.9933310350342, 0.990999245807151, 0.98905130373017,
+          0.987257739978207, 0.984897031588263, 0.981492787306857,
+          0.9766673141557, 0.968686240948038, 0.955634366723833,
+          0.9333801029294, 0.895907358522987, 0.836661271636903,
+          0.746801163214231, 0.621415950298903, 0.468910608066693,
+          0.317915153427838, 0.195039481469627, 0.105384282613558,
+          0.94718229582512, 0.980790142908247, 0.992803300931434,
+          0.993541163348349, 0.991307476656705, 0.989225443921528,
+          0.987176469101693, 0.98460623719428, 0.981246509709472,
+          0.976718447241337, 0.969160984177711, 0.956833654346736,
+          0.935783935128507, 0.900223655022325, 0.843512027545961,
+          0.75698552882669, 0.634658767009268, 0.482935300037292,
+          0.329848456620383, 0.20331169504299, 0.10978246168398,
+          0.953756599857967, 0.984059901645376, 0.993955214747401,
+          0.994268297381336, 0.991878910452198, 0.989316711233698,
+          0.98661050317541, 0.983509725457937, 0.980068840413112,
+          0.975865841537748, 0.96873162752321, 0.957191306973029,
+          0.937336013374256, 0.903694171906013, 0.84952642553648,
+          0.76536853834578, 0.645403389194791, 0.494479119379901,
+          0.339863899010747, 0.210826549541289, 0.113471459046826,
+          0.960713535654886, 0.987033073900882, 0.994716009106623,
+          0.994894234746082, 0.992525302465177, 0.9895538842758,
+          0.986152805334012, 0.982472321949589, 0.979073662581534,
+          0.975395328209274, 0.968932259296626, 0.958359612326248,
+          0.939967835580358, 0.908680780255339, 0.857541392392623,
+          0.777353828146545, 0.661131513296524, 0.512711981039796,
+          0.357346432815676, 0.224662101978509, 0.121638559000661,
+          0.967668081909087, 0.990008068984907, 0.995560889886471,
+          0.995641991716643, 0.993624470509278, 0.991059373658537,
+          0.988040353436121, 0.984673329703723, 0.981470430737054,
+          0.977934939728761, 0.971766487949973, 0.961780792273926,
+          0.944588362056491, 0.915303175185752, 0.867175365253056,
+          0.790635307536064, 0.677656963580601, 0.530729783448463,
+          0.374404597176352, 0.237203123872316, 0.127388492872508,
+          0.973550993968318, 0.99237559671888, 0.996334437474569,
+          0.996302569319733, 0.994665042785698, 0.992854350176394,
+          0.990804628097226, 0.988299827070781, 0.985427217612243,
+          0.98180549401774, 0.975726794918091, 0.966103852460658,
+          0.949838547130851, 0.92234926803937, 0.877071883537886,
+          0.804140728455995, 0.694271895287286, 0.548487335263838,
+          0.389191763586342, 0.24733085854494, 0.131770634512774,
+          0.977644495019607, 0.993809059507753, 0.996921848401461,
+          0.996813848508293, 0.995383460235151, 0.993952685083984,
+          0.992408034278954, 0.990393195059418, 0.987756840918104,
+          0.984187864274535, 0.978346485320431, 0.969132680765175,
+          0.953701455005333, 0.92763044136048, 0.884677298385547,
+          0.814988789377917, 0.708878868603095, 0.565946568832964,
+          0.40677396957209, 0.26150417331884, 0.138468877454496, 0.99589669,
+          0.99939439, 0.99968965, 0.99946356, 0.99900599, 0.99861604,
+          0.99832041, 0.99779148, 0.99668581, 0.9946399, 0.9913872,
+          0.98672374, 0.97979077, 0.96855904, 0.94971941, 0.9172498,
+          0.85796829, 0.74889563, 0.57422822, 0.35395265, 0.158786622360056,
+          0.9963443, 0.99946056, 0.99972569, 0.99951979, 0.99909662,
+          0.99871978, 0.99844108, 0.9979583, 0.99694158, 0.99505178,
+          0.99204825, 0.98775485, 0.98138426, 0.97102475, 0.95351064,
+          0.92310744, 0.86661424, 0.76030246, 0.58585433, 0.36085561,
+          0.160294341407215, 0.99679414, 0.99952694, 0.99976136, 0.99957681,
+          0.99919131, 0.99883301, 0.99857404, 0.99814009, 0.99721897,
+          0.99549888, 0.99276597, 0.98887133, 0.98310375, 0.97368874,
+          0.95762803, 0.92951136, 0.87619312, 0.77320377,
+          0.59930763, 0.36898278, 0.162083812814283, 0.9971375, 0.99957757,
+          0.99978821, 0.99962071, 0.99926645, 0.99892672, 0.99868509,
+          0.9982903, 0.99744714, 0.99586696, 0.99335694, 0.98978824,
+          0.98451115, 0.97587178, 0.96101941, 0.93482131, 0.88424374,
+          0.78427805, 0.61113191, 0.37625625, 0.163698785477625, 0.99744017,
+          0.99962217, 0.9998116, 0.99965971, 0.99933486, 0.99901498,
+          0.99879044, 0.99843159, 0.99766096, 0.99621214, 0.99391125,
+          0.99064648, 0.985825, 0.97791169, 0.9642029, 0.93983665,
+          0.89194494, 0.79508552, 0.62293587, 0.38364594, 0.165352911452495,
+          0.99771115, 0.99966211, 0.99983233, 0.99969485, 0.99939794,
+          0.99909887, 0.9988912, 0.99856572, 0.9978633, 0.996539, 0.9944362,
+          0.99145779, 0.98706409, 0.97983722, 0.96722115, 0.94462117,
+          0.89938678, 0.80574358, 0.63485152, 0.39124343, 0.167067858821075)
+
+
+all_years <- c("1950", "1955", "1960", "1965", "1970", "1975",
+               "1980", "1985", "1990", "1995", "2000", "2005",
+               "2010", "2015", "2020", "2025", "2030", "2035",
+               "2040", "2045", "2050")
+
+# Population for males as matrix
+pop_m_mat <- matrix(pop_m, nrow = 21, ncol = 21)
+colnames(pop_m_mat) <- all_years
+
+# Population for females as matrix
+pop_f_mat <- matrix(pop_f, nrow = 21, ncol = 21)
+colnames(pop_f_mat) <- all_years
+
+# Age-specific-fertility-rate for as matrix
+asfr_mat <- matrix(asfr, nrow = 7, ncol = 20)
+colnames(asfr_mat) <- all_years[-length(all_years)]
+
+# Sex ratio at birth as vector
+srb_vec <- c(1.058, 1.057, 1.055, 1.055, 1.06, 1.056, 1.056, 1.052, 1.056,
+             1.054, 1.054, 1.053, 1.054, 1.053, 1.056, 1.056, 1.056, 1.056,
+             1.056, 1.056)
+
+names(srb_vec) <- all_years[-length(all_years)]
+
+# Survival ratio for males as matrix
+sr_m_mat <- matrix(sr_m, nrow = 21, ncol = 20)
+colnames(sr_m_mat) <- all_years[-length(all_years)]
+
+# Survival ratio for females as matrix
+sr_f_mat <- matrix(sr_f, nrow = 21, ncol = 20)
+colnames(sr_f_mat) <- all_years[-length(all_years)]
+
+# Age/year sequence of all the data from above
+interval <- 5
+ages <- seq(0, 100, by = interval)
+years <- seq(1950, 2050, by = interval)
+ages_fertility <- seq(15, 45, by = interval)
+
+mig_res <-
+  mig_resid_stock(
+    pop_m_mat = pop_m_mat,
+    pop_f_mat = pop_f_mat,
+    sr_m_mat = sr_m_mat,
+    sr_f_mat = sr_f_mat,
+    asfr_mat = asfr_mat,
+    srb_vec = srb_vec,
+    ages = ages,
+    ages_fertility = ages_fertility
+  )
+
+# Net migration for males using stock change method
+mig_res$mig_m
+
                                                              #>                1955          1960          1965          1970          1975
+#>  [1,]  1.7899322803  3.919021e+00 -5.8543069648 12.9092374640  2.921119e+01
+#>  [2,] 51.6027045000  5.385321e+01 22.2235544078 22.9335351570  3.470664e+01
+#>  [3,] 29.1541335600  2.840815e+01  8.0306470720 19.3499225033  3.062147e+01
+#>  [4,]  7.3711371748  4.381831e+00 -5.0199593568 15.8034252562  3.131827e+01
+#>  [5,] 21.6150524800  1.566065e+01 -2.0994925197 40.1160914300  5.180611e+01
+#>  [6,] 47.8647752800  6.026311e+01 23.3854103582 84.5010463454  8.870085e+01
+#>  [7,] 37.0250555000  4.779935e+01 15.9943014370 54.4810744403  5.262150e+01
+#>  [8,] 37.0572080800  3.955049e+01  9.4550107496 26.9198029920  2.392796e+01
+#>  [9,] 26.6996119446  1.316362e+01  0.9599276814  8.9668772707  1.721723e+01
+#> [10,] 19.3238383059  8.535687e+00 -5.7535831006  7.5537018836  4.394683e+00
+#> [11,] 10.8706376948  5.802709e+00  0.6788420915  0.5444807024  1.053791e+01
+#> [12,] -0.4710838276  2.382475e+00 -3.2403068092  6.2785759004 -7.931068e-01
+#> [13,] -2.6739240018 -5.165887e-01  0.4397308595  6.8077825892  9.082899e+00
+#> [14,]  8.2682526723  9.950218e+00  4.2594462540  9.7035767336  8.412578e+00
+#> [15,]  5.1050824363  5.938504e+00  5.7341530320  1.5588869257  6.650109e+00
+#> [16,] -0.5308153600  3.023748e-01 -0.9342732742 -1.0616547642  1.687302e-01
+#> [17,]  2.6458591680  2.923921e+00  2.3863401360  3.0909371135 -6.546424e-01
+#> [18,] -0.0002322246  6.833100e-05 -0.0016322983  0.0003876803 -2.982705e-04
+#> [19,] -0.0007090603 -4.370166e-04  0.0007138143  0.0007593676  4.629872e-05
+#> [20,] -0.0004184120  8.371022e-05 -0.0002732981 -0.0013148423 -9.538320e-05
+#> [21,]  0.0003631314 -3.905702e-04  0.0004240167 -0.0001150535  1.791806e-04
+#>                1980          1985          1990          1995          2000
+#>  [1,] 14.4819380766 15.0050326629 30.6160862966  2.279134e+01 33.4094386250
+#>  [2,] 24.2440961290 14.9144048249 39.4586215291  3.366381e+01 27.6300354900
+#>  [3,] 19.8909763056 12.9976172834 31.3140753898  3.926617e+01 35.2017492389
+#>  [4,] 34.5412167950 31.1408714290 53.7835608961  5.806982e+01 56.4028387060
+#>  [5,] 35.7746574910 48.8141184045 46.6091830651  3.355813e+01 46.6090142025
+#>  [6,] 12.2320674097 15.7490655447 36.0248896021  2.994211e+01 35.2341965234
+#>  [7,] 15.2768180385 15.1454438950 48.9912652841  2.939288e+01 43.0299390765
+#>  [8,]  3.4133017642  2.6512718093 35.7081057973  2.377502e+01 38.0465234759
+#>  [9,] 11.3648881127  0.9157143211 26.0002282136  1.815304e+01 25.2147939559
+#> [10,] -0.9025899903 -6.1414794104  9.4697258728  9.669273e+00 15.3801489305
+#> [11,]  4.1228516807  1.9180957545 11.4602048293  7.420501e-01  9.1744221357
+#> [12,]  2.8324768102  8.0029005255 13.9249297918  9.461663e+00  7.0314765200
+#> [13,]  5.9587573287  6.1901407708 12.8552522823  1.254064e+01  5.9398053623
+#> [14,]  5.0265223258  6.1334063238 15.3105496329  6.693441e+00  6.2767061297
+#> [15,]  3.0667904029  2.1040520686  8.0829480782  9.051541e-01  4.9118127171
+#> [16,]  1.1085509966  0.4440585403  4.6902499048 -1.740263e+00  1.6956094777
+#> [17,] -0.3545171554 -1.0788234380 -0.8912981728 -3.259234e+00  1.0782154117
+#> [18,] -0.0007250264 -0.0003707507  0.0009264276 -1.502028e-03  0.0006504005
+#> [19,] -0.0010096296 -0.0013578629 -0.0001364608 -1.536187e-03  0.0002672308
+#> [20,] -0.0005597711  0.0002593028  0.0006724129  4.940180e-06  0.0000220428
+#> [21,] -0.0002663077  0.0002379727  0.0001025889 -1.467865e-04 -0.0004672003
+#>                2005          2010          2015          2020          2025
+#>  [1,] 35.2063149299  2.574073e+01  1.686618e+01  3.726188e+01  3.596241e+01
+#>  [2,] 39.7815636351  5.198246e+01  3.545773e+01  1.621746e+01  1.572724e+01
+#>  [3,] 43.6134320305  4.087724e+01  2.529691e+01  1.243047e+01  1.205461e+01
+#>  [4,] 69.4862261899  6.151655e+01  1.053807e+02  6.337355e+01  6.148319e+01
+#>  [5,] 61.4795683305  1.081416e+02  1.462701e+02  1.140761e+02  1.106660e+02
+#>  [6,] 40.9816130294  9.212637e+01  2.026667e+01  1.096321e+02  1.063574e+02
+#>  [7,] 54.2000383773  6.484557e+01  5.491794e+01  8.303648e+01  8.055508e+01
+#>  [8,] 54.3621106693  6.210615e+01  4.419383e+01  5.759037e+01  5.586794e+01
+#>  [9,] 46.1009769761  4.050389e+01  2.708597e+01  3.866853e+01  3.751901e+01
+#> [10,] 24.3645150956  3.725019e+01  8.129041e+00  2.565676e+01  2.489228e+01
+#> [11,] 19.0808001159  1.891404e+01  1.625201e+01  1.695107e+01  1.644513e+01
+#> [12,]  0.6483630675  1.268029e+01  3.792549e+01  1.118371e+01  1.085143e+01
+#> [13,] 15.0435641770  1.036964e+01  1.188830e+01  7.376952e+00  7.159785e+00
+#> [14,]  6.2874481579  8.037788e+00  3.314290e+01  4.860257e+00  4.720100e+00
+#> [15,]  1.6845365642  1.544022e+00  1.088919e+00  3.201408e+00  3.110899e+00
+#> [16,]  1.7851618553  2.240903e+00  2.708415e+00  2.114225e+00  2.050721e+00
+#> [17,]  4.4904038435  1.151357e+01 -2.168209e+00  1.393847e+00  1.351674e+00
+#> [18,]  0.0003877412 -3.414889e-04  1.892320e-03  1.947218e-03 -5.815492e-05
+#> [19,] -0.0002732069  3.774664e-04 -1.778366e-03 -4.196944e-04  6.462397e-04
+#> [20,]  0.0003672265 -2.385004e-04 -1.187608e-03  9.547959e-04 -4.276800e-04
+#> [21,]  0.0004257843  1.591630e-04 -5.068183e-05 -3.603256e-04 -1.501552e-04
+#>               2030          2035          2040          2045          2050
+#>  [1,] 3.713937e+01  3.872296e+01  38.710244991  3.875942e+01  3.884919e+01
+#>  [2,] 1.624346e+01  1.692868e+01  16.922542527  1.694990e+01  1.698616e+01
+#>  [3,] 1.245485e+01  1.298092e+01  12.972254791  1.299493e+01  1.303085e+01
+#>  [4,] 6.348393e+01  6.617035e+01  66.152791816  6.626290e+01  6.641678e+01
+#>  [5,] 1.142679e+02  1.191112e+02 119.075586720  1.192639e+02  1.195424e+02
+#>  [6,] 1.098159e+02  1.144742e+02 114.442603943  1.146180e+02  1.148904e+02
+#>  [7,] 8.317144e+01  8.669744e+01  86.667686177  8.681930e+01  8.701090e+01
+#>  [8,] 5.769732e+01  6.012598e+01  60.113368482  6.020745e+01  6.034099e+01
+#>  [9,] 3.874399e+01  4.037813e+01  40.365452745  4.042687e+01  4.052742e+01
+#> [10,] 2.570411e+01  2.679773e+01  26.784212118  2.683642e+01  2.688482e+01
+#> [11,] 1.699112e+01  1.771062e+01  17.697703877  1.773515e+01  1.777363e+01
+#> [12,] 1.120040e+01  1.167745e+01  11.672268085  1.169656e+01  1.172089e+01
+#> [13,] 7.386918e+00  7.707266e+00   7.697176849  7.715443e+00  7.730054e+00
+#> [14,] 4.878769e+00  5.078950e+00   5.072805619  5.084954e+00  5.100982e+00
+#> [15,] 3.212492e+00  3.348749e+00   3.347115246  3.356025e+00  3.363517e+00
+#> [16,] 2.116043e+00  2.206720e+00   2.205534484  2.206645e+00  2.212107e+00
+#> [17,] 1.392679e+00  1.453848e+00   1.454611333  1.461609e+00  1.459596e+00
+#> [18,] 5.095878e-04  1.592002e-03  -0.003113428 -1.789957e-04 -1.280066e-03
+#> [19,] 6.580606e-04 -6.258823e-04   0.001700642 -2.324909e-03  1.481708e-03
+#> [20,] 5.002212e-05 -3.570606e-04  -0.001343537  1.088385e-03  9.059035e-05
+#> [21,] 6.132923e-05 -2.312750e-04   0.000230856 -1.040497e-04  4.127133e-05
                                                              
+# Net migration for females using stock change method
+mig_res$mig_f
+
                                                              #>               1955         1960        1965        1970       1975       1980
+#>  [1,] 113.17293508 114.89636101 90.23263311 87.45872642 90.0432974 70.4587935
+#>  [2,]  95.34564459  97.41671484 70.19452018 65.87207075 59.8263941 44.5359312
+#>  [3,]  39.38203524  37.98494056 21.59118455 30.06125339 40.8511939 25.2266777
+#>  [4,]  18.93727890  15.46635355 15.01716094 35.73093675 43.5519113 44.4185849
+#>  [5,]  35.06866853  38.29910784 32.49304247 61.96567298 71.3823189 60.4114357
+#>  [6,]  46.52931657  51.15055184 29.78558329 69.40305410 73.3726655 28.8506481
+#>  [7,]  49.26659651  53.64077560 28.11913980 41.28897946 46.1446383 30.6183323
+#>  [8,]  42.94237789  47.62602471 24.65857528 25.85740478 24.8366719 12.9039601
+#>  [9,]  29.40340471  20.97115764 13.49470393 11.16104258 23.5821805 19.5062463
+#> [10,]  18.57086996  17.19342125  7.68481801 16.01699993 16.0862520 12.4982334
+#> [11,]  13.71191120  17.11939842 13.92770203 13.62686866 27.4480823 18.0019021
+#> [12,]  10.00669977  14.47415466 10.65372584 22.55101461 15.3631445 21.7322432
+#> [13,]  11.50849438  12.18200068 18.50430067 27.64042225 28.9167882 29.1498623
+#> [14,]  22.50025175  25.23334367 26.44347204 35.00252644 34.6956380 36.6317503
+#> [15,]  29.00954957  31.29857322 32.48545915 32.31809795 32.5289233 38.7470346
+#> [16,]  16.82217097  18.04473806 21.47476384 30.18757231 33.0546994 39.0628759
+#> [17,]  19.08659895  21.79087700 26.53310358 31.16165091 30.7052393 36.2665503
+#> [18,]   7.00799770   9.63018174 11.75234533 16.01617288 19.8479281 24.8705826
+#> [19,]   2.73784736   3.09952968  4.37523028  5.83026818  8.0543360 11.6835028
+#> [20,]   0.47757027   0.63464332  0.73783617  1.16624771  1.6228540  2.8761175
+#> [21,]   0.03766896   0.04934962  0.06275325  0.08664575  0.1430509  0.2940806
+#>             1985       1990       1995       2000       2005        2010
+#>  [1,] 65.4905895 76.3891331 68.7130184 70.2702199 57.8078009  48.4244763
+#>  [2,] 33.0373278 54.3669316 47.8767793 41.8410746 46.4490916  53.0134297
+#>  [3,] 23.5971948 35.5482798 42.9332129 37.8361399 48.9140770  41.0945558
+#>  [4,] 30.0666391 49.2268809 54.1049977 53.0111697 61.1189534  59.3054565
+#>  [5,] 51.8729917 66.5234561 53.3486767 57.1899226 80.8186413 108.2549385
+#>  [6,] 24.8247651 59.2788922 49.3469215 48.0041198 67.9264170 111.1587027
+#>  [7,] 30.4020995 63.2339790 44.9045881 54.5849296 74.0834107 103.0106675
+#>  [8,] 13.7500933 46.1317183 42.2221569 54.2424859 64.2324081  79.7542022
+#>  [9,] 16.8177810 42.4617981 35.6323975 44.6059232 63.2572127  59.0680211
+#> [10,] 10.6764505 24.0128959 31.6798027 36.3022661 41.1520887  57.4674939
+#> [11,] 19.1201073 21.6856843 25.6122443 28.1799534 37.8505654  39.2408961
+#> [12,] 27.1662997 25.9759242 32.3966763 25.4397052 35.6465904  39.5720843
+#> [13,] 31.4169027 32.3333678 33.6616631 29.4462690 42.3024418  45.1001209
+#> [14,] 38.5246016 46.0765985 37.8977766 37.4361944 40.0309377  44.4626225
+#> [15,] 41.3330306 45.5233111 45.3688341 47.3005896 48.0556069  45.6277274
+#> [16,] 43.8517132 52.2627443 48.9690557 59.9854503 61.9408546  63.0543494
+#> [17,] 39.1634375 44.2990904 50.6981503 60.8224943 76.3761824  86.1956876
+#> [18,] 30.2454966 34.3783368 40.9716493 47.5660261 54.0094551  70.3597063
+#> [19,] 15.3906445 17.5112915 20.6083492 22.8002233 27.7876959  34.3398055
+#> [20,]  4.4186387  5.1672347  5.9996563  6.0220990  6.9467713   9.5562259
+#> [21,]  0.5528349  0.7229901  0.8275142  0.6994879  0.6407134   0.8698355
+#>             2015        2020          2025          2030          2035
+#>  [1,]  40.893862  54.2482009  3.639646e+01  3.756888e+01  3.915000e+01
+#>  [2,]  44.012591  21.6549536  1.571378e+01  1.622971e+01  1.691831e+01
+#>  [3,]  28.495198  15.8682358  1.285681e+01  1.327584e+01  1.383276e+01
+#>  [4,]  93.817691  72.3096374  6.796569e+01  7.018092e+01  7.315433e+01
+#>  [5,] 150.599940 125.3306506  1.182762e+02  1.221251e+02  1.272877e+02
+#>  [6,]  82.384536 118.5424184  1.095158e+02  1.130743e+02  1.178537e+02
+#>  [7,] 100.055532  89.6012339  7.987071e+01  8.246815e+01  8.595619e+01
+#>  [8,]  66.203624  64.3219840  5.333906e+01  5.508070e+01  5.740907e+01
+#>  [9,]  51.479253  46.0958958  3.449321e+01  3.560806e+01  3.711957e+01
+#> [10,]  25.465817  34.5174228  2.203458e+01  2.275099e+01  2.371591e+01
+#> [11,]  36.408361  29.2195403  1.402299e+01  1.447923e+01  1.509269e+01
+#> [12,]  46.657383  32.4093781  8.899044e+00  9.189698e+00  9.580706e+00
+#> [13,]  37.500438  38.5073520  5.651936e+00  5.833683e+00  6.079587e+00
+#> [14,]  53.185508  47.8148003  3.591765e+00  3.699831e+00  3.864664e+00
+#> [15,]  55.705684  62.3853696  2.279530e+00  2.350295e+00  2.453483e+00
+#> [16,]  65.979261  71.5828801  1.447269e+00  1.494431e+00  1.554029e+00
+#> [17,]  75.191382  78.4085394  9.191045e-01  9.422448e-01  9.851964e-01
+#> [18,]  75.580026  73.6692924  2.023718e-03 -1.309560e-03 -3.205581e-03
+#> [19,]  44.131329  45.7727592 -4.575784e-05 -1.760394e-03  5.530703e-05
+#> [20,]  11.032250  13.0486004 -6.680146e-04  1.115547e-03  2.261978e-04
+#> [21,]   0.919266   0.8355002 -3.823113e-06 -6.037463e-04  1.228691e-05
+#>                2040          2045          2050
+#>  [1,]  3.915065e+01  3.921944e+01  3.931634e+01
+#>  [2,]  1.691457e+01  1.693440e+01  1.697581e+01
+#>  [3,]  1.383380e+01  1.385223e+01  1.388592e+01
+#>  [4,]  7.313045e+01  7.324795e+01  7.341229e+01
+#>  [5,]  1.272581e+02  1.274533e+02  1.277532e+02
+#>  [6,]  1.178278e+02  1.180086e+02  1.182870e+02
+#>  [7,]  8.594408e+01  8.607590e+01  8.627359e+01
+#>  [8,]  5.739172e+01  5.748318e+01  5.762230e+01
+#>  [9,]  3.711233e+01  3.716508e+01  3.724629e+01
+#> [10,]  2.371210e+01  2.374261e+01  2.380269e+01
+#> [11,]  1.509005e+01  1.510669e+01  1.514986e+01
+#> [12,]  9.583997e+00  9.598664e+00  9.619318e+00
+#> [13,]  6.081444e+00  6.084633e+00  6.102345e+00
+#> [14,]  3.859534e+00  3.866878e+00  3.879600e+00
+#> [15,]  2.451742e+00  2.453048e+00  2.460003e+00
+#> [16,]  1.554556e+00  1.562182e+00  1.559817e+00
+#> [17,]  9.835399e-01  9.827943e-01  9.950495e-01
+#> [18,]  2.799884e-03  3.560486e-03 -4.231530e-03
+#> [19,] -7.542806e-04  2.292086e-03 -1.102522e-03
+#> [20,]  7.982875e-04  2.063124e-03 -4.913987e-04
+#> [21,] -5.497503e-04 -2.754639e-04 -2.854814e-05
                                                              
+
+################ cohort even flow method  #####################
+
+# We reuse the same data from before
+
+mig_res <-
+  mig_resid_cohort(
+    pop_m_mat = pop_m_mat,
+    pop_f_mat = pop_f_mat,
+    sr_m_mat = sr_m_mat,
+    sr_f_mat = sr_f_mat,
+    asfr_mat = asfr_mat,
+    srb_vec = srb_vec,
+    ages = ages,
+    ages_fertility = ages_fertility
+  )
+
+# Net migration for males using the cohort even flow method
+mig_res$mig_m
+
                                                              #>                1955          1960          1965          1970          1975
+#>  [1,] 27.7345854720 31.0019371482  5.1942053540 24.5624659219  4.688525e+01
+#>  [2,] 40.5102116592 41.2343357598 15.1606536999 21.1793513869  3.271567e+01
+#>  [3,] 18.3013151966 16.4222220410  1.5058793365 17.6053516384  3.102412e+01
+#>  [4,] 14.5468170638 10.0564010777 -3.5687147753 28.0614898551  4.172147e+01
+#>  [5,] 34.8888314231 38.1140374410 10.6856647744 62.5658663403  7.055654e+01
+#>  [6,] 42.6329532877 54.2516150063 19.7676547862 69.7666525943  7.093797e+01
+#>  [7,] 37.2248714152 43.8717655890 12.7781665228 40.8677327075  3.842664e+01
+#>  [8,] 32.0814535293 26.5020660760  5.2333131522 18.0380639382  2.068785e+01
+#>  [9,] 23.2334561570 10.9459192982 -2.4258656763  8.3347645192  1.089106e+01
+#> [10,] 15.3265554535  7.2748524613 -2.5633574346  4.0973563278  7.587504e+00
+#> [11,]  5.3028125043  4.1864884603 -1.3245914367  3.5133073257  4.957193e+00
+#> [12,] -1.6476700692  0.9576589788 -1.4395096532  6.8074763188  4.347471e+00
+#> [13,]  3.0476956367  5.0897985122  2.5236522427  8.7981552818  9.271884e+00
+#> [14,]  7.2975977560  8.6761911905  5.4904630231  6.0964521738  8.217822e+00
+#> [15,]  2.5311742841  3.4940993902  2.6452918966  0.2448806986  3.800742e+00
+#> [16,]  1.3981785084  2.0660658863  0.9794162929  1.3492354084 -3.171552e-01
+#> [17,]  1.7122472632  1.8870772095  1.5306732316  1.9744450576 -4.164296e-01
+#> [18,] -0.0008180112 -0.0003418182 -0.0005657794  0.0009453795 -1.747616e-04
+#> [19,] -0.0011282773 -0.0002951569  0.0003245206 -0.0008184162 -6.416606e-05
+#> [20,] -0.0009690561  0.0001945259 -0.0006136342 -0.0029657259 -2.079006e-04
+#> [21,]  0.0003631314 -0.0003905702  0.0004240167 -0.0001150535  1.791806e-04
+#>                1980          1985          1990          1995          2000
+#>  [1,] 26.7290464322 22.5514135124 50.5005203859  3.971947e+01  4.733699e+01
+#>  [2,] 22.0962653344 13.9694166410 35.4141109601  3.648698e+01  3.143147e+01
+#>  [3,] 27.2613524622 22.0997423995 42.5971117509  4.871322e+01  4.583846e+01
+#>  [4,] 35.2720807678 40.0886284255 50.3067733763  4.589406e+01  5.158882e+01
+#>  [5,] 24.1056789950 32.3931085097 41.4420067581  3.183571e+01  4.101680e+01
+#>  [6,] 13.8048985375 15.4971051546 42.6358411330  2.975410e+01  3.922889e+01
+#>  [7,]  9.3790926968  8.9270470232 42.4876305010  2.667472e+01  4.065530e+01
+#>  [8,]  7.4318376300  1.7905172492 30.9784476453  2.105322e+01  3.174649e+01
+#>  [9,]  5.2620681370 -2.6361050610 17.8294359966  1.398769e+01  2.039907e+01
+#> [10,]  1.6409798784 -2.1235510680 10.5680414394  5.242261e+00  1.236784e+01
+#> [11,]  3.5528032225  5.0697335208 12.9059761124  5.190501e+00  8.201545e+00
+#> [12,]  4.5669024604  7.3126011011 13.7574122211  1.127484e+01  6.618932e+00
+#> [13,]  5.7964848148  6.4725021367 14.7392106038  9.973541e+00  6.324542e+00
+#> [14,]  4.3823473498  4.4122945905 12.4941899906  4.003111e+00  5.912938e+00
+#> [15,]  2.3424884610  1.4118350206  7.0917545044 -4.958813e-01  3.587037e+00
+#> [16,]  0.4223807796 -0.4160797352  2.1344354343 -2.976618e+00  1.602920e+00
+#> [17,] -0.2237692930 -0.6727819681 -0.5508956693 -1.992855e+00  6.527169e-01
+#> [18,] -0.0013472520 -0.0013649720  0.0005310532 -2.288892e-03  6.637464e-04
+#> [19,] -0.0014153765 -0.0008447875  0.0005782918 -1.248544e-03  2.421791e-04
+#> [20,] -0.0011599514  0.0005239992  0.0013807440  1.016482e-05  4.658246e-05
+#> [21,] -0.0002663077  0.0002379727  0.0001025889 -1.467865e-04 -4.672003e-04
+#>               2005          2010          2015          2020          2025
+#>  [1,] 5.521348e+01  5.181794e+01  3.464569e+01  4.546868e+01  4.391070e+01
+#>  [2,] 4.171498e+01  4.644763e+01  3.038654e+01  1.432799e+01  1.389523e+01
+#>  [3,] 5.658804e+01  5.122663e+01  6.537600e+01  3.792246e+01  3.678382e+01
+#>  [4,] 6.557516e+01  8.495273e+01  1.259713e+02  8.882648e+01  8.614305e+01
+#>  [5,] 5.133396e+01  1.003256e+02  8.339817e+01  1.120286e+02  1.086440e+02
+#>  [6,] 4.769132e+01  7.864393e+01  3.765750e+01  9.649354e+01  9.359811e+01
+#>  [7,] 5.441186e+01  6.361715e+01  4.964878e+01  7.043648e+01  6.832703e+01
+#>  [8,] 5.038857e+01  5.144900e+01  3.572532e+01  4.823902e+01  4.679176e+01
+#>  [9,] 3.538553e+01  3.904144e+01  1.766614e+01  3.227328e+01  3.130150e+01
+#> [10,] 2.187529e+01  2.825565e+01  1.226915e+01  2.142061e+01  2.076887e+01
+#> [11,] 9.953746e+00  1.595878e+01  2.736801e+01  1.418917e+01  1.375317e+01
+#> [12,] 8.024718e+00  1.171475e+01  2.523316e+01  9.404806e+00  9.113674e+00
+#> [13,] 1.095858e+01  9.441914e+00  2.309601e+01  6.246623e+00  6.051291e+00
+#> [14,] 4.157159e+00  4.963837e+00  1.761464e+01  4.165787e+00  4.032837e+00
+#> [15,] 1.879553e+00  2.032313e+00  2.029089e+00  2.804870e+00  2.709220e+00
+#> [16,] 3.633292e+00  7.856819e+00  2.269679e-01  1.921612e+00  1.849407e+00
+#> [17,] 2.648544e+00  6.637323e+00 -1.231256e+00  7.883184e-01  7.562531e-01
+#> [18,] 4.033675e-05  7.105796e-05 -1.498956e-04  9.007669e-04  4.340415e-04
+#> [19,] 1.617833e-04  5.104875e-05 -2.514495e-03  6.246262e-04  5.782513e-05
+#> [20,] 7.570578e-04 -4.775804e-04 -2.347887e-03  1.876386e-03 -8.238661e-04
+#> [21,] 4.257843e-04  1.591630e-04 -5.068183e-05 -3.603256e-04 -1.501552e-04
+#>               2030          2035          2040          2045         2050
+#>  [1,] 4.533636e+01  4.725714e+01  4.723415e+01  4.729110e+01 4.739403e+01
+#>  [2,] 1.435297e+01  1.495834e+01  1.495057e+01  1.497529e+01 1.501112e+01
+#>  [3,] 3.798272e+01  3.958804e+01  3.957368e+01  3.963905e+01 3.973308e+01
+#>  [4,] 8.893813e+01  9.269935e+01  9.266739e+01  9.281208e+01 9.302437e+01
+#>  [5,] 1.121642e+02  1.169091e+02  1.168658e+02  1.170394e+02 1.173074e+02
+#>  [6,] 9.662625e+01  1.007130e+02  1.006725e+02  1.008273e+02 1.010516e+02
+#>  [7,] 7.054235e+01  7.351530e+01  7.348607e+01  7.360189e+01 7.375815e+01
+#>  [8,] 4.831215e+01  5.033959e+01  5.031996e+01  5.039165e+01 5.050329e+01
+#>  [9,] 3.231319e+01  3.367316e+01  3.365320e+01  3.370409e+01 3.377327e+01
+#> [10,] 2.144079e+01  2.234327e+01  2.232287e+01  2.236153e+01 2.239942e+01
+#> [11,] 1.419322e+01  1.478717e+01  1.477057e+01  1.479497e+01 1.482056e+01
+#> [12,] 9.393798e+00  9.787974e+00  9.772460e+00  9.787043e+00 9.800488e+00
+#> [13,] 6.235955e+00  6.491396e+00  6.475070e+00  6.483358e+00 6.492494e+00
+#> [14,] 4.154458e+00  4.317637e+00  4.305202e+00  4.308499e+00 4.313789e+00
+#> [15,] 2.783655e+00  2.892001e+00  2.881492e+00  2.878664e+00 2.878182e+00
+#> [16,] 1.892981e+00  1.963852e+00  1.953794e+00  1.949436e+00 1.943300e+00
+#> [17,] 7.721803e-01  8.009601e-01  7.934284e-01  7.943901e-01 7.884978e-01
+#> [18,] 7.786621e-04  5.130995e-04 -6.648082e-04 -1.703315e-03 2.604560e-04
+#> [19,] 5.174199e-04 -7.749870e-04 -5.907210e-05 -6.034064e-04 1.089173e-03
+#> [20,] 9.482005e-05 -6.681631e-04 -2.483761e-03  1.989245e-03 1.637868e-04
+#> [21,] 6.132923e-05 -2.312750e-04  2.308560e-04 -1.040497e-04 4.127133e-05
                                                              
+# Net migration for females using the cohort even flow method
+mig_res$mig_f
+
                                                              #>               1955         1960         1965         1970        1975
+#>  [1,] 171.72131816 173.14456142 131.77820968 125.77017105 124.6968682
+#>  [2,]  69.20081450  69.28238640  46.84641331  48.75618851  50.9922442
+#>  [3,]  29.46477771  26.96618336  18.44416874  33.10558630  42.4374326
+#>  [4,]  27.29600628  27.13733711  23.95068047  49.19164496  57.8183162
+#>  [5,]  41.33304378  45.22976783  31.44274248  66.24574140  72.9147253
+#>  [6,]  48.61860483  53.07861527  29.28367497  55.88984313  60.2716586
+#>  [7,]  46.88561716  51.38378241  26.73500957  33.95470778  35.8411804
+#>  [8,]  36.86298992  34.86859624  19.36135344  18.75110168  24.4968027
+#>  [9,]  24.51040044  19.45753274  10.77528557  13.81136654  20.1137017
+#> [10,]  16.56535817  17.57029029  11.05335164  15.11774766  22.1790338
+#> [11,]  12.26265055  16.29327790  12.64429657  18.59839574  21.9134313
+#> [12,]  11.30080079  13.92818445  15.23128103  26.10027153  22.9939702
+#> [13,]  18.40400698  20.13441348  24.01142703  33.27825496  33.6423294
+#> [14,]  29.00224834  31.55144157  32.65724529  36.93668833  36.6712748
+#> [15,]  27.17509206  28.94143981  31.42783859  36.27914530  37.7960682
+#> [16,]  23.83314481  26.11712003  31.07521196  39.00073414  39.9437017
+#> [17,]  19.13202079  22.82358621  27.36889447  33.37685139  35.5223684
+#> [18,]   8.57065143  10.90765432  13.64065673  18.13800515  22.8559186
+#> [19,]   3.41815884   3.91939256   5.21967120   7.05826177   9.5714802
+#> [20,]   1.25221623   1.63514702   1.86297785   2.89364187   3.9438894
+#> [21,]   0.03766896   0.04934962   0.06275325   0.08664575   0.1430509
+#>             1980       1985        1990       1995       2000       2005
+#>  [1,] 95.9520589 84.4975756 106.1761668 94.7745340 93.0428964 82.3545273
+#>  [2,] 35.2853877 28.5730084  45.3124335 45.7159530 40.0648036 47.8985128
+#>  [3,] 34.9937120 26.9396863  42.5369562 48.6845584 45.5573823 55.1597026
+#>  [4,] 52.7039012 41.1581199  58.1083629 53.9313215 55.2936585 71.1989878
+#>  [5,] 44.9214309 38.5564169  63.2154839 51.5985008 52.8430913 74.7031986
+#>  [6,] 29.9682458 27.7977659  61.6234186 47.4051198 51.6083434 71.4428640
+#>  [7,] 21.9525937 22.2422255  55.0624099 43.8731207 54.8247021 69.7022819
+#>  [8,] 16.3825468 15.4282339  44.6721699 39.2611120 49.8762677 64.3662482
+#>  [9,] 16.2070083 13.9022608  33.5791503 34.0132172 40.9028124 52.7987748
+#> [10,] 15.5127441 15.1324013  23.1657203 29.0358476 32.6897767 40.0602448
+#> [11,] 20.3315864 23.6430427  24.3027282 29.5679655 27.3167444 37.4326840
+#> [12,] 26.3244627 30.2152519  30.0197155 33.9584308 28.2084960 40.0225780
+#> [13,] 34.6656953 36.7180278  41.0735925 37.3701400 34.8975556 42.8133296
+#> [14,] 40.9309041 43.1354913  49.2244096 44.6705493 45.3497331 46.9441002
+#> [15,] 44.4445436 48.3036273  55.1229691 52.8406941 59.9426380 61.0737010
+#> [16,] 46.6140330 50.7362876  58.3363476 59.9609448 72.1377381 82.0927869
+#> [17,] 42.3677161 47.5944714  53.1999714 61.2981433 71.6760979 84.6800893
+#> [18,] 29.5426976 36.3664993  40.6306969 47.4201296 53.3764469 60.9562720
+#> [19,] 14.1771134 19.0369669  21.3787584 24.5943500 26.1127004 30.5702757
+#> [20,]  6.8026913 10.2343876  11.7002979 13.3059286 13.1155117 14.6560891
+#> [21,]  0.2940806  0.5528349   0.7229901  0.8275142  0.6994879  0.6407134
+#>              2010       2015        2020          2025          2030
+#>  [1,]  75.8669558  63.536304  65.7260833  4.433063e+01  4.575479e+01
+#>  [2,]  47.2331783  36.364455  18.8075067  1.428867e+01  1.475587e+01
+#>  [3,]  50.3105897  61.269592  44.1589114  4.042136e+01  4.173772e+01
+#>  [4,]  84.0183980 122.497446  99.0230306  9.315949e+01  9.618904e+01
+#>  [5,] 110.1303313 116.841872 122.2617185  1.139634e+02  1.176635e+02
+#>  [6,] 107.6456040  91.599610 104.4228958  9.476476e+01  9.783964e+01
+#>  [7,]  92.0023679  83.556553  77.2882500  6.666796e+01  6.883477e+01
+#>  [8,]  69.9977501  59.226427  55.5069429  4.397429e+01  4.539983e+01
+#>  [9,]  58.8675964  38.779598  40.5871463  2.832219e+01  2.923507e+01
+#> [10,]  48.9595289  31.279570  32.1673624  1.808882e+01  1.867232e+01
+#> [11,]  40.0787725  42.163635  31.2312923  1.152124e+01  1.189181e+01
+#> [12,]  43.3773939  42.973366  36.1762133  7.334320e+00  7.567618e+00
+#> [13,]  46.4391888  46.928486  44.5379457  4.679762e+00  4.821689e+00
+#> [14,]  47.7359675  57.430282  57.9859587  2.994345e+00  3.080767e+00
+#> [15,]  59.9554207  66.529195  72.8098599  1.925118e+00  1.981168e+00
+#> [16,]  87.8106758  81.908880  86.2100493  1.251706e+00  1.283795e+00
+#> [17,] 100.6440966  96.146695  95.5267726  4.973036e-01  5.053310e-01
+#> [18,]  76.3506288  86.396271  84.8470594  1.139063e-03 -1.900901e-03
+#> [19,]  37.8712336  46.461630  48.6423262 -5.916063e-04 -2.214464e-04
+#> [20,]  19.6212289  22.183238  25.5167066 -1.122828e-03  1.857040e-03
+#> [21,]   0.8698355   0.919266   0.8355002 -3.823113e-06 -6.037463e-04
+#>                2035          2040          2045          2050
+#>  [1,]  4.767406e+01  4.766587e+01  4.773854e+01  4.785075e+01
+#>  [2,]  1.537836e+01  1.537671e+01  1.539557e+01  1.543288e+01
+#>  [3,]  4.350211e+01  4.348980e+01  4.355698e+01  4.365529e+01
+#>  [4,]  1.002545e+02  1.002246e+02  1.003781e+02  1.006076e+02
+#>  [5,]  1.226309e+02  1.225980e+02  1.227812e+02  1.230660e+02
+#>  [6,]  1.019700e+02  1.019459e+02  1.020974e+02  1.023309e+02
+#>  [7,]  7.174004e+01  7.172074e+01  7.182816e+01  7.199256e+01
+#>  [8,]  4.731691e+01  4.730032e+01  4.736846e+01  4.747491e+01
+#>  [9,]  3.047038e+01  3.046052e+01  3.049816e+01  3.056507e+01
+#> [10,]  1.945852e+01  1.945084e+01  1.947030e+01  1.951809e+01
+#> [11,]  1.239102e+01  1.238686e+01  1.239838e+01  1.242642e+01
+#> [12,]  7.883037e+00  7.881202e+00  7.886047e+00  7.901435e+00
+#> [13,]  5.024070e+00  5.018025e+00  5.019264e+00  5.030757e+00
+#> [14,]  3.211853e+00  3.203963e+00  3.204237e+00  3.210327e+00
+#> [15,]  2.059526e+00  2.054404e+00  2.054788e+00  2.053114e+00
+#> [16,]  1.332189e+00  1.326888e+00  1.326015e+00  1.327060e+00
+#> [17,]  5.244282e-01  5.245498e-01  5.223082e-01  5.222588e-01
+#> [18,] -1.787040e-03  1.098362e-03  3.448556e-03 -3.048916e-03
+#> [19,]  2.219107e-04  1.682787e-04  3.117486e-03 -1.084673e-03
+#> [20,]  3.723792e-04  1.301420e-03  3.330889e-03 -7.856163e-04
+#> [21,]  1.228691e-05 -5.497503e-04 -2.754639e-04 -2.854814e-05
                                                              
+################ time even flow method  #####################
+
+# We reuse the same data from before
+
+mig_res <-
+  mig_resid_time(
+    pop_m_mat = pop_m_mat,
+    pop_f_mat = pop_f_mat,
+    sr_m_mat = sr_m_mat,
+    sr_f_mat = sr_f_mat,
+    asfr_mat = asfr_mat,
+    srb_vec = srb_vec,
+    ages = ages,
+    ages_fertility = ages_fertility
+  )
+
+# Net migration for males using the time even flow method
+mig_res$mig_m
+
                                                              #>               1955         1960          1965         1970         1975
+#>  [1,]   3.57986456   7.83804281 -11.708613930  25.81847493  58.42237710
+#>  [2,]  99.65372764  99.91545521  56.096782636  20.16120060  11.21457792
+#>  [3,] -40.94162774 -42.78430686 -39.882708344  18.59078430  50.05512859
+#>  [4,]  55.48348586  51.36112198  29.686148299  13.09179503  12.81047107
+#>  [5,] -11.79757463 -19.64383338 -33.657655806  67.25140324  90.92287133
+#>  [6,] 107.42501258 140.01220683  80.160934509 102.29400345  87.21131258
+#>  [7,] -32.40904222 -43.26549135 -47.553510864   7.44391246  18.67159066
+#>  [8,] 106.17661163 121.93971456  66.014834078  46.46533877  29.35435108
+#>  [9,] -51.15631402 -93.89843855 -63.174578114 -27.88927758   5.48775806
+#> [10,]  88.57046150 108.82847303  50.267264262  42.37000677   3.42290940
+#> [11,] -63.32934115 -93.13047934 -47.057179615 -39.73615654  17.77493878
+#> [12,]  58.46883834  92.26143114  37.785755257  49.95450413 -18.35527636
+#> [13,] -58.32986589 -84.64085443 -33.370638099 -31.75995974  34.90416044
+#> [14,]  66.83013145  92.79117747  37.313414670  46.77483495 -13.45378292
+#> [15,] -43.57046784 -62.56497815 -18.527162169 -34.44289291  24.15556419
+#> [16,]  30.01886407  45.21821705  11.378053697  22.72777192 -17.18309176
+#> [17,] -12.62450034 -21.29332857  -2.130173274  -7.74682943   9.31779578
+#> [18,]   5.60441174   9.70724986   0.986839238   3.68891047  -4.50288791
+#> [19,]  -1.70123799  -3.01062248  -0.313951952  -1.20302297   1.52466156
+#> [20,]   0.31632108   0.55768479   0.061728916   0.23383116  -0.32111739
+#> [21,]  -0.02811154  -0.05411331  -0.005447638  -0.02448547   0.03644719
+#>               1980         1985          1990         1995        2000
+#>  [1,]  28.96387615  30.01006533  61.232172593  45.58267324  66.8188773
+#>  [2,]  19.61458145  -0.11384016  17.798839229  21.81186415 -11.4789958
+#>  [3,]  20.20595285  26.10889922  44.850683328  56.74184507  81.8729514
+#>  [4,]  48.95919987  36.25809415  62.845864529  59.53667644  31.0999449
+#>  [5,]  23.01801705  61.62328797  30.756451497   7.89400063  62.2618417
+#>  [6,]   1.62298038 -29.72231189  41.475065426  52.03339643   8.4963877
+#>  [7,]  28.94199434  59.82581388  56.757960851   7.07303243  77.6072932
+#>  [8,] -21.87239151 -54.10090181  15.060691363  40.53058132  -1.0157575
+#>  [9,]  44.33117340  55.38825229  37.079303424  -3.83519751  51.4366294
+#> [10,] -45.24366939 -66.76114668 -17.615872177  23.12250876 -20.0512376
+#> [11,]  51.97198921  68.73233751  40.118018493 -21.15026980  38.0260225
+#> [12,] -43.51296896 -49.55790353 -10.657202225  39.33641676 -22.7965318
+#> [13,]  51.82676172  58.29271858  35.664479258 -11.98652737  33.5229966
+#> [14,] -35.33901983 -39.41621873  -1.325981088  24.25817390 -18.2014075
+#> [15,]  34.99220822  36.88719713  17.281657343 -18.97573621  25.6182594
+#> [16,] -23.57247670 -26.76288480  -3.793379475  11.33350586 -16.9839990
+#> [17,]  14.15059062  15.05938901   0.694028281 -14.10486407  13.7584471
+#> [18,]  -7.09942975  -7.76410472  -0.358176585   7.41969081  -7.3510661
+#> [19,]   2.59035597   2.91884576   0.132464878  -2.78840546   2.7397113
+#> [20,]  -0.60437431  -0.71424802  -0.030070852   0.65864048  -0.6134260
+#> [21,]   0.07855023   0.09881163   0.004110768  -0.08173701   0.0688248
+#>              2005         2010          2015        2020        2025
+#>  [1,]  70.4126299  51.48145831   33.73236083  74.5237684  71.9248264
+#>  [2,]   9.2223352  52.52998713   37.20561118 -42.0424248 -40.4151595
+#>  [3,]  78.0107215  29.25548089   13.40764281  66.8830873  64.5071365
+#>  [4,]  61.1002538  93.82266121  197.37105948  59.9439586  58.5164254
+#>  [5,]  62.1021505 122.80643192   95.77213448 168.3817044 162.9313907
+#>  [6,]  20.1148330  61.93362372  -54.90300355  51.4450008  50.2569949
+#>  [7,]  88.3718114  68.01002222  164.54991665 114.7947184 111.0135390
+#>  [8,]  20.8205517  56.52957864  -75.48650810   0.8280661   1.1274237
+#>  [9,]  71.5336598  24.86139074  129.21554946  76.5135937  73.9162539
+#> [10,] -22.0122498  49.89176029 -111.77480201 -24.5285913 -23.5568882
+#> [11,]  59.7886258 -11.24947223  142.64920256  58.0869502  56.1548785
+#> [12,] -56.8405940  36.31951173  -63.55416721 -34.4574904 -33.3626521
+#> [13,]  84.3838898 -14.13565389   85.10506293  48.0687406  46.6862333
+#> [14,] -65.7712511  29.32707902  -14.15647172 -35.8655365 -35.0816217
+#> [15,]  61.6773024 -23.35409488   15.10886419  39.3393827  38.7196217
+#> [16,] -47.0754634  24.20349394   -7.59220972 -29.9265975 -29.9667998
+#> [17,]  42.8165215   4.82076390    1.53813111  26.2495781  26.6336803
+#> [18,] -24.3487769  -2.93691216   -0.96750858 -16.8699992 -17.6523987
+#> [19,]   9.6710926   1.24605503    0.42211034   7.5546157   8.3531350
+#> [20,]  -2.2748115  -0.31123505   -0.11037382  -1.9541809  -2.2518481
+#> [21,]   0.2597073   0.03683747    0.01288714   0.2285256   0.2509391
+#>              2030        2035        2040        2045        2050
+#>  [1,]  74.2787331  77.4459176  77.4204900  77.5188470  77.6983814
+#>  [2,] -41.7427286 -43.5429804 -43.5345068 -43.5819780 -43.6922370
+#>  [3,]  66.6372408  69.4908108  69.4665111  69.5605646  69.7436785
+#>  [4,]  60.3817971  62.8975960  62.8819987  63.0042277  63.1255567
+#>  [5,] 168.2596094 175.4243967 175.3598544 175.6067308 176.0358055
+#>  [6,]  51.8140624  53.9474549  53.9155099  53.9903843  54.0801592
+#>  [7,] 114.6788458 119.5914528 119.5528521 119.7715458 120.0562085
+#>  [8,]   1.0935709   1.0225270   1.0072712   0.9519375   0.9121235
+#>  [9,]  76.3993357  79.7379684  79.7274719  79.9051533  80.1456864
+#> [10,] -24.4557117 -25.6296697 -25.6871280 -25.7956559 -25.9708380
+#> [11,]  58.1644701  60.7878345  60.8398404  61.0409161  61.3084604
+#> [12,] -34.7478059 -36.4591281 -36.5987410 -36.8176156 -37.0954370
+#> [13,]  48.5890735  50.9769737  51.1657131  51.4807825  51.8403704
+#> [14,] -36.8046551 -38.8691838 -39.2202149 -39.6397621 -40.0819810
+#> [15,]  40.7791816  43.1850851  43.6969678  44.2773104  44.8632888
+#> [16,] -32.0866992 -34.3995882 -35.1814702 -35.9946429 -36.7846096
+#> [17,]  28.8995750  31.3094023  32.3339309  33.3781480  34.3711942
+#> [18,] -19.6928288 -21.7940080 -22.9599143 -24.1187034 -25.2468632
+#> [19,]   9.6521764  10.9779695  11.8694772  12.7578752  13.6605184
+#> [20,]  -2.6861664  -3.1357372  -3.4757375  -3.8169784  -4.1788256
+#> [21,]   0.3042474   0.3588661   0.4034127   0.4472692   0.4953120
                                                              
+# Net migration for females using the time even flow method
+mig_res$mig_f
+
                                                              #>              1955         1960         1965         1970         1975
+#>  [1,] 226.3458702 229.79272201 180.46526622 174.91745284  180.0865949
+#>  [2,] -21.0374290 -22.28540657 -31.50143500 -36.12133305  -54.2299461
+#>  [3,]  99.3415777  97.83858796  74.17190806  95.74522542  135.2772394
+#>  [4,] -59.6809765 -65.39419993 -43.13633166 -23.17129337  -46.8001148
+#>  [5,] 128.4354061 140.68621033 107.36582549 146.74972311  188.9421056
+#>  [6,] -31.8378149 -35.02737104 -45.53762715  -5.25186295  -39.1701555
+#>  [7,] 129.3780748 141.35666443 100.68019100  87.71909797  130.7375337
+#>  [8,] -38.9436452 -41.71922721 -48.58209382 -33.86767281  -78.2687759
+#>  [9,]  96.1969156  82.17618397  74.01404694  55.22193698  123.4478722
+#> [10,] -54.6355206 -44.39140208 -55.84845122 -21.29545414  -87.4661601
+#> [11,]  78.9618389  76.33323073  81.03814346  47.61263555  138.8417828
+#> [12,] -52.9859019 -42.06729052 -54.48681576   0.34775936 -100.3924019
+#> [13,]  70.2110143  62.17078749  86.34367902  54.96362841  150.0050752
+#> [14,] -13.6705663  -2.18847224 -21.04847767  22.41355071  -61.7595508
+#> [15,]  68.3375982  64.28074915  81.42847971  46.82844725  114.8253826
+#> [16,] -10.3770787  -6.38475405 -12.10171449  27.99107016  -14.8924516
+#> [17,]  43.4733361  46.94240803  59.62805297  46.69267660   69.9571248
+#> [18,]  -1.9687420   1.39813765  -0.03423644  12.94953562   10.0953495
+#> [19,]   5.9489557   5.85004073   8.75937070   8.16573505   13.2737132
+#> [20,]   0.0898606   0.38802533   0.10170185   1.00605627    0.9981967
+#> [21,]   0.0679131   0.06587289   0.11661638   0.08288009    0.1932348
+#>               1980         1985        1990        1995        2000        2005
+#>  [1,]  140.9175869  130.9811791 152.7782663 137.4260367 140.5404398 115.6156018
+#>  [2,]  -47.6765502  -61.7171348 -40.8585367 -39.0325437 -54.6180620 -21.2182395
+#>  [3,]   97.6260741  108.3826166 111.6492954 124.6180639 129.9601873 118.9342766
+#>  [4,]   -7.8818342  -47.4337354 -12.4509483 -15.6031808 -23.1929547   3.9108808
+#>  [5,]  128.6090432  150.7026598 145.3857926 122.1649032 137.3844478 157.7556344
+#>  [6,]  -69.0487765  -99.2216052 -25.2362233 -22.1547875 -39.9084905 -20.2548668
+#>  [7,]  129.1419899  158.6280625 151.3826146 111.6798611 148.5439952 168.1412151
+#>  [8,] -100.8450048 -128.4887081 -56.8328512 -25.5163741 -37.6094921 -36.7292738
+#>  [9,]  137.5336842  159.5245635 140.7046297  96.3026480 126.0717377 162.4750899
+#> [10,] -108.6421302 -134.1399364 -89.3958210 -30.7009674 -50.4245702 -76.1732663
+#> [11,]  140.6039363  167.8574350 129.9678704  80.9786683 105.2077827 149.5078659
+#> [12,]  -89.8706856 -105.5535968 -72.2499150 -12.6897624 -49.8245648 -71.9891197
+#> [13,]  141.3008408  160.8936779 132.1033687  79.1982021 105.5948969 152.2723407
+#> [14,]  -51.4025974  -66.1123963 -26.1991832   4.4994582 -20.5531042 -58.3050739
+#> [15,]  119.4994680  137.3914275 112.9664642  86.9423211 112.0615845 146.1102282
+#> [16,]   -7.9495626  -13.1711402  20.1620016  32.1240324  34.2024895  10.3023640
+#> [17,]   77.2260167   86.3128305  76.0691914  81.0085017  99.5705861 145.9411473
+#> [18,]   15.8341860   21.3140279  33.0870227  42.8214335  45.8964765  33.1931354
+#> [19,]   18.7134443   24.2671827  24.5037172  27.0921147  30.0018912  43.7139432
+#> [20,]    2.4071631    4.3137979   5.5552771   6.4911688   5.7190028   4.0726763
+#> [21,]    0.3523143    0.6684041   0.8605413   0.9424119   0.7500321   0.7860324
+#>              2010        2015        2020        2025        2030        2035
+#>  [1,]  96.8489527   81.787724 108.4964018  72.7929143  75.1377635  78.2999917
+#>  [2,]  10.1456148    6.861041 -64.5147999 -41.3212737 -42.6378073 -44.4263380
+#>  [3,]  72.0885342   50.154505  96.0526852  67.0220607  69.1777814  72.0812494
+#>  [4,]  46.8365412  137.666319  48.8726280  68.9452677  71.2172872  74.2579199
+#>  [5,] 169.9719435  164.268004 202.0142957 167.6757076 173.0972445 180.3776182
+#>  [6,]  53.8651176    1.674869  36.2921853  51.5879497  53.2730104  55.5402431
+#>  [7,] 152.8004253  198.451595 143.1858115 108.2401113 111.7463469 116.4513446
+#>  [8,]   9.0499008  -63.722429 -13.1662853  -1.3229319  -1.3567847  -1.4166120
+#>  [9,] 109.2538323  165.752322 105.1968799  70.3049707  72.5687612  75.6518060
+#> [10,]   8.0918478 -111.804908 -34.4986470 -25.8589758 -26.7076872 -27.8794694
+#> [11,]  70.6184057  181.907767  92.1907107  53.6822324  55.4537780  57.8631744
+#> [12,]  11.2247424  -82.427027 -24.5262744 -35.1714449 -36.3953427 -38.0578218
+#> [13,]  79.5974807  153.293244 100.4054475  45.7645298  47.3851820  49.5739605
+#> [14,]  16.0694181  -35.018895   2.4904511 -37.1421185 -38.6125234 -40.5402789
+#> [15,]  77.3204513  142.125455 122.5674937  39.8336501  41.5180415  43.7294736
+#> [16,]  64.9764201   17.669656  43.2746269 -33.6428689 -35.3367518 -37.5389842
+#> [17,] 128.3596517  138.115219 126.1406102  30.7027237  32.5078219  34.8617924
+#> [18,]  72.5951224   75.405603  75.9497393 -22.9890882 -24.7183961 -26.9616805
+#> [19,]  41.4996635   58.915419  60.6511414  13.2008917  14.4778586  16.1584514
+#> [20,]   9.2686019    7.492899  10.2366742  -4.6738266  -5.2221854  -5.9617379
+#> [21,]   0.5589578    0.851188   0.2535397   0.7421335   0.8358793   0.9663258
+#>             2040       2045       2050
+#>  [1,]  78.301291  78.438885  78.632681
+#>  [2,] -44.439072 -44.540444 -44.654494
+#>  [3,]  72.097267  72.236517  72.418842
+#>  [4,]  74.190986  74.283958  74.427831
+#>  [5,] 180.379735 180.672096 181.123362
+#>  [6,]  55.469495  55.523010  55.613918
+#>  [7,] 116.491593 116.695956 116.994920
+#>  [8,]  -1.508978  -1.546569  -1.582514
+#>  [9,]  75.729792  75.873104  76.071716
+#> [10,] -27.992601 -28.100488 -28.203044
+#> [11,]  57.986739  58.142769  58.345844
+#> [12,] -38.226599 -38.401601 -38.608806
+#> [13,]  49.797401  50.026525  50.314056
+#> [14,] -40.876810 -41.187768 -41.540384
+#> [15,]  44.186892  44.619462  45.098744
+#> [16,] -38.197735 -38.810642 -39.481595
+#> [17,]  35.743188  36.582545  37.499323
+#> [18,] -28.026998 -29.079131 -30.223302
+#> [19,]  17.126684  18.119018  19.185104
+#> [20,]  -6.442425  -6.947161  -7.507029
+#> [21,]   1.053518   1.148182   1.254126
                                                              
+
                                                              +
                                                              + +
                                                              + + +
                                                              +
                                                              +

                                                              Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                                              +
                                                              + +
                                                              +

                                                              Site built with pkgdown 1.6.1.

                                                              +
                                                              + +
                                                              +
                                                              + + + + + + + + diff --git a/docs/reference/mig_un_fam.html b/docs/reference/mig_un_fam.html new file mode 100644 index 000000000..d9f7ee18c --- /dev/null +++ b/docs/reference/mig_un_fam.html @@ -0,0 +1,241 @@ + + + + + + + + +Net migration by age for an UN family — mig_un_fam • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                              +
                                                              + + + + +
                                                              + +
                                                              +
                                                              +
                                                              +

                                                              Net migration by age for an UN family

                                                              + Source: R/mig_un_fam.R + +
                                                              + +
                                                              +

                                                              Given a total net migration, +calculate the net migration age schedule based on the Rogers and Castro formula for UN families.

                                                              +
                                                              + + 
                                                              mig_un_fam(NM, family, Single = TRUE, OAnew = 100)
                                                              + +

                                                              Arguments

                                                              + + + + + + + + + + + + + + + + + + +
                                                              NM

                                                              numeric. Total net migration to distribute between ages and sex.

                                                              family

                                                              character. Could be "Family", "Female Labor", "Male Labor".

                                                              Single

                                                              logical. Results by simple age. Default FALSE. +Typically from pre-working age and working age parts of in Roger-Castro formula.

                                                              OAnew

                                                              The age from which to group all ages into an open ended age group. +By default it is set to 100, so it groups all ages up to 120, which is the +maximum age.

                                                              + +

                                                              Value

                                                              + +

                                                              List with

                                                                +

                                                              • params_RC data.frame. Roger-Castro parameters in a data.frame. Same as mig_un_params data.

                                                                • +

                                                              • net_migr data.frame. Net migrants by age and sex for the chosen family.

                                                                • +
                                                              + + +

                                                              Examples

                                                              + 
                                                              # 10000 net migrants, comparing two possible families
+nm1 <- mig_un_fam(NM = 10000, family = "Male Labor", OAnew = 100)
+nm2 <- mig_un_fam(NM = 10000, family = "Family", OAnew = 100)
+# See the female profile in for these models:
+if (FALSE) {
+plot(nm1$net_migr$age[nm1$net_migr$sex=="Female"],
+     nm1$net_migr$nm[nm1$net_migr$sex=="Female"],
+     xlab="Age",ylab="nm",ylim=c(0,300))
+points(nm2$net_migr$age[nm2$net_migr$sex=="Female"],
+       nm2$net_migr$nm[nm2$net_migr$sex=="Female"], col=2)
+}
+
                                                              +
                                                              + +
                                                              + + +
                                                              +
                                                              +

                                                              Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                                              +
                                                              + +
                                                              +

                                                              Site built with pkgdown 1.6.1.

                                                              +
                                                              + +
                                                              +
                                                              + + + + + + + + diff --git a/docs/reference/mig_un_families.html b/docs/reference/mig_un_families.html new file mode 100644 index 000000000..96361ffec --- /dev/null +++ b/docs/reference/mig_un_families.html @@ -0,0 +1,210 @@ + + + + + + + + +Proportion of net migrants by age and sex for considered migration profiles — mig_un_families • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                              +
                                                              + + + + +
                                                              + +
                                                              +
                                                              +
                                                              +

                                                              Proportion of net migrants by age and sex for considered migration profiles

                                                              + Source: R/data.R + +
                                                              + +
                                                              +

                                                              Roger-Castro estimated proportion of total net migrants using parameters from mig_un_params data.

                                                              +
                                                              + + 
                                                              mig_un_families
                                                              + + +

                                                              Format

                                                              + +

                                                              A data frame with:

                                                              +
                                                              family

                                                              Types Family, Male Labor or Female Labor.

                                                              +
                                                              sex

                                                              Male and Female.

                                                              +
                                                              mig_sign

                                                              Inmigration or Emigration.

                                                              +
                                                              age

                                                              Simple ages from 0 to 80 (OAG).

                                                              +
                                                              prop

                                                              Proportion of net migrants due to that sex and age.

                                                              + +
                                                              + +

                                                              Source

                                                              + +

                                                              UN spreadsheet "UNPD_Migration Age Profiles.xlsx"

                                                              + +
                                                              + +
                                                              + + +
                                                              +
                                                              +

                                                              Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                                              +
                                                              + +
                                                              +

                                                              Site built with pkgdown 1.6.1.

                                                              +
                                                              + +
                                                              +
                                                              + + + + + + + + diff --git a/docs/reference/mig_un_params.html b/docs/reference/mig_un_params.html new file mode 100644 index 000000000..c76b7693c --- /dev/null +++ b/docs/reference/mig_un_params.html @@ -0,0 +1,210 @@ + + + + + + + + +Parameters for considered migration profiles — mig_un_params • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                              +
                                                              + + + + +
                                                              + +
                                                              +
                                                              +
                                                              +

                                                              Parameters for considered migration profiles

                                                              + Source: R/data.R + +
                                                              + +
                                                              +

                                                              Roger-Castro estimated parameters using mig_estimate_rc for Pre Working Age and Working Age profiles of migration.

                                                              +
                                                              + + 
                                                              mig_un_params
                                                              + + +

                                                              Format

                                                              + +

                                                              A data frame with:

                                                              +
                                                              family

                                                              Types Family, Male Labor or Female Labor.

                                                              +
                                                              sex

                                                              Male and Female.

                                                              +
                                                              mig_sign

                                                              Inmigration or Emigration.

                                                              +
                                                              param

                                                              Parameters from Roger-Castro.

                                                              +
                                                              median

                                                              median of posterior distribution using Monte Carlo Markov Chains in mig_estimate_rc.

                                                              + +
                                                              + +

                                                              Source

                                                              + +

                                                              UN spreadsheet "UNPD_Migration Age Profiles.xlsx"

                                                              + +
                                                              + +
                                                              + + +
                                                              +
                                                              +

                                                              Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                                              +
                                                              + +
                                                              +

                                                              Site built with pkgdown 1.6.1.

                                                              +
                                                              + +
                                                              +
                                                              + + + + + + + + diff --git a/docs/reference/names2age.html b/docs/reference/names2age.html index fd473ecf9..9a91a8ab9 100644 --- a/docs/reference/names2age.html +++ b/docs/reference/names2age.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                                              • - +
                                                                • @@ -129,13 +129,13 @@
                                                                • - +
                                                                • - + github diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html index 2f3c88adc..2ac6251ac 100644 --- a/docs/reference/pipe.html +++ b/docs/reference/pipe.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                                                  • - +
                                                                    • @@ -129,13 +129,13 @@
                                                                    • - +
                                                                    • - + github @@ -164,7 +164,22 @@

                                                                      Pipe operator

                                                                      lhs %>% rhs
                                                                      - +

                                                                      Arguments

                                                                      + + + + + + + + + + +
                                                                      lhs

                                                                      A value or the magrittr placeholder.

                                                                      rhs

                                                                      A function call using the magrittr semantics.

                                                                      + +

                                                                      Value

                                                                      + +

                                                                      The result of calling rhs(lhs).

                                                                      @@ -89,7 +89,7 @@
                                                                      • - +
                                                                        • @@ -130,13 +130,13 @@
                                                                        • - +
                                                                        • - + github diff --git a/docs/reference/pop1m_ind.html b/docs/reference/pop1m_ind.html index 8b86cc9fe..fcd0c4a30 100644 --- a/docs/reference/pop1m_ind.html +++ b/docs/reference/pop1m_ind.html @@ -6,7 +6,7 @@ -Indian male population 1991 — pop1m_ind • DemoTools +Indian male population 1971 — pop1m_ind • DemoTools @@ -46,8 +46,8 @@ - - + + @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                                                          • - +
                                                                            • @@ -129,13 +129,13 @@
                                                                            • - +
                                                                            • - + github @@ -153,13 +153,13 @@
                                                                              -

                                                                              Indian male population 1991

                                                                              +

                                                                              Indian male population 1971

                                                                              Source: R/data.R
                                                                              -

                                                                              Indian male population 1991

                                                                              +

                                                                              Indian male population 1971

                                                                              pop1m_ind
                                                                              diff --git a/docs/reference/pop1m_pasex.html b/docs/reference/pop1m_pasex.html index 75ce66667..db26b135e 100644 --- a/docs/reference/pop1m_pasex.html +++ b/docs/reference/pop1m_pasex.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                                                                              @@ -88,7 +88,7 @@
                                                                              • - +
                                                                                • @@ -129,13 +129,13 @@
                                                                                • - +
                                                                                • - + github diff --git a/docs/reference/pop1m_rus2002.html b/docs/reference/pop1m_rus2002.html new file mode 100644 index 000000000..649c8995a --- /dev/null +++ b/docs/reference/pop1m_rus2002.html @@ -0,0 +1,203 @@ + + + + + + + + +Russian census 2002 male population by 1 year age groups — pop1m_rus2002 • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                                  +
                                                                                  + + + + +
                                                                                  + +
                                                                                  +
                                                                                  +
                                                                                  +

                                                                                  Russian census 2002 male population by 1 year age groups

                                                                                  + Source: R/data.R + +
                                                                                  + +
                                                                                  +

                                                                                  Male population by 1 year age groups from Russian census help on 2002-10-16

                                                                                  +
                                                                                  + + 
                                                                                  pop1m_rus2002
                                                                                  + + +

                                                                                  Format

                                                                                  + +

                                                                                  A numeric vector of length 101

                                                                                  +

                                                                                  Source

                                                                                  + +

                                                                                  The data comes from +http://www.demoscope.ru/weekly/ssp/rus2002_01.php

                                                                                  + +
                                                                                  + +
                                                                                  + + +
                                                                                  +
                                                                                  +

                                                                                  Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                                                                  +
                                                                                  + +
                                                                                  +

                                                                                  Site built with pkgdown 1.6.1.

                                                                                  +
                                                                                  + +
                                                                                  +
                                                                                  + + + + + + + + diff --git a/docs/reference/pop1m_rus2010.html b/docs/reference/pop1m_rus2010.html new file mode 100644 index 000000000..b7d0f4c56 --- /dev/null +++ b/docs/reference/pop1m_rus2010.html @@ -0,0 +1,203 @@ + + + + + + + + +Russian census 2010 male population by 1 year age groups — pop1m_rus2010 • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                                  +
                                                                                  + + + + +
                                                                                  + +
                                                                                  +
                                                                                  +
                                                                                  +

                                                                                  Russian census 2010 male population by 1 year age groups

                                                                                  + Source: R/data.R + +
                                                                                  + +
                                                                                  +

                                                                                  Male population by 1 year age groups from Russian census help on 2010-10-25

                                                                                  +
                                                                                  + + 
                                                                                  pop1m_rus2010
                                                                                  + + +

                                                                                  Format

                                                                                  + +

                                                                                  A numeric vector of length 101

                                                                                  +

                                                                                  Source

                                                                                  + +

                                                                                  The data comes from +http://www.demoscope.ru/weekly/ssp/rus_age1_10.php

                                                                                  + +
                                                                                  + +
                                                                                  + + +
                                                                                  +
                                                                                  +

                                                                                  Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                                                                  +
                                                                                  + +
                                                                                  +

                                                                                  Site built with pkgdown 1.6.1.

                                                                                  +
                                                                                  + +
                                                                                  +
                                                                                  + + + + + + + + diff --git a/docs/reference/pop5_mat.html b/docs/reference/pop5_mat.html index a097300ad..898873582 100644 --- a/docs/reference/pop5_mat.html +++ b/docs/reference/pop5_mat.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76
                                                                              @@ -88,7 +88,7 @@
                                                                              • - +
                                                                                • @@ -129,13 +129,13 @@
                                                                                • - +
                                                                                • - + github diff --git a/docs/reference/pop5m_pasex.html b/docs/reference/pop5m_pasex.html index f7a98caf8..fa4aa2cb3 100644 --- a/docs/reference/pop5m_pasex.html +++ b/docs/reference/pop5m_pasex.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                                                                  • - +
                                                                                    • @@ -129,13 +129,13 @@
                                                                                    • - +
                                                                                    • - + github diff --git a/docs/reference/popA_earlier.html b/docs/reference/popA_earlier.html index eb2a6bbe0..76847c901 100644 --- a/docs/reference/popA_earlier.html +++ b/docs/reference/popA_earlier.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                                                                      • - +
                                                                                        • @@ -129,13 +129,13 @@
                                                                                        • - +
                                                                                        • - + github diff --git a/docs/reference/popA_later.html b/docs/reference/popA_later.html index 278f4874d..e51abc373 100644 --- a/docs/reference/popA_later.html +++ b/docs/reference/popA_later.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                                                                          • - +
                                                                                            • @@ -129,13 +129,13 @@
                                                                                            • - +
                                                                                            • - + github diff --git a/docs/reference/pop_f_mat_five.html b/docs/reference/pop_f_mat_five.html new file mode 100644 index 000000000..b8c3375ff --- /dev/null +++ b/docs/reference/pop_f_mat_five.html @@ -0,0 +1,204 @@ + + + + + + + + +Population matrix for females five year age groups between 1950 and 2050 — pop_f_mat_five • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                                              +
                                                                                              + + + + +
                                                                                              + +
                                                                                              +
                                                                                              +
                                                                                              +

                                                                                              Population matrix for females five year age groups between 1950 and 2050

                                                                                              + Source: R/data.R + +
                                                                                              + +
                                                                                              +

                                                                                              Population matrix for females five year age groups between 1950 and 2050 for +unknown country

                                                                                              +
                                                                                              + + 
                                                                                              pop_f_mat_five
                                                                                              + + +

                                                                                              Format

                                                                                              + +

                                                                                              A matrix of dimensions 21 x 21

                                                                                              +

                                                                                              Source

                                                                                              + +

                                                                                              Migration residual PAS spreadsheet

                                                                                              + +
                                                                                              + +
                                                                                              + + +
                                                                                              +
                                                                                              +

                                                                                              Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                                                                              +
                                                                                              + +
                                                                                              +

                                                                                              Site built with pkgdown 1.6.1.

                                                                                              +
                                                                                              + +
                                                                                              +
                                                                                              + + + + + + + + diff --git a/docs/reference/pop_f_mat_single.html b/docs/reference/pop_f_mat_single.html new file mode 100644 index 000000000..f2bdc58c0 --- /dev/null +++ b/docs/reference/pop_f_mat_single.html @@ -0,0 +1,204 @@ + + + + + + + + +Population matrix for females single ages between 1999 and 2019 — pop_f_mat_single • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                                              +
                                                                                              + + + + +
                                                                                              + +
                                                                                              +
                                                                                              +
                                                                                              +

                                                                                              Population matrix for females single ages between 1999 and 2019

                                                                                              + Source: R/data.R + +
                                                                                              + +
                                                                                              +

                                                                                              Population matrix for females single ages between 1999 and 2019 for +Sweden

                                                                                              +
                                                                                              + + 
                                                                                              pop_f_mat_single
                                                                                              + + +

                                                                                              Format

                                                                                              + +

                                                                                              A matrix of dimensions 101 x 21

                                                                                              +

                                                                                              Source

                                                                                              + +

                                                                                              Migration residual PAS spreadsheet

                                                                                              + +
                                                                                              + +
                                                                                              + + +
                                                                                              +
                                                                                              +

                                                                                              Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                                                                              +
                                                                                              + +
                                                                                              +

                                                                                              Site built with pkgdown 1.6.1.

                                                                                              +
                                                                                              + +
                                                                                              +
                                                                                              + + + + + + + + diff --git a/docs/reference/pop_m_mat_five.html b/docs/reference/pop_m_mat_five.html new file mode 100644 index 000000000..17a314068 --- /dev/null +++ b/docs/reference/pop_m_mat_five.html @@ -0,0 +1,204 @@ + + + + + + + + +Population matrix for males five year age groups between 1950 and 2050 — pop_m_mat_five • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                                              +
                                                                                              + + + + +
                                                                                              + +
                                                                                              +
                                                                                              +
                                                                                              +

                                                                                              Population matrix for males five year age groups between 1950 and 2050

                                                                                              + Source: R/data.R + +
                                                                                              + +
                                                                                              +

                                                                                              Population matrix for males five year age groups between 1950 and 2050 for +unknown country

                                                                                              +
                                                                                              + + 
                                                                                              pop_m_mat_five
                                                                                              + + +

                                                                                              Format

                                                                                              + +

                                                                                              A matrix of dimensions 21 x 21

                                                                                              +

                                                                                              Source

                                                                                              + +

                                                                                              Migration residual PAS spreadsheet

                                                                                              + +
                                                                                              + +
                                                                                              + + +
                                                                                              +
                                                                                              +

                                                                                              Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                                                                              +
                                                                                              + +
                                                                                              +

                                                                                              Site built with pkgdown 1.6.1.

                                                                                              +
                                                                                              + +
                                                                                              +
                                                                                              + + + + + + + + diff --git a/docs/reference/pop_m_mat_single.html b/docs/reference/pop_m_mat_single.html new file mode 100644 index 000000000..1a6bed285 --- /dev/null +++ b/docs/reference/pop_m_mat_single.html @@ -0,0 +1,204 @@ + + + + + + + + +Population matrix for males single ages between 1999 and 2019 — pop_m_mat_single • DemoTools + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
                                                                                              +
                                                                                              + + + + +
                                                                                              + +
                                                                                              +
                                                                                              +
                                                                                              +

                                                                                              Population matrix for males single ages between 1999 and 2019

                                                                                              + Source: R/data.R + +
                                                                                              + +
                                                                                              +

                                                                                              Population matrix for males single ages between 1999 and 2019 for +Sweden

                                                                                              +
                                                                                              + + 
                                                                                              pop_m_mat_single
                                                                                              + + +

                                                                                              Format

                                                                                              + +

                                                                                              A matrix of dimensions 101 x 21

                                                                                              +

                                                                                              Source

                                                                                              + +

                                                                                              Migration residual PAS spreadsheet

                                                                                              + +
                                                                                              + +
                                                                                              + + +
                                                                                              +
                                                                                              +

                                                                                              Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.

                                                                                              +
                                                                                              + +
                                                                                              +

                                                                                              Site built with pkgdown 1.6.1.

                                                                                              +
                                                                                              + +
                                                                                              +
                                                                                              + + + + + + + + diff --git a/docs/reference/ratx.html b/docs/reference/ratx.html index 981151875..df54389a4 100644 --- a/docs/reference/ratx.html +++ b/docs/reference/ratx.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                                                                              • - +
                                                                                                • @@ -129,13 +129,13 @@
                                                                                                • - +
                                                                                                • - + github diff --git a/docs/reference/rescaleAgeGroups.html b/docs/reference/rescaleAgeGroups.html index 26b51ef65..a2808d915 100644 --- a/docs/reference/rescaleAgeGroups.html +++ b/docs/reference/rescaleAgeGroups.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                                                                                  • - +
                                                                                                    • @@ -129,13 +129,13 @@
                                                                                                    • - +
                                                                                                    • - + github diff --git a/docs/reference/rescale_vector.html b/docs/reference/rescale_vector.html index c7d761a38..fa85ebafd 100644 --- a/docs/reference/rescale_vector.html +++ b/docs/reference/rescale_vector.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                                                                                      • - +
                                                                                                        • @@ -129,13 +129,13 @@
                                                                                                        • - +
                                                                                                        • - + github diff --git a/docs/reference/rlog.html b/docs/reference/rlog.html index 2849e0217..e9f465e03 100644 --- a/docs/reference/rlog.html +++ b/docs/reference/rlog.html @@ -82,7 +82,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -90,7 +90,7 @@
                                                                                                          • - +
                                                                                                            • @@ -131,13 +131,13 @@
                                                                                                            • - +
                                                                                                            • - + github diff --git a/docs/reference/sexRatioScore.html b/docs/reference/sexRatioScore.html index ceeb299ac..b57406000 100644 --- a/docs/reference/sexRatioScore.html +++ b/docs/reference/sexRatioScore.html @@ -83,7 +83,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -91,7 +91,7 @@
                                                                                                              • - +
                                                                                                                • @@ -132,13 +132,13 @@
                                                                                                                • - +
                                                                                                                • - + github @@ -214,7 +214,7 @@

                                                                                                                  R United States Census Bureau (2017). “Population Analysis System (PAS) Software.” -https://www.census.gov/data/software/pas.html.

                                                                                                                  +https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.

                                                                                                                  Examples

                                                                                                                  Males   <- c(4677000,4135000,3825000,3647000,3247000,2802000,2409000,2212000,
diff --git a/docs/reference/shift.vector.html b/docs/reference/shift.vector.html
index 55195f211..4812973be 100644
--- a/docs/reference/shift.vector.html
+++ b/docs/reference/shift.vector.html
@@ -82,7 +82,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
                                                                                                                  
 
@@ -90,7 +90,7 @@
       
                                                                                                                    
         
                                                                                                                  • 
   
-    
+    
      
   
 
                                                                                                                    • 
@@ -131,13 +131,13 @@
       
                                                                                                                      
         
                                                                                                                    • 
   
-    
+    
      
   
 
                                                                                                                      • 
 
                                                                                                                    • 
   
-    
+    
      
     github
   
diff --git a/docs/reference/shift_census_ages_to_cohorts.html b/docs/reference/shift_census_ages_to_cohorts.html
new file mode 100644
index 000000000..3d4ba4b9f
--- /dev/null
+++ b/docs/reference/shift_census_ages_to_cohorts.html
@@ -0,0 +1,382 @@
+
+
+
+  
+  
+
+
+
+shift census populations to match single year cohorts — shift_census_ages_to_cohorts • DemoTools
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
                                                                                                                      
+      
                                                                                                                      
+      
+
+      
+
+      
                                                                                                                      
+
+
                                                                                                                      
+  
                                                                                                                      
+    
                                                                                                                      
+    
                                                                                                                      shift census populations to match single year cohorts
                                                                                                                      
+    Source: R/interp_coh.R
+    
+    
                                                                                                                      
+
+    
                                                                                                                      
+    
                                                                                                                      Matches the (single) ages of a census to single cohorts. For use in intercensal interpolations. Ages are potentially blended to match single cohort line assuming that the population in each age is uniformly distributed over the age group.
                                                                                                                      
+    
                                                                                                                      
+
+    
                                                                                                                      shift_census_ages_to_cohorts(
+  pop,
+  age,
+  date,
+  censusYearOpt = "frac",
+  OAG = TRUE
+)
                                                                                                                      
+
+    
                                                                                                                      Arguments
                                                                                                                      
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
                                                                                                                      pop
                                                                                                                      numeric vector. Population counts in age groups, presumably from a census with an exact reference date.
                                                                                                                      age
                                                                                                                      integer vector. Lower bound of single age groups
                                                                                                                      date
                                                                                                                      Either a Date class object or an unambiguous character string in the format "YYYY-MM-DD".
                                                                                                                      censusYearOpt
                                                                                                                      character or NA. Options include:
                                                                                                                        
+
                                                                                                                      • "frac" keep the partial cohort observed in the year of the census.
                                                                                                                        • 
+
                                                                                                                      • "drop" remove the partial cohort from the census year (and trim other outputs to match)
                                                                                                                        • 
+
                                                                                                                      • "extrap" inflate the partial cohort from the census year. Specifically we keep it the same as the input age 0.
                                                                                                                        • 
+
                                                                                                                      • NA return NA for the census year cohort size.
                                                                                                                        • 
+
                                                                                                                      OAG
                                                                                                                      logical. Is the highest age group an open age? If TRUE
                                                                                                                      
+
+
+    
                                                                                                                      Examples
                                                                                                                      
+    
                                                                                                                      pop <- seq(10000,100,length.out = 101)
+age <- 0:100
+d1 <- "2020-01-01"
+d2 <- "2020-07-01"
+d3 <- "2020-12-21"
+
+shift_census_ages_to_cohorts(pop, age, d1)
+
                                                                                                                      #> $cohort_size
+#>   [1] 10000  9901  9802  9703  9604  9505  9406  9307  9208  9109  9010  8911
+#>  [13]  8812  8713  8614  8515  8416  8317  8218  8119  8020  7921  7822  7723
+#>  [25]  7624  7525  7426  7327  7228  7129  7030  6931  6832  6733  6634  6535
+#>  [37]  6436  6337  6238  6139  6040  5941  5842  5743  5644  5545  5446  5347
+#>  [49]  5248  5149  5050  4951  4852  4753  4654  4555  4456  4357  4258  4159
+#>  [61]  4060  3961  3862  3763  3664  3565  3466  3367  3268  3169  3070  2971
+#>  [73]  2872  2773  2674  2575  2476  2377  2278  2179  2080  1981  1882  1783
+#>  [85]  1684  1585  1486  1387  1288  1189  1090   991   892   793   694   595
+#>  [97]   496   397   298   199
+#> 
+#> $birth_year
+#>   [1] 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005
+#>  [16] 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990
+#>  [31] 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976 1975
+#>  [46] 1974 1973 1972 1971 1970 1969 1968 1967 1966 1965 1964 1963 1962 1961 1960
+#>  [61] 1959 1958 1957 1956 1955 1954 1953 1952 1951 1950 1949 1948 1947 1946 1945
+#>  [76] 1944 1943 1942 1941 1940 1939 1938 1937 1936 1935 1934 1933 1932 1931 1930
+#>  [91] 1929 1928 1927 1926 1925 1924 1923 1922 1921 1920
+#> 
+#> $age
+#>   [1]  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
+#>  [26] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
+#>  [51] 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
+#>  [76] 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
+#> 
+#> $date
+#> [1] 2020
+#> 
+#> $f1
+#> [1] 0
+#> 
                                                                                                                      shift_census_ages_to_cohorts(pop, age, d2)
+
                                                                                                                      #> $cohort_size
+#>   [1] 4972.6776 9950.7705 9851.7705 9752.7705 9653.7705 9554.7705 9455.7705
+#>   [8] 9356.7705 9257.7705 9158.7705 9059.7705 8960.7705 8861.7705 8762.7705
+#>  [15] 8663.7705 8564.7705 8465.7705 8366.7705 8267.7705 8168.7705 8069.7705
+#>  [22] 7970.7705 7871.7705 7772.7705 7673.7705 7574.7705 7475.7705 7376.7705
+#>  [29] 7277.7705 7178.7705 7079.7705 6980.7705 6881.7705 6782.7705 6683.7705
+#>  [36] 6584.7705 6485.7705 6386.7705 6287.7705 6188.7705 6089.7705 5990.7705
+#>  [43] 5891.7705 5792.7705 5693.7705 5594.7705 5495.7705 5396.7705 5297.7705
+#>  [50] 5198.7705 5099.7705 5000.7705 4901.7705 4802.7705 4703.7705 4604.7705
+#>  [57] 4505.7705 4406.7705 4307.7705 4208.7705 4109.7705 4010.7705 3911.7705
+#>  [64] 3812.7705 3713.7705 3614.7705 3515.7705 3416.7705 3317.7705 3218.7705
+#>  [71] 3119.7705 3020.7705 2921.7705 2822.7705 2723.7705 2624.7705 2525.7705
+#>  [78] 2426.7705 2327.7705 2228.7705 2129.7705 2030.7705 1931.7705 1832.7705
+#>  [85] 1733.7705 1634.7705 1535.7705 1436.7705 1337.7705 1238.7705 1139.7705
+#>  [92] 1040.7705  941.7705  842.7705  743.7705  644.7705  545.7705  446.7705
+#>  [99]  347.7705  248.7705
+#> 
+#> $birth_year
+#>   [1] 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006
+#>  [16] 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991
+#>  [31] 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976
+#>  [46] 1975 1974 1973 1972 1971 1970 1969 1968 1967 1966 1965 1964 1963 1962 1961
+#>  [61] 1960 1959 1958 1957 1956 1955 1954 1953 1952 1951 1950 1949 1948 1947 1946
+#>  [76] 1945 1944 1943 1942 1941 1940 1939 1938 1937 1936 1935 1934 1933 1932 1931
+#>  [91] 1930 1929 1928 1927 1926 1925 1924 1923 1922 1921
+#> 
+#> $age
+#>   [1]  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
+#>  [26] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
+#>  [51] 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
+#>  [76] 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
+#> 
+#> $date
+#> [1] 2020.497
+#> 
+#> $f1
+#> [1] 0.4972678
+#> 
                                                                                                                      shift_census_ages_to_cohorts(pop, age, d3)
+
                                                                                                                      #> $cohort_size
+#>   [1] 9699.4536 9903.9754 9804.9754 9705.9754 9606.9754 9507.9754 9408.9754
+#>   [8] 9309.9754 9210.9754 9111.9754 9012.9754 8913.9754 8814.9754 8715.9754
+#>  [15] 8616.9754 8517.9754 8418.9754 8319.9754 8220.9754 8121.9754 8022.9754
+#>  [22] 7923.9754 7824.9754 7725.9754 7626.9754 7527.9754 7428.9754 7329.9754
+#>  [29] 7230.9754 7131.9754 7032.9754 6933.9754 6834.9754 6735.9754 6636.9754
+#>  [36] 6537.9754 6438.9754 6339.9754 6240.9754 6141.9754 6042.9754 5943.9754
+#>  [43] 5844.9754 5745.9754 5646.9754 5547.9754 5448.9754 5349.9754 5250.9754
+#>  [50] 5151.9754 5052.9754 4953.9754 4854.9754 4755.9754 4656.9754 4557.9754
+#>  [57] 4458.9754 4359.9754 4260.9754 4161.9754 4062.9754 3963.9754 3864.9754
+#>  [64] 3765.9754 3666.9754 3567.9754 3468.9754 3369.9754 3270.9754 3171.9754
+#>  [71] 3072.9754 2973.9754 2874.9754 2775.9754 2676.9754 2577.9754 2478.9754
+#>  [78] 2379.9754 2280.9754 2181.9754 2082.9754 1983.9754 1884.9754 1785.9754
+#>  [85] 1686.9754 1587.9754 1488.9754 1389.9754 1290.9754 1191.9754 1092.9754
+#>  [92]  993.9754  894.9754  795.9754  696.9754  597.9754  498.9754  399.9754
+#>  [99]  300.9754  201.9754
+#> 
+#> $birth_year
+#>   [1] 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006
+#>  [16] 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991
+#>  [31] 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976
+#>  [46] 1975 1974 1973 1972 1971 1970 1969 1968 1967 1966 1965 1964 1963 1962 1961
+#>  [61] 1960 1959 1958 1957 1956 1955 1954 1953 1952 1951 1950 1949 1948 1947 1946
+#>  [76] 1945 1944 1943 1942 1941 1940 1939 1938 1937 1936 1935 1934 1933 1932 1931
+#>  [91] 1930 1929 1928 1927 1926 1925 1924 1923 1922 1921
+#> 
+#> $age
+#>   [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
+#>  [19]  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
+#>  [37]  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
+#>  [55]  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72
+#>  [73]  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90
+#>  [91]  91  92  93  94  95  96  97  98  99 100
+#> 
+#> $date
+#> [1] 2020.97
+#> 
+#> $f1
+#> [1] 0.9699454
+#> 
                                                                                                                      shift_census_ages_to_cohorts(pop, age, 2020.5)
+
                                                                                                                      #> $cohort_size
+#>   [1] 5000.0 9950.5 9851.5 9752.5 9653.5 9554.5 9455.5 9356.5 9257.5 9158.5
+#>  [11] 9059.5 8960.5 8861.5 8762.5 8663.5 8564.5 8465.5 8366.5 8267.5 8168.5
+#>  [21] 8069.5 7970.5 7871.5 7772.5 7673.5 7574.5 7475.5 7376.5 7277.5 7178.5
+#>  [31] 7079.5 6980.5 6881.5 6782.5 6683.5 6584.5 6485.5 6386.5 6287.5 6188.5
+#>  [41] 6089.5 5990.5 5891.5 5792.5 5693.5 5594.5 5495.5 5396.5 5297.5 5198.5
+#>  [51] 5099.5 5000.5 4901.5 4802.5 4703.5 4604.5 4505.5 4406.5 4307.5 4208.5
+#>  [61] 4109.5 4010.5 3911.5 3812.5 3713.5 3614.5 3515.5 3416.5 3317.5 3218.5
+#>  [71] 3119.5 3020.5 2921.5 2822.5 2723.5 2624.5 2525.5 2426.5 2327.5 2228.5
+#>  [81] 2129.5 2030.5 1931.5 1832.5 1733.5 1634.5 1535.5 1436.5 1337.5 1238.5
+#>  [91] 1139.5 1040.5  941.5  842.5  743.5  644.5  545.5  446.5  347.5  248.5
+#> 
+#> $birth_year
+#>   [1] 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006
+#>  [16] 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991
+#>  [31] 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976
+#>  [46] 1975 1974 1973 1972 1971 1970 1969 1968 1967 1966 1965 1964 1963 1962 1961
+#>  [61] 1960 1959 1958 1957 1956 1955 1954 1953 1952 1951 1950 1949 1948 1947 1946
+#>  [76] 1945 1944 1943 1942 1941 1940 1939 1938 1937 1936 1935 1934 1933 1932 1931
+#>  [91] 1930 1929 1928 1927 1926 1925 1924 1923 1922 1921
+#> 
+#> $age
+#>   [1]  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
+#>  [26] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
+#>  [51] 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
+#>  [76] 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
+#> 
+#> $date
+#> [1] 2020.5
+#> 
+#> $f1
+#> [1] 0.5
+#> 
                                                                                                                      
+  
                                                                                                                      
+  
+
                                                                                                                      
+
+
+      
                                                                                                                      
+      
                                                                                                                      
+  
                                                                                                                      Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                                                      
+
                                                                                                                      
+
+
                                                                                                                      
+  
                                                                                                                      Site built with pkgdown 1.6.1.
                                                                                                                      
+
                                                                                                                      
+
+      
                                                                                                                      
+   
                                                                                                                      
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/simplify.text.html b/docs/reference/simplify.text.html
index d0b37e90b..4ee61e473 100644
--- a/docs/reference/simplify.text.html
+++ b/docs/reference/simplify.text.html
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diff --git a/docs/reference/single2abridged.html b/docs/reference/single2abridged.html
new file mode 100644
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--- /dev/null
+++ b/docs/reference/single2abridged.html
@@ -0,0 +1,383 @@
+
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+  
+  
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+
+Convert single age groups to five-year group abridged — single2abridged • DemoTools
+
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+      
                                                                                                                          
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+      
                                                                                                                          
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+  
                                                                                                                          
+    
                                                                                                                          
+    
                                                                                                                          Convert single age groups to five-year group abridged
                                                                                                                          
+    Source: R/utils.R
+    
+    
                                                                                                                          
+
+    
                                                                                                                          
+    
                                                                                                                          Convert single age groups to five-year group abridged
                                                                                                                          
+    
                                                                                                                          
+
+    
                                                                                                                          single2abridged(Age)
                                                                                                                          
+
+    
                                                                                                                          Arguments
                                                                                                                          
+    
+    
+    
+      
+      
+    
+    
                                                                                                                          Age
                                                                                                                          a numeric vector with counts for age groups. These are assumed to begin at zero.
                                                                                                                          
+
+    
                                                                                                                          Value
                                                                                                                          
+
+    
                                                                                                                          A numeric vector with five-year age groups abridged
                                                                                                                          
+
+    
                                                                                                                          Examples
                                                                                                                          
+    
                                                                                                                          pop_male_counts <-
+  c(
+    `0` = 11684,
+    `1` = 11473,
+    `2` = 11647,
+    `3` = 11939,
+    `4` = 11680,
+    `5` = 10600,
+    `6` = 11100,
+    `7` = 11157,
+    `8` = 11238,
+    `9` = 11544,
+    `10` = 7216,
+    `11` = 7407,
+    `12` = 7461,
+    `13` = 7656,
+    `14` = 7774,
+    `15` = 5709,
+    `16` = 5629,
+    `17` = 5745,
+    `18` = 6056,
+    `19` = 6259,
+    `20` = 5303,
+    `21` = 5423,
+    `22` = 5497,
+    `23` = 5547,
+    `24` = 5417,
+    `25` = 5441,
+    `26` = 5466,
+    `27` = 5500,
+    `28` = 5668,
+    `29` = 5694,
+    `30` = 4365,
+    `31` = 4252,
+    `32` = 4122,
+    `33` = 4142,
+    `34` = 4039,
+    `35` = 3210,
+    `36` = 3222,
+    `37` = 3258,
+    `38` = 3413,
+    `39` = 3871,
+    `40` = 2684,
+    `41` = 2844,
+    `42` = 3052,
+    `43` = 3182,
+    `44` = 3237,
+    `45` = 2263,
+    `46` = 2298,
+    `47` = 2318,
+    `48` = 2257,
+    `49` = 2194,
+    `50` = 2231,
+    `51` = 2172,
+    `52` = 2072,
+    `53` = 2008,
+    `54` = 1932,
+    `55` = 1301,
+    `56` = 1262,
+    `57` = 1213,
+    `58` = 1197,
+    `59` = 1191,
+    `60` = 1601,
+    `61` = 1593,
+    `62` = 1490,
+    `63` = 1348,
+    `64` = 1299,
+    `65` = 568,
+    `66` = 745,
+    `67` = 843,
+    `68` = 801,
+    `69` = 925,
+    `70` = 806,
+    `71` = 883,
+    `72` = 796,
+    `73` = 725,
+    `74` = 672,
+    `75` = 470,
+    `76` = 441,
+    `77` = 340,
+    `78` = 300,
+    `79` = 289,
+    `80` = 4200
+  )
+pop_female_counts <-
+  c(
+    `0` = 11673,
+    `1` = 11474,
+    `2` = 11670,
+    `3` = 11934,
+    `4` = 11614,
+    `5` = 10603,
+    `6` = 11144,
+    `7` = 11179,
+    `8` = 11269,
+    `9` = 11617,
+    `10` = 6772,
+    `11` = 6948,
+    `12` = 7030,
+    `13` = 7211,
+    `14` = 7306,
+    `15` = 6531,
+    `16` = 6443,
+    `17` = 6535,
+    `18` = 6951,
+    `19` = 7213,
+    `20` = 6096,
+    `21` = 6234,
+    `22` = 6327,
+    `23` = 6410,
+    `24` = 6285,
+    `25` = 6464,
+    `26` = 6492,
+    `27` = 6549,
+    `28` = 6739,
+    `29` = 6795,
+    `30` = 5013,
+    `31` = 4888,
+    `32` = 4735,
+    `33` = 4747,
+    `34` = 4646,
+    `35` = 3040,
+    `36` = 3068,
+    `37` = 3107,
+    `38` = 3246,
+    `39` = 3658,
+    `40` = 2650,
+    `41` = 2788,
+    `42` = 2977,
+    `43` = 3108,
+    `44` = 3156,
+    `45` = 1756,
+    `46` = 1784,
+    `47` = 1802,
+    `48` = 1764,
+    `49` = 1724,
+    `50` = 1982,
+    `51` = 1935,
+    `52` = 1846,
+    `53` = 1795,
+    `54` = 1731,
+    `55` = 863,
+    `56` = 850,
+    `57` = 825,
+    `58` = 819,
+    `59` = 816,
+    `60` = 1348,
+    `61` = 1342,
+    `62` = 1246,
+    `63` = 1138,
+    `64` = 1101,
+    `65` = 391,
+    `66` = 520,
+    `67` = 585,
+    `68` = 560,
+    `69` = 659,
+    `70` = 670,
+    `71` = 750,
+    `72` = 686,
+    `73` = 634,
+    `74` = 604,
+    `75` = 353,
+    `76` = 340,
+    `77` = 270,
+    `78` = 246,
+    `79` = 247,
+    `80` = 4143
+  )
+
+single2abridged(pop_male_counts)
+
                                                                                                                          #>     0     1     5    10    15    20    25    30    35    40    45    50    55 
+#> 11684 46739 55639 37514 29398 27187 27769 20920 16974 14999 11330 10415  6164 
+#>    60    65    70    75    80 
+#>  7331  3882  3882  1840  4200 
                                                                                                                          
+
                                                                                                                          
+  
                                                                                                                          
+  
+
                                                                                                                          
+
+
+      
                                                                                                                          
+      
                                                                                                                          
+  
                                                                                                                          Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                                                          
+
                                                                                                                          
+
+
                                                                                                                          
+  
                                                                                                                          Site built with pkgdown 1.6.1.
                                                                                                                          
+
                                                                                                                          
+
+      
                                                                                                                          
+   
                                                                                                                          
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/smooth_age_5.html b/docs/reference/smooth_age_5.html
index 00f9bcc2d..5ae13789c 100644
--- a/docs/reference/smooth_age_5.html
+++ b/docs/reference/smooth_age_5.html
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@@ -163,7 +163,7 @@ 
                                                                                                                              Smooth populations in 5-year age groups using various methods
                                                                                                                              
 
     
                                                                                                                              
     
                                                                                                                              Smooth population counts in 5-year age groups using the Carrier-Farrag,
-Karup-King-Newton, Arriaga, United Nations, Stong, or Zigzag methods. Allows for imputation
+Karup-King-Newton, Arriaga, United Nations, Strong, MAV or Zigzag methods. Allows for imputation
 of values in the youngest and oldest age groups for the Carrier-Farrag, Karup-King-Newton,
 and United Nations methods.
                                                                                                                              
     
                                                                                                                              
@@ -177,7 +177,7 @@ 
                                                                                                                              Smooth populations in 5-year age groups using various methods
                                                                                                                              
   ageMin = 10,
   ageMax = 65,
   n = 3,
-  young.tail = c("Original", "Arriaga", "Strong", NA),
+  young.tail = c("Original", "Arriaga", "Strong", "Cascade", NA),
   old.tail = young.tail
 )
                                                                                                                  @@ -194,7 +194,7 @@

                                                                                                                  Arg method -

                                                                                                                  character string. Options include "Carrier-Farrag","Arriaga","KKN","United Nations", "Strong", and "Zigzag". See details. Default "Carrier-Farrag".

                                                                                                                  +

                                                                                                                  character string. Options include "Carrier-Farrag","Arriaga","KKN","United Nations", "Strong", MAV and "Zigzag". See details. Default "Carrier-Farrag".

                                                                                                                  OAG @@ -234,8 +234,8 @@

                                                                                                                  Details operate based on 10-year age group totals, excluding the open age group.

                                                                                                                  The Carrier-Farrag, Karup-King-Newton, and United Nations methods do not produce estimates for the first and final 10-year age groups. By default, these are imputed with the original 5-year age group totals, but -you can also specify to impute with NA, or the results of the Arriaga or -Strong methods. If the terminal digit of the open age group is 5, then the terminal 10-year +you can also specify to impute with NA, or the results of the Arriaga, +Strong and Cascade methods. If the terminal digit of the open age group is 5, then the terminal 10-year age group shifts down, so imputations may affect more ages in this case. Imputation can follow different methods for young and old ages.

                                                                                                                  Method names are simplified using simplify.text and checked against a set of plausible matches diff --git a/docs/reference/smooth_age_5_arriaga.html b/docs/reference/smooth_age_5_arriaga.html index 5628bd1f5..b8e892870 100644 --- a/docs/reference/smooth_age_5_arriaga.html +++ b/docs/reference/smooth_age_5_arriaga.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@

                                                                                                                  • - +
                                                                                                                    • @@ -129,13 +129,13 @@
                                                                                                                    • - +
                                                                                                                    • - + github diff --git a/docs/reference/smooth_age_5_cf.html b/docs/reference/smooth_age_5_cf.html index 21887280c..ae2040950 100644 --- a/docs/reference/smooth_age_5_cf.html +++ b/docs/reference/smooth_age_5_cf.html @@ -80,7 +80,7 @@ DemoTools - 01.13.20 + 01.13.76 @@ -88,7 +88,7 @@
                                                                                                                      • - +
                                                                                                                        • @@ -129,13 +129,13 @@
                                                                                                                        • - +
                                                                                                                        • - + github @@ -199,7 +199,7 @@

                                                                                                                          R United States Census Bureau (2017). “Population Analysis System (PAS) Software.” -https://www.census.gov/data/software/pas.html.

                                                                                                                          +https://www.census.gov/data/software/pas.html, https://www.census.gov/data/software/pas.html.

                                                                                                                          Examples

                                                                                                                          # from PASEX AGESMTH
diff --git a/docs/reference/smooth_age_5_feeney.html b/docs/reference/smooth_age_5_feeney.html
index d9af090e1..31759f20c 100644
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diff --git a/docs/reference/smooth_age_5_mav.html b/docs/reference/smooth_age_5_mav.html
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                                                                                                                                      Smooth in 5-year age groups using a moving average
                                                                                                                                      
     
                                                                                                                                      Smooth data in 5-year age groups.
                                                                                                                                      
     
 
-    
                                                                                                                                      smooth_age_5_mav(Value, Age, OAG = TRUE, n = 3)
                                                                                                                                      
+    
                                                                                                                                      smooth_age_5_mav(Value, Age, OAG = TRUE, n = 3, tails = FALSE)
                                                                                                                                      
 
     
                                                                                                                                      Arguments
                                                                                                                                      
     
@@ -183,6 +183,11 @@ 
                                                                                                                                      Arg
       
                                                                                                                                      
+    
+      
+      
+    
                                                                                                                                      n
       
                                                                                                                                      integer. The width of the moving average. Default 3 intervals (x-5 to x+9).
                                                                                                                                      		
     
                                                                                                                                      tails
                                                                                                                                      logical. If tails is FALSE, both tails are left untouched.
+Otherwise, the tails are filled out using a cascade method.
                                                                                                                                      
     
                                                                                                                                      
 
     
                                                                                                                                      Value
                                                                                                                                      
@@ -191,7 +196,9 @@ 
                                                                                                                                      Value
                                                                                                                                      
     
                                                                                                                                      Details
                                                                                                                                      
 
     
                                                                                                                                      This function calls smooth_age_5_zigzag_inner(), but prepares data in a way consistent with other methods called by smooth_age_5(). It is probably preferable to call zigzag() from the top level, or else call this method from agesmth() for more control over tail imputations.
                                                                                                                                      
-
                                                                                                                                      This function calls mav(), which itself relies on the more general ma(). We lose the lowest and highest ages with this method, unless n=1, in which case data is returned in the original 5-year age groups. The total population count is not constrained to sum to the orignal total.
                                                                                                                                      
+
                                                                                                                                      If tails is set to FALSE, this function calls mav(), which itself relies on the more general ma(). We lose the lowest and highest ages with this method, unless n=1, in which case data is returned in the original 5-year age groups. The total population count is not constrained to sum to the original total.
                                                                                                                                      
+
                                                                                                                                      If tails is TRUE, the same results are expected but the tails are
+filled in using a cascading method.
                                                                                                                                      
 
     
                                                                                                                                      Examples
                                                                                                                                      
     
                                                                                                                                      Age <- c(0,1,seq(5,90,by=5))
diff --git a/docs/reference/smooth_age_5_strong.html b/docs/reference/smooth_age_5_strong.html
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--- a/docs/reference/smooth_age_5_zigzag_p.html
+++ b/docs/reference/smooth_age_5_zigzag_p.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
 
@@ -88,7 +88,7 @@
       
                                                                                                                                                            
         
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-    
+    
      
   
 
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-    
+    
      
     github
   
diff --git a/docs/reference/spencer.html b/docs/reference/spencer.html
index 77c2e4c9b..b2cefeef1 100644
--- a/docs/reference/spencer.html
+++ b/docs/reference/spencer.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
 
@@ -88,7 +88,7 @@
       
                                                                                                                                                                
         
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-    
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     github
   
diff --git a/docs/reference/splitOscillate.html b/docs/reference/splitOscillate.html
index 8424542ad..ed1d56bcf 100644
--- a/docs/reference/splitOscillate.html
+++ b/docs/reference/splitOscillate.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
 
@@ -88,7 +88,7 @@
       
                                                                                                                                                                    
         
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     github
   
diff --git a/docs/reference/sr_f_mat_five.html b/docs/reference/sr_f_mat_five.html
new file mode 100644
index 000000000..1e9a8ff84
--- /dev/null
+++ b/docs/reference/sr_f_mat_five.html
@@ -0,0 +1,204 @@
+
+
+
+  
+  
+
+
+
+Survival rates matrix for females five year age groups between 1950 and 2045 — sr_f_mat_five • DemoTools
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+  
+
+  
+    
                                                                                                                                                                      
+      
                                                                                                                                                                      
+      
+
+      
+
+      
                                                                                                                                                                      
+
+
                                                                                                                                                                      
+  
                                                                                                                                                                      
+    
                                                                                                                                                                      
+    
                                                                                                                                                                      Survival rates matrix for females five year age groups between 1950 and 2045
                                                                                                                                                                      
+    Source: R/data.R
+    
+    
                                                                                                                                                                      
+
+    
                                                                                                                                                                      
+    
                                                                                                                                                                      Survival rates matrix for females five year age groups between 1950 and 2045
+for unknown country
                                                                                                                                                                      
+    
                                                                                                                                                                      
+
+    
                                                                                                                                                                      sr_f_mat_five
                                                                                                                                                                      
+
+
+    
                                                                                                                                                                      Format
                                                                                                                                                                      
+
+    
                                                                                                                                                                      A matrix of dimensions 21 x 20
                                                                                                                                                                      
+    
                                                                                                                                                                      Source
                                                                                                                                                                      
+
+    
                                                                                                                                                                      Migration residual PAS spreadsheet
                                                                                                                                                                      
+
+  
                                                                                                                                                                      
+  
+
                                                                                                                                                                      
+
+
+      
                                                                                                                                                                      
+      
                                                                                                                                                                      
+  
                                                                                                                                                                      Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                                                                                                      
+
                                                                                                                                                                      
+
+
                                                                                                                                                                      
+  
                                                                                                                                                                      Site built with pkgdown 1.6.1.
                                                                                                                                                                      
+
                                                                                                                                                                      
+
+      
                                                                                                                                                                      
+   
                                                                                                                                                                      
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/sr_f_mat_single.html b/docs/reference/sr_f_mat_single.html
new file mode 100644
index 000000000..c4c9d6647
--- /dev/null
+++ b/docs/reference/sr_f_mat_single.html
@@ -0,0 +1,204 @@
+
+
+
+  
+  
+
+
+
+Survival rates matrix for females single ages between 1999 and 2019 — sr_f_mat_single • DemoTools
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+  
+
+  
+    
                                                                                                                                                                      
+      
                                                                                                                                                                      
+      
+
+      
+
+      
                                                                                                                                                                      
+
+
                                                                                                                                                                      
+  
                                                                                                                                                                      
+    
                                                                                                                                                                      
+    
                                                                                                                                                                      Survival rates matrix for females single ages between 1999 and 2019
                                                                                                                                                                      
+    Source: R/data.R
+    
+    
                                                                                                                                                                      
+
+    
                                                                                                                                                                      
+    
                                                                                                                                                                      Survival rates matrix for females single ages between 1999 and 2019 for
+Sweden
                                                                                                                                                                      
+    
                                                                                                                                                                      
+
+    
                                                                                                                                                                      sr_f_mat_single
                                                                                                                                                                      
+
+
+    
                                                                                                                                                                      Format
                                                                                                                                                                      
+
+    
                                                                                                                                                                      A matrix of dimensions 101 x 20
                                                                                                                                                                      
+    
                                                                                                                                                                      Source
                                                                                                                                                                      
+
+    
                                                                                                                                                                      Migration residual PAS spreadsheet
                                                                                                                                                                      
+
+  
                                                                                                                                                                      
+  
+
                                                                                                                                                                      
+
+
+      
                                                                                                                                                                      
+      
                                                                                                                                                                      
+  
                                                                                                                                                                      Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                                                                                                      
+
                                                                                                                                                                      
+
+
                                                                                                                                                                      
+  
                                                                                                                                                                      Site built with pkgdown 1.6.1.
                                                                                                                                                                      
+
                                                                                                                                                                      
+
+      
                                                                                                                                                                      
+   
                                                                                                                                                                      
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/sr_m_mat_five.html b/docs/reference/sr_m_mat_five.html
new file mode 100644
index 000000000..9fa96c5df
--- /dev/null
+++ b/docs/reference/sr_m_mat_five.html
@@ -0,0 +1,204 @@
+
+
+
+  
+  
+
+
+
+Survival rates matrix for males five year age groups between 1950 and 2045 — sr_m_mat_five • DemoTools
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
                                                                                                                                                                      
+      
                                                                                                                                                                      
+      
+
+      
+
+      
                                                                                                                                                                      
+
+
                                                                                                                                                                      
+  
                                                                                                                                                                      
+    
                                                                                                                                                                      
+    
                                                                                                                                                                      Survival rates matrix for males five year age groups between 1950 and 2045
                                                                                                                                                                      
+    Source: R/data.R
+    
+    
                                                                                                                                                                      
+
+    
                                                                                                                                                                      
+    
                                                                                                                                                                      Survival rates matrix for males five year age groups between 1950 and 2045
+for unknown country
                                                                                                                                                                      
+    
                                                                                                                                                                      
+
+    
                                                                                                                                                                      sr_m_mat_five
                                                                                                                                                                      
+
+
+    
                                                                                                                                                                      Format
                                                                                                                                                                      
+
+    
                                                                                                                                                                      A matrix of dimensions 21 x 20
                                                                                                                                                                      
+    
                                                                                                                                                                      Source
                                                                                                                                                                      
+
+    
                                                                                                                                                                      Migration residual PAS spreadsheet
                                                                                                                                                                      
+
+  
                                                                                                                                                                      
+  
+
                                                                                                                                                                      
+
+
+      
                                                                                                                                                                      
+      
                                                                                                                                                                      
+  
                                                                                                                                                                      Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                                                                                                      
+
                                                                                                                                                                      
+
+
                                                                                                                                                                      
+  
                                                                                                                                                                      Site built with pkgdown 1.6.1.
                                                                                                                                                                      
+
                                                                                                                                                                      
+
+      
                                                                                                                                                                      
+   
                                                                                                                                                                      
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/sr_m_mat_single.html b/docs/reference/sr_m_mat_single.html
new file mode 100644
index 000000000..0da4c92bc
--- /dev/null
+++ b/docs/reference/sr_m_mat_single.html
@@ -0,0 +1,204 @@
+
+
+
+  
+  
+
+
+
+Survival rates matrix for males single ages between 1999 and 2019 — sr_m_mat_single • DemoTools
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
                                                                                                                                                                      
+      
                                                                                                                                                                      
+      
+
+      
+
+      
                                                                                                                                                                      
+
+
                                                                                                                                                                      
+  
                                                                                                                                                                      
+    
                                                                                                                                                                      
+    
                                                                                                                                                                      Survival rates matrix for males single ages between 1999 and 2019
                                                                                                                                                                      
+    Source: R/data.R
+    
+    
                                                                                                                                                                      
+
+    
                                                                                                                                                                      
+    
                                                                                                                                                                      Survival rates matrix for males single ages between 1999 and 2019 for
+Sweden
                                                                                                                                                                      
+    
                                                                                                                                                                      
+
+    
                                                                                                                                                                      sr_m_mat_single
                                                                                                                                                                      
+
+
+    
                                                                                                                                                                      Format
                                                                                                                                                                      
+
+    
                                                                                                                                                                      A matrix of dimensions 101 x 20
                                                                                                                                                                      
+    
                                                                                                                                                                      Source
                                                                                                                                                                      
+
+    
                                                                                                                                                                      Migration residual PAS spreadsheet
                                                                                                                                                                      
+
+  
                                                                                                                                                                      
+  
+
                                                                                                                                                                      
+
+
+      
                                                                                                                                                                      
+      
                                                                                                                                                                      
+  
                                                                                                                                                                      Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                                                                                                      
+
                                                                                                                                                                      
+
+
                                                                                                                                                                      
+  
                                                                                                                                                                      Site built with pkgdown 1.6.1.
                                                                                                                                                                      
+
                                                                                                                                                                      
+
+      
                                                                                                                                                                      
+   
                                                                                                                                                                      
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/srb_vec_five.html b/docs/reference/srb_vec_five.html
new file mode 100644
index 000000000..a88f0abd8
--- /dev/null
+++ b/docs/reference/srb_vec_five.html
@@ -0,0 +1,202 @@
+
+
+
+  
+  
+
+
+
+Sex ratio at birth between 1950 and 2045 — srb_vec_five • DemoTools
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
                                                                                                                                                                      
+      
                                                                                                                                                                      
+      
+
+      
+
+      
                                                                                                                                                                      
+
+
                                                                                                                                                                      
+  
                                                                                                                                                                      
+    
                                                                                                                                                                      
+    
                                                                                                                                                                      Sex ratio at birth between 1950 and 2045
                                                                                                                                                                      
+    Source: R/data.R
+    
+    
                                                                                                                                                                      
+
+    
                                                                                                                                                                      
+    
                                                                                                                                                                      Sex ratio at birth between 1950 and 2045 for unknown country
                                                                                                                                                                      
+    
                                                                                                                                                                      
+
+    
                                                                                                                                                                      srb_vec_five
                                                                                                                                                                      
+
+
+    
                                                                                                                                                                      Format
                                                                                                                                                                      
+
+    
                                                                                                                                                                      A vector of length 20
                                                                                                                                                                      
+    
                                                                                                                                                                      Source
                                                                                                                                                                      
+
+    
                                                                                                                                                                      Migration residual PAS spreadsheet
                                                                                                                                                                      
+
+  
                                                                                                                                                                      
+  
+
                                                                                                                                                                      
+
+
+      
                                                                                                                                                                      
+      
                                                                                                                                                                      
+  
                                                                                                                                                                      Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                                                                                                      
+
                                                                                                                                                                      
+
+
                                                                                                                                                                      
+  
                                                                                                                                                                      Site built with pkgdown 1.6.1.
                                                                                                                                                                      
+
                                                                                                                                                                      
+
+      
                                                                                                                                                                      
+   
                                                                                                                                                                      
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/srb_vec_single.html b/docs/reference/srb_vec_single.html
new file mode 100644
index 000000000..c0e2e7193
--- /dev/null
+++ b/docs/reference/srb_vec_single.html
@@ -0,0 +1,202 @@
+
+
+
+  
+  
+
+
+
+Sex ratio at birth between 1999 and 2019 — srb_vec_single • DemoTools
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+  
+    
                                                                                                                                                                      
+      
                                                                                                                                                                      
+      
+
+      
+
+      
                                                                                                                                                                      
+
+
                                                                                                                                                                      
+  
                                                                                                                                                                      
+    
                                                                                                                                                                      
+    
                                                                                                                                                                      Sex ratio at birth between 1999 and 2019
                                                                                                                                                                      
+    Source: R/data.R
+    
+    
                                                                                                                                                                      
+
+    
                                                                                                                                                                      
+    
                                                                                                                                                                      Sex ratio at birth between 1999 and 2019 for Sweden
                                                                                                                                                                      
+    
                                                                                                                                                                      
+
+    
                                                                                                                                                                      srb_vec_single
                                                                                                                                                                      
+
+
+    
                                                                                                                                                                      Format
                                                                                                                                                                      
+
+    
                                                                                                                                                                      A vector of length 20
                                                                                                                                                                      
+    
                                                                                                                                                                      Source
                                                                                                                                                                      
+
+    
                                                                                                                                                                      Migration residual PAS spreadsheet
                                                                                                                                                                      
+
+  
                                                                                                                                                                      
+  
+
                                                                                                                                                                      
+
+
+      
                                                                                                                                                                      
+      
                                                                                                                                                                      
+  
                                                                                                                                                                      Developed by Tim Riffe, José Manuel Aburto, Ilya Kashnitsky, Monica Alexander, Marius D. Pascariu, Sara Hertog, Sean Fennell.
                                                                                                                                                                      
+
                                                                                                                                                                      
+
+
                                                                                                                                                                      
+  
                                                                                                                                                                      Site built with pkgdown 1.6.1.
                                                                                                                                                                      
+
                                                                                                                                                                      
+
+      
                                                                                                                                                                      
+   
                                                                                                                                                                      
+
+  
+
+
+  
+
+
+
diff --git a/docs/reference/surv10.html b/docs/reference/surv10.html
index 781bcc124..596d1aab0 100644
--- a/docs/reference/surv10.html
+++ b/docs/reference/surv10.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
 
@@ -88,7 +88,7 @@
       
                                                                                                                                                                        
         
                                                                                                                                                                      • 
   
-    
+    
      
   
 
                                                                                                                                                                        • 
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                                                                                                                                                                        • 
   
-    
+    
      
   
 
                                                                                                                                                                          • 
 
                                                                                                                                                                        • 
   
-    
+    
      
     github
   
diff --git a/docs/reference/surv5.html b/docs/reference/surv5.html
index d3ec4f896..0e6f5bba0 100644
--- a/docs/reference/surv5.html
+++ b/docs/reference/surv5.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
 
@@ -88,7 +88,7 @@
       
                                                                                                                                                                            
         
                                                                                                                                                                          • 
   
-    
+    
      
   
 
                                                                                                                                                                            • 
@@ -129,13 +129,13 @@
       
                                                                                                                                                                              
         
                                                                                                                                                                            • 
   
-    
+    
      
   
 
                                                                                                                                                                              • 
 
                                                                                                                                                                            • 
   
-    
+    
      
     github
   
diff --git a/docs/reference/survN.html b/docs/reference/survN.html
index 28b992a43..8086f8748 100644
--- a/docs/reference/survN.html
+++ b/docs/reference/survN.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
 
@@ -88,7 +88,7 @@
       
                                                                                                                                                                                
         
                                                                                                                                                                              • 
   
-    
+    
      
   
 
                                                                                                                                                                                • 
@@ -129,13 +129,13 @@
       
                                                                                                                                                                                  
         
                                                                                                                                                                                • 
   
-    
+    
      
   
 
                                                                                                                                                                                  • 
 
                                                                                                                                                                                • 
   
-    
+    
      
     github
   
diff --git a/docs/reference/survRatioError.html b/docs/reference/survRatioError.html
index 4b70c7ae2..26774e07b 100644
--- a/docs/reference/survRatioError.html
+++ b/docs/reference/survRatioError.html
@@ -81,7 +81,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
 
@@ -89,7 +89,7 @@
       
                                                                                                                                                                                    
         
                                                                                                                                                                                  • 
   
-    
+    
      
   
 
                                                                                                                                                                                    • 
@@ -130,13 +130,13 @@
       
                                                                                                                                                                                      
         
                                                                                                                                                                                    • 
   
-    
+    
      
   
 
                                                                                                                                                                                      • 
 
                                                                                                                                                                                    • 
   
-    
+    
      
     github
   
diff --git a/docs/reference/zelnik.html b/docs/reference/zelnik.html
index e1b064026..4d78ff51a 100644
--- a/docs/reference/zelnik.html
+++ b/docs/reference/zelnik.html
@@ -80,7 +80,7 @@
       
       
         DemoTools
-        01.13.20
+        01.13.76
       
     
 
@@ -88,7 +88,7 @@
       
                                                                                                                                                                                        
         
                                                                                                                                                                                      • 
   
-    
+    
      
   
 
                                                                                                                                                                                        • 
@@ -129,13 +129,13 @@
       
                                                                                                                                                                                          
         
                                                                                                                                                                                        • 
   
-    
+    
      
   
 
                                                                                                                                                                                          • 
 
                                                                                                                                                                                        • 
   
-    
+    
      
     github
   
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index a924cd1b1..7e8fb85a5 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -123,6 +123,9 @@
   
     https://timriffe.github.io/DemoTools//reference/dec.date.html
   
+  
+    https://timriffe.github.io/DemoTools//reference/downloadAsfr.html
+  
   
     https://timriffe.github.io/DemoTools//reference/downloadSRB.html
   
@@ -135,6 +138,9 @@
   
     https://timriffe.github.io/DemoTools//reference/e0_swe.html
   
+  
+    https://timriffe.github.io/DemoTools//reference/fetch_wpp_births.html
+  
   
     https://timriffe.github.io/DemoTools//reference/find_my_case.html
   
@@ -360,6 +366,9 @@
   
     https://timriffe.github.io/DemoTools//reference/maxA2abridged.html
   
+  
+    https://timriffe.github.io/DemoTools//reference/mig_beta.html
+  
   
     https://timriffe.github.io/DemoTools//reference/mig_calculate_rc.html
   
diff --git a/man/HMD_old_logquad.Rd b/man/HMD_old_logquad.Rd
new file mode 100644
index 000000000..dd6f75f87
--- /dev/null
+++ b/man/HMD_old_logquad.Rd
@@ -0,0 +1,83 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/log_quad_augm.R
+\name{HMD_old_logquad}
+\alias{HMD_old_logquad}
+\title{HMD pattern for adult ages.}
+\usage{
+HMD_old_logquad(
+  nMx,
+  Age = NULL,
+  Sex = "b",
+  q0_5 = NULL,
+  q15_45 = NULL,
+  q60_15 = NULL,
+  Age_transition = 80,
+  window_transition = 3,
+  plot_comparison = FALSE,
+  fitted_logquad = NULL,
+  ...
+)
+}
+\arguments{
+\item{nMx}{numeric. Vector of mortality rates in abridged age classes.}
+
+\item{Age}{integer. Single ages (abridged not allowed).}
+
+\item{Sex}{character. Either male \code{"m"}, female \code{"f"}, or both \code{"b"}.}
+
+\item{q0_5}{numeric. Probability of death from born to age 5. By default implicit values in \code{nMx} should be entered.}
+
+\item{q15_45}{numeric. Probability of death from age 15 to age 60. By default implicit values in \code{nMx} should be entered.}
+
+\item{q60_15}{numeric. Probability of death from age 60 to age 75. When external information on those ages level is available,
+can be included to increase parameter \code{ax} from log-quad model in last ages (Li, 2003).}
+
+\item{Age_transition}{integer. Form which age should transition to HMD pattern starts.}
+
+\item{window_transition}{integer. Number of ages to the left and to the right of \code{Age_transition} to do a log-linear transition in rates.}
+
+\item{plot_comparison}{Show or not a plot with the result.}
+
+\item{fitted_logquad}{Optional, defaults to \code{NULL}. An object of class
+\code{wilmoth}. If full HMD is not enough, one
+can fit a Log-Quadratic (\url{https://github.com/mpascariu/MortalityEstimate}) model
+based on any other collection  of life tables;}
+
+\item{...}{Other arguments to be passed on to the \code{lt_single} function.}
+}
+\value{
+life table as in \code{lt_single} function.
+}
+\description{
+Adjust rates in oldest ages using HMD pattern, based on log-quad method.
+}
+\details{
+One possible scenario when mortality data on last ages is not reliable, is to use a mortality pattern with some known index in previous ages.
+This function gives a HMD pattern based on 5q0 and 45q15, using log-quad model. Additionally, a value on mortality between 60 and 75 can be included to make a better adjustment in level.
+}
+\examples{
+# Mortality rates from UN Chilean with e0=70. Wat would be the rates based on HMD pattern? 
+# In this case making a transition of 10 years at age 80, and returning an OAG=100.
+\dontrun{
+lt <- DemoToolsData::modelLTx1
+lt <- lt[lt$family == "Chilean" & lt$sex == "female" & lt$e0 == 70,]
+chilean70_adjHMD <- HMD_old_logquad(nMx = lt$mx1, 
+                                         Age = lt$age, 
+                                       Sex = "f", 
+                                       q0_5 = 1 - lt$lx1[lt$age==5]/lt$lx1[lt$age==0], 
+                                       q15_45 = 1 - lt$lx1[lt$age==60]/lt$lx1[lt$age==15],
+                                       Age_transition = 80, 
+                                       window_transition = 10, 
+                                       plot_comparison = TRUE,
+                                       OAnew = 100)
+# We know (as an example) that q60_15 is .5 higher than  what HMD pattern would be.
+chilean70_adjHMD_augm <- HMD_old_logquad(nMx = lt$mx1, 
+                                       Age = lt$age, 
+                                       Sex = "f", 
+                                       q0_5 = 1 - lt$lx1[lt$age==5]/lt$lx1[lt$age==0], 
+                                       q15_45 = 1 - lt$lx1[lt$age==60]/lt$lx1[lt$age==15],
+                                        q60_15 = (1 - lt$lx1[lt$age==75]/lt$lx1[lt$age==60]) * 1.5, 
+                                        Age_transition = 80, window_transition = 10,
+                                       OAnew = 100, plot_comparison = TRUE)
+}
+}
diff --git a/man/OPAG.Rd b/man/OPAG.Rd
index e447cc4d5..7aaa27e9a 100644
--- a/man/OPAG.Rd
+++ b/man/OPAG.Rd
@@ -7,16 +7,13 @@
 OPAG(
   Pop,
   Age_Pop,
-  AgeInt_Pop,
   nLx,
   Age_nLx,
-  AgeInt_nLx = NULL,
   Age_fit = NULL,
   AgeInt_fit = NULL,
   Redistribute_from = max(Age_Pop),
   OAnew = max(Age_nLx),
-  method = "uniform",
-  continuous = TRUE
+  method = "mono"
 )
 }
 \arguments{
@@ -24,13 +21,9 @@ OPAG(
 
 \item{Age_Pop}{integer vector of the lower bounds of the population age groups}
 
-\item{AgeInt_Pop}{integer vector of the population age group interval widths, using \code{Inf} for the open age group.}
+\item{nLx}{numeric vector of stationary population age structure in arbitrary integer age groups}
 
-\item{nLx}{numeric vector of stable population standard}
-
-\item{Age_nLx}{integer vector of lower bounds for age groups of \code{nLx}}
-
-\item{AgeInt_nLx}{optional integer vector of widths of age groups of \code{nLx}, inferred if not given.}
+\item{Age_nLx}{integer vector of lower bounds of age groups of \code{nLx}}
 
 \item{Age_fit}{integer vector of lower bounds for age groups of \code{Pop_fit}}
 
@@ -40,9 +33,7 @@ OPAG(
 
 \item{OAnew}{integer. Desired open age group in the output (must being element of \code{Age_nLx})}
 
-\item{method}{character. Graduation method, default \code{"uniform"}. \code{"mono"} or \code{"pclm"} would also be good choices.}
-
-\item{continuous}{logical. If \code{TRUE} we use the growth adjustment. \code{e^(-age*r)}. If \code{FALSE} we assume \code{r} is geometric growth, and we use \code{(1+r)^age} for the growth adjustment.}
+\item{method}{character, graduation method used for intermediate graduation. Default \code{"mono"}. Other reasonable choices include \code{"pclm"} or \code{"uniform"}.}
 }
 \description{
 This can be used as an external check of population counts
@@ -58,37 +49,29 @@ constant growth, \code{r}.
 It may be helpful to try more than one fitting possibility,
 and more than one \code{Redistribute_from} cut point, as results may vary.
 
-The argument \code{"method"} refers to which graduation method (see \code{?graduate})
-is only relevant if input data are in grouped ages. This is innocuous if
-ages are single to begin with. The choice of whether to assume
-\code{continuous = TRUE} constant growth versus geometric (\code{FALSE}) growth
-has little leverage.
-
 \code{Redistribute_from} can be lower than your current open age group,
 and \code{OAnew} can be higher, as long as it is within the range of \code{Age_nLx}.
 If \code{Age_nLx} doesn't go high enough for your needs, you can extrapolate
-it ahead of time. For this, you'd want the \code{nMx} the underly it, and you
+it ahead of time. For this, you'd want the \code{nMx} the underlie it, and you
 can use \code{lt_abridged()}, specifying a higher open age, and then
 extracting \code{nLx} again from it.
 }
 \examples{
-# India Males, 1991
+# India Males, 1971
 Pop            <- smooth_age_5(pop1m_ind,
                          Age = 0:100,
                          method = "Arriaga")
 Age_Pop        <- names2age(Pop)
 AgeInt_Pop     <- age2int(Age_Pop, OAvalue = 1)
 
-nLx            <- downloadnLx(NULL, "India","male",1991)
+nLx            <- downloadnLx(NULL, "India","male",1971)
 Age_nLx        <- names2age(nLx)
 AgeInt_nLx     <- age2int(Age_nLx, OAvalue = 1)
 
 Pop_fit <- OPAG(Pop,
     Age_Pop = Age_Pop,
-    AgeInt_Pop = AgeInt_Pop,
     nLx = nLx,
     Age_nLx = Age_nLx,
-    AgeInt_nLx,
     Age_fit =  c(60,70),
     AgeInt_fit = c(10,10),
     Redistribute_from = 80)
diff --git a/man/OPAG_fit_stable_standard.Rd b/man/OPAG_fit_stable_standard.Rd
index c3b2a4e07..4f4954dcc 100644
--- a/man/OPAG_fit_stable_standard.Rd
+++ b/man/OPAG_fit_stable_standard.Rd
@@ -4,16 +4,7 @@
 \alias{OPAG_fit_stable_standard}
 \title{creates stable standard based on optimizing the growth rate}
 \usage{
-OPAG_fit_stable_standard(
-  Pop_fit,
-  Age_fit,
-  AgeInt_fit,
-  nLx,
-  Age_nLx,
-  AgeInt_nLx,
-  method = "uniform",
-  continuous = TRUE
-)
+OPAG_fit_stable_standard(Pop_fit, Age_fit, AgeInt_fit, Lx1, Age_Lx1)
 }
 \arguments{
 \item{Pop_fit}{numeric vector of at least two population counts to use for fitting}
@@ -22,15 +13,9 @@ OPAG_fit_stable_standard(
 
 \item{AgeInt_fit}{integer vector of widths of age groups of \code{Pop_fit}}
 
-\item{nLx}{numeric vector of stable population standard}
-
-\item{Age_nLx}{integer vector of lower bounds for age groups of \code{nLx}}
-
-\item{AgeInt_nLx}{optional integer vector of widths of age groups of \code{nLx}, inferred if not given.}
-
-\item{method}{character. Graduation method, default \code{"uniform"}. \code{"mono"} or \code{"pclm"} would also be good choices.}
+\item{Lx1}{numeric vector of stable population standard by single ages}
 
-\item{continuous}{logical. If \code{TRUE} we use the growth adjustment. \code{e^(-age*r)}. If \code{FALSE} we assume \code{r} is geometric growth, and we use \code{(1+r)^age} for the growth adjustment.}
+\item{Age_Lx1}{integer vector of lower bounds for age groups of \code{Lx1}}
 }
 \value{
 list constaining
@@ -44,47 +29,44 @@ redistribution in \code{OPAG()}
 The stationary standard, \code{nLx} is transformed into a stable standard by optimizing a growth rate, \code{r} such that the stable standard matches observed population counts in selected age groups. Usually the ages used for fitting are wide age groups in older ages preceding the open age group. The standard output by this function is used by \code{OPAG} to create the standard used to redistribute counts over older age groups up to a specified open age group, such as 100.
 }
 \details{
-The arguments \code{method} and \code{continous} don't have much leverage on the result. In short, the stable population transformation is done by ungrouping \code{nLx} to single ages (if it isn't already), and \code{method} controls which graduation method is used for this, where \code{"uniform"}, \code{"mono"}, \code{"pclm"} are the reasonable choices at this writing. In single ages, the difference between using a geometric \code{r} versus continuous \code{r} are quite small for this task.
+The argument \code{method} don't have much leverage on the result. In short, the stable population transformation is done by ungrouping \code{nLx} to single ages (if it isn't already), and \code{method} controls which graduation method is used for this, where \code{"uniform"}, \code{"mono"}, \code{"pclm"} are the reasonable choices at this writing.
 }
 \examples{
 Pop_fit    <- c(85000,37000)
 Age_fit    <- c(70,80)
-AgeInt_fit <- c(10,10)
 nLx        <- downloadnLx(NULL, "Spain","female",1971)
 Age_nLx    <- names2age(nLx)
-
-# India Males, 1991
+Lx1 <- graduate(nLx,Age=Age_nLx,method = "mono")
+Age_Lx1 <- 0:100
+# India Males, 1971
 Pop        <- smooth_age_5(pop1m_ind,
                            Age = 0:100,
                            method = "Arriaga")
 Pop80      <- groupOAG(Pop, names2age(Pop), 80)
 Age        <- names2age(Pop80)
-AgeInt     <- age2int(Age, OAvalue = 1)
 
-nLx        <- downloadnLx(NULL, "India","male",1991)
+nLx        <- downloadnLx(NULL, "India","male",1971)
 Age_nLx    <- names2age(nLx)
-AgeInt_nLx <- age2int(Age_nLx,OAvalue = 1)
+
 
 Pop_fit    <- groupAges(Pop80, Age, N = 10)[c("60","70")]
 Age_fit    <- c(60,70)
 AgeInt_fit <- c(10,10)
 
 Standard <- OPAG_fit_stable_standard(
-             Pop_fit,
-             Age_fit,
-             AgeInt_fit,
-             nLx = nLx,
-             Age_nLx = Age_nLx,
-             AgeInt_nLx = AgeInt_nLx,
-             method = "uniform",
-             continuous = TRUE)
+  Pop_fit = Pop_fit,
+  Age_fit = Age_fit,
+  AgeInt_fit = AgeInt_fit,
+  Lx1=Lx1,
+  Age_Lx1 = Age_Lx1
+)
 
 # A visual comparison:
 nL60 <- rescale_vector(nLx[Age_nLx >= 60])
-St60p <- rescale_vector( Standard$Standard[Age_nLx >= 60] )
+St60p <- rescale_vector( Standard$Standard[c(0:100) >= 60] )
 ages_plot <- seq(60,100,by=5)
 \dontrun{
-plot(ages_plot,nL60, type = 'l')
-lines(ages_plot, St60p, col = "blue")
+  plot(ages_plot,nL60, type = 'l')
+  lines(60:100, St60p, col = "blue")
 }
 }
diff --git a/man/OPAG_nLx_warp_r.Rd b/man/OPAG_nLx_warp_r.Rd
index f12537911..d884a4171 100644
--- a/man/OPAG_nLx_warp_r.Rd
+++ b/man/OPAG_nLx_warp_r.Rd
@@ -4,27 +4,14 @@
 \alias{OPAG_nLx_warp_r}
 \title{Warps a given stationary population into a stable population}
 \usage{
-OPAG_nLx_warp_r(
-  nLx,
-  Age,
-  r,
-  AgeInt = NULL,
-  continuous = TRUE,
-  method = "uniform"
-)
+OPAG_nLx_warp_r(Lx1, Age_Lx1, r)
 }
 \arguments{
-\item{nLx}{numeric vector of stationary population age structure in arbitrary integer age groups}
+\item{Lx1}{numeric vector of stationary population age structure in arbitrary integer age groups}
 
-\item{Age}{interger vector of lower bounds of age groups of \code{nLx}}
+\item{Age_Lx1}{integer vector of lower bounds of age groups of \code{nLx}}
 
 \item{r}{stable growth rate}
-
-\item{AgeInt}{optional integer vector of widths of age groups, inferred if not given.}
-
-\item{continuous}{logical. If \code{TRUE} we use the growth adjustment. \code{e^(-age*r)}. If \code{FALSE} we assume \code{r} is geometric growth, and we use \code{(1+r)^age} for the growth adjustment.}
-
-\item{method}{character, graduation method used for intermediate graduation. Default \code{"uniform"}. Other reasonable choices include \code{"mono"} or \code{"pclm"}.}
 }
 \value{
 numeric vector of the transformed \code{nLx}. Note, this vector sums to \code{1}.
@@ -34,7 +21,7 @@ We take \code{nLx} as indicative of a stationary population age structure,
 then subject the population structure to long-term growth by a constant rate, \code{r}.
 }
 \details{
-\code{nLx} could be any population structure of any scale, as long as you're comfortable
+\code{Lx1} could be any population structure of any scale, as long as you're comfortable
 assuming it's stationary and can be warped into stable. For the oldest ages, this is probably
 quite often an acceptable and useful approximation. The transformation is applied at the single-age scale, even if the input \code{nLx} is in wider (e.g. abridged) age groups. When needed, we reduce to single ages using (default) \code{graduate_uniform()}, then apply the transformation, then group back. This is innocuous if \code{nLx} is given in single ages. You may want to change \code{method} to \code{"mono"} or \code{"pclm"}.
 }
diff --git a/man/OPAG_r_min.Rd b/man/OPAG_r_min.Rd
index c071d52ce..f77b5307c 100644
--- a/man/OPAG_r_min.Rd
+++ b/man/OPAG_r_min.Rd
@@ -4,36 +4,20 @@
 \alias{OPAG_r_min}
 \title{calculates residual for optimizing growth rate r for OPAG family}
 \usage{
-OPAG_r_min(
-  r,
-  Pop_fit,
-  Age_fit,
-  AgeInt_fit,
-  nLx,
-  Age_nLx,
-  AgeInt_nLx = NULL,
-  continuous = TRUE,
-  method = "uniform"
-)
+OPAG_r_min(r, Age_fit, Pop_fit, AgeInt_fit, Lx1, Age_Lx1)
 }
 \arguments{
 \item{r}{given stable growth rate}
 
-\item{Pop_fit}{numeric vector of at least two population counts to use for fitting}
-
 \item{Age_fit}{integer vector of lower bounds for age groups of \code{Pop_fit}}
 
-\item{AgeInt_fit}{integer vector of widths of age groups of \code{Pop_fit}}
-
-\item{nLx}{numeric vector of stable population standard}
-
-\item{Age_nLx}{integer vector of lower bounds for age groups of \code{nLx}}
+\item{Pop_fit}{numeric vector of at least two population counts to use for fitting}
 
-\item{AgeInt_nLx}{optional integer vector of widths of age groups of \code{nLx}, inferred if not given.}
+\item{AgeInt_fit}{integer vector of widths of age groups of \code{Pop_fit}}
 
-\item{continuous}{logical. If \code{TRUE} we use the growth adjustment. \code{e^(-age*r)}. If \code{FALSE} we assume \code{r} is geometric growth, and we use \code{(1+r)^age} for the growth adjustment.}
+\item{Lx1}{numeric vector of stable population standard by single ages}
 
-\item{method}{character. Graduation method, default \code{"uniform"}. \code{"mono"} or \code{"pclm"} would also be good choices.}
+\item{Age_Lx1}{integer vector of lower bounds for age groups of \code{Lx1}}
 }
 \value{
 numeric. A residual that you're presumably trying to minimize.
@@ -43,7 +27,7 @@ For a given set of age groups to fit against, and a given stable growth rate, $r
 what is the error implied given the current $r$ and stationary standard?
 }
 \details{
-This is a utiltiy function for \code{OPAG()}, which needs to optimize $r$ for a
+This is a utility function for \code{OPAG()}, which needs to optimize $r$ for a
 given population vector and stationary standard.
 }
 \examples{
@@ -52,32 +36,25 @@ Pop_fit    <- c(85000,37000)
 Age_fit    <- c(70,80)
 AgeInt_fit <- c(10,10)
 nLx        <- downloadnLx(NULL, "Spain","female",1971)
-Age_nLx    <- names2age(nLx)
+# graduate(nLx, Age_nLx, method = method, constrain = TRUE)
+Ageab    <- names2age(nLx)
+Lx1 <- graduate(c(nLx), Ageab, method = "mono", constrain = TRUE)
+Age_Lx1 <- 0:100
 r          <- .01
 
 OPAG_r_min(r,
-           Pop_fit,
-           Age_fit,
-           AgeInt_fit,
-           nLx,
-           Age_nLx)
+           Pop_fit = Pop_fit,
+           Age_fit = Age_fit,
+           AgeInt_fit = AgeInt_fit,
+           Lx1 = Lx1,
+           Age_Lx1 = Age_Lx1)
 
 (r_opt <- optimize(OPAG_r_min,
          Pop_fit = Pop_fit,
          Age_fit = Age_fit,
          AgeInt_fit = AgeInt_fit,
-         nLx = nLx,
-         Age_nLx = Age_nLx,
+         Lx1 = Lx1,
+         Age_Lx1 = Age_Lx1,
          interval = c(-0.05,.05))$min)
 
-ai  <- age2int(Age_nLx)
-
-# Note the whole age range is being scaled to 1 here, but in practice
-# you'd only be doing this in the highest ages. If only two fitting
-# ages are given, then we can get an r that matches them perfectly,
-# at least within reasonable bounds.
-\dontrun{
-plot(Age, nLx / sum(nLx) / ai, type = 's')
-lines(Age,OPAG_nLx_warp_r(Lx,Age,r=r_opt)/ai,type='s',col = "red")
-}
 }
diff --git a/man/OPAG_simple.Rd b/man/OPAG_simple.Rd
index ece6c6a27..405bed0ad 100644
--- a/man/OPAG_simple.Rd
+++ b/man/OPAG_simple.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/OPAG.R
 \name{OPAG_simple}
 \alias{OPAG_simple}
-\title{redistripute an open age group count over higher ages proportional to an arbitrary standard}
+\title{redistribute an open age group count over higher ages proportional to an arbitrary standard}
 \usage{
 OPAG_simple(Pop, Age, OAnow = max(Age), StPop, StAge, OAnew = max(StAge))
 }
diff --git a/man/ageRatioScore.Rd b/man/ageRatioScore.Rd
index 71b6507b3..8970c5ea9 100644
--- a/man/ageRatioScore.Rd
+++ b/man/ageRatioScore.Rd
@@ -39,7 +39,7 @@ Age groups must be of equal intervals. Five year age groups are assumed.
 It is also assumed that the final age group is open, unless \code{ageMax < max(Age)}.
 Setting \code{OAG = FALSE} will override this and potentially include \code{max(Age)} in calculations.
 The method argument determines the weighting of numerators and denominators, where the UN method puts
-twice the numerator over the sum of the adjacent ages classes, Zelnich does thrice the
+twice the numerator over the sum of the adjacent ages classes, Zelnik does thrice the
 numerator over the sum of the whole range from the next lowest to the next highest age, and
 Ramachandran does four times the numerator over the same sum, but with the central age
 double-counted in the numerator.
diff --git a/man/age_abridge_force.Rd b/man/age_abridge_force.Rd
index a6052a31d..06c6ec526 100644
--- a/man/age_abridge_force.Rd
+++ b/man/age_abridge_force.Rd
@@ -4,17 +4,15 @@
 \alias{age_abridge_force}
 \title{force a (count) vector to abridged ages}
 \usage{
-age_abridge_force(Value, AgeInt, Age)
+age_abridge_force(Value, Age)
 }
 \arguments{
 \item{Value}{numeric vector, presumably counts in grouped ages}
 
-\item{AgeInt}{integer vector, age interval widths}
-
 \item{Age}{integer vector, lower bounds of age groups}
 }
 \description{
-This is a robustness utility, in place to avoid annoying hang-ups in \code{LTAbr()}. If data are given in non-standard ages, they are forced to standard abrdiged ages on the fly. Really this should happen prior to calling \code{lt_abridged()}
+This is a robustness utility, in place to avoid annoying hang-ups in \code{LTAbr()}. If data are given in non-standard ages, they are forced to standard abridged ages on the fly. Really this should happen prior to calling \code{lt_abridged()}
 }
 \details{
 This should be able to group up and group down as needed. \code{graduate_mono()} is used below the hood. \code{pclm()} or \code{graduate_uniform()} out to be flexible enough to do the same.
@@ -26,7 +24,7 @@ AgeInt    <- c(1,2,2,rep(5,19),1)
 Value     <- tapply(V1,rep(Age,times=AgeInt), sum)
 
 is_abridged(Age)
-age_abridge_force(Value, AgeInt, Age)
+age_abridge_force(Value, Age)
 }
 \seealso{
 graduate_mono_closeout, lt_abridged
diff --git a/man/ages_asfr_five.Rd b/man/ages_asfr_five.Rd
index 0ece98bd7..cf44c2765 100644
--- a/man/ages_asfr_five.Rd
+++ b/man/ages_asfr_five.Rd
@@ -8,7 +8,7 @@
 A vector of length 7
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 ages_asfr_five
diff --git a/man/ages_asfr_single.Rd b/man/ages_asfr_single.Rd
index 8e1eab99e..8b397587b 100644
--- a/man/ages_asfr_single.Rd
+++ b/man/ages_asfr_single.Rd
@@ -8,7 +8,7 @@
 A vector of length 36
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 ages_asfr_single
diff --git a/man/ages_five.Rd b/man/ages_five.Rd
index 4bb4a2642..40606061a 100644
--- a/man/ages_five.Rd
+++ b/man/ages_five.Rd
@@ -8,7 +8,7 @@
 A vector of length 21
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 ages_five
diff --git a/man/ages_single.Rd b/man/ages_single.Rd
index 773c95c27..4532d9626 100644
--- a/man/ages_single.Rd
+++ b/man/ages_single.Rd
@@ -8,7 +8,7 @@
 A vector of length 101
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 ages_single
diff --git a/man/agesmth1.Rd b/man/agesmth1.Rd
index ddaaac244..2df2e1e70 100644
--- a/man/agesmth1.Rd
+++ b/man/agesmth1.Rd
@@ -42,7 +42,7 @@ constrained to the original total in all cases.
 LOESS (locally weighted smoothing) helps to smooth data over age, preserving the open age group if necessary.
 It is a popular tool to create a smooth line through a timeplot or scatter plot.
 Loess smoothness may be tweaked by specifying an optional \code{"span"} argument.
-Polynomial  fitting is used to mooth data over age or time fitting linear models.
+Polynomial  fitting is used to smooth data over age or time fitting linear models.
 It can be tweaked by changing the degree and by either log or power transforming.
 The open age group can be kept as-is if desired by specifying \code{OAG = TRUE}.
 May be used on any age groups, including irregularly spaced, single age, or 5-year age groups.
diff --git a/man/asfr_mat_five.Rd b/man/asfr_mat_five.Rd
index 7e60ea2f1..e5f945a95 100644
--- a/man/asfr_mat_five.Rd
+++ b/man/asfr_mat_five.Rd
@@ -8,7 +8,7 @@
 A matrix of dimensions 7 x 20
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 asfr_mat_five
diff --git a/man/asfr_mat_single.Rd b/man/asfr_mat_single.Rd
index 7940b4dfc..43395ac3c 100644
--- a/man/asfr_mat_single.Rd
+++ b/man/asfr_mat_single.Rd
@@ -8,7 +8,7 @@
 A matrix of dimensions 35 x 20
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 asfr_mat_single
diff --git a/man/basepop_five.Rd b/man/basepop_five.Rd
index 0a37bc585..501f54d4a 100644
--- a/man/basepop_five.Rd
+++ b/man/basepop_five.Rd
@@ -111,8 +111,8 @@ should be aware of.}
 \item \code{nLxm} numeric matrix of male \code{nLx}, abridged ages in rows and (potentially interpolated) time in columns. Potentially downloaded.
 \item \code{Asfr} numeric matrix of age specific fertility in 5-year age groups ages 15-19 until 45-49 in rows, and (potentially interpolated) time in columns. Potentially downloaded.
 \item \code{Exposure_female} numeric matrix of approximated age-specific exposure in 5-year age groups ages 15-19 until 45-49 in rows, and (potentially interpolated) time in columns.
-\item \code{Bt} births at three time points prior to census corrsponding to the midpoints of the cohorts entering ages 0, 1-4, and 5-9.
-\item \code{SRB} sex ratio at birth at three time points prior to census corrsponding to the midpoints of the cohorts entering ages 0, 1-4, and 5-9. Potentially downloaded.
+\item \code{Bt} births at three time points prior to census corresponding to the midpoints of the cohorts entering ages 0, 1-4, and 5-9.
+\item \code{SRB} sex ratio at birth at three time points prior to census corresponding to the midpoints of the cohorts entering ages 0, 1-4, and 5-9. Potentially downloaded.
 \item \code{Age} age groups of the input population counts.
 }
 }
diff --git a/man/calcAgeN.Rd b/man/calcAgeN.Rd
index ff3e1702d..fd2ac7306 100644
--- a/man/calcAgeN.Rd
+++ b/man/calcAgeN.Rd
@@ -21,7 +21,7 @@ Assign single ages to age groups of equal and arbitrary width, and also optional
 }
 \details{
 If you shift the groupings, then the first age groups may have a negative lower bound
-(for example of -5). These counts would be discarded for the osculatory version of Sprague smoothing,
+(for example of -5). These counts would be discarded for the oscillatory version of Sprague smoothing,
 for example, but they are preserved in this function. The important thing to know is that if you shift
 the groups, the first and last groups won't be N years wide. For example if \code{shiftdown} is 1,
 the first age group is 4-ages wide.
diff --git a/man/check_heaping_kannisto.Rd b/man/check_heaping_kannisto.Rd
index 7e1840b0c..f2d69a00e 100644
--- a/man/check_heaping_kannisto.Rd
+++ b/man/check_heaping_kannisto.Rd
@@ -22,7 +22,7 @@ The value of the index.
 This age heaping index is used for particular old ages, such as 90, 95, 100, 105, and so forth.
 }
 \details{
-The index looks down two ages and up two ages, so the data must accommodate that range. The denominator is a mean of the counts in the sourrounding 5 single ages. The kind of mean can be controlled with the \code{pow} argument. By default, this takes the antilog of the arithmetic mean of the natural log of the five denominator counts. That will fail if one of the counts is equal to 0. In such cases, another power, such as 2 or 10 or 100 may be used, which is more robust to 0s. The higher the power, the closer the result will resemble the default output. If \code{pow=="exp"} but a 0 is detected among the denominator ages, then \code{pow} is assigned a value of 1000. \code{pow=1} would imply an arithmetic mean in the denominator.
+The index looks down two ages and up two ages, so the data must accommodate that range. The denominator is a mean of the counts in the surrounding 5 single ages. The kind of mean can be controlled with the \code{pow} argument. By default, this takes the antilog of the arithmetic mean of the natural log of the five denominator counts. That will fail if one of the counts is equal to 0. In such cases, another power, such as 2 or 10 or 100 may be used, which is more robust to 0s. The higher the power, the closer the result will resemble the default output. If \code{pow=="exp"} but a 0 is detected among the denominator ages, then \code{pow} is assigned a value of 1000. \code{pow=1} would imply an arithmetic mean in the denominator.
 }
 \examples{
 Age <- 0:99
diff --git a/man/check_heaping_roughness.Rd b/man/check_heaping_roughness.Rd
index 258b2bef6..10c36f2cb 100644
--- a/man/check_heaping_roughness.Rd
+++ b/man/check_heaping_roughness.Rd
@@ -18,10 +18,10 @@ check_heaping_roughness(
 
 \item{ageMin}{integer evenly divisible by 5. Lower bound of evaluated age range, default 20.}
 
-\item{ageMax}{integer evently divisibly by 5. Upper bound of evaluated age range, defaults to highest age evenly divisible by 10.}
+\item{ageMax}{integer evenly divisible by 5. Upper bound of evaluated age range, defaults to highest age evenly divisible by 10.}
 }
 \description{
-For a given age-structured vector of counts, how rough is data after grouping to 5-year age bins? Data may require smoothing even if there is no detectable sawtooth pattern. It is best to use the value in this method together with visual evidence to gauage whether use of \code{smooth_age_5()} is recommended.
+For a given age-structured vector of counts, how rough is data after grouping to 5-year age bins? Data may require smoothing even if there is no detectable sawtooth pattern. It is best to use the value in this method together with visual evidence to gauge whether use of \code{smooth_age_5()} is recommended.
 }
 \details{
 First we group data to 5-year age bins. Then we take first differences (d1) of these within the evaluated age range. Then we smooth first differences (d1s) using a generic smoother (\code{ogive()}). Roughness is defined as the mean of the absolute differences between \code{mean(abs(d1 - d1s) / abs(d1s))}. Higher values indicate rougher data, and may suggest more aggressive smoothing. Just eyeballing, one could consider smoothing if the returned value is greater than ca 0.2, and values greater than 0.5 already highly recommend it (pending visual verification).
diff --git a/man/check_heaping_sawtooth.Rd b/man/check_heaping_sawtooth.Rd
index 563343ebc..b02257a68 100644
--- a/man/check_heaping_sawtooth.Rd
+++ b/man/check_heaping_sawtooth.Rd
@@ -18,13 +18,13 @@ check_heaping_sawtooth(
 
 \item{ageMin}{integer evenly divisible by 10. Lower bound of evaluated age range, default 40.}
 
-\item{ageMax}{integer evently divisibly by 5. Upper bound of evaluated age range, defaults to highest age evenly divisible by 10.}
+\item{ageMax}{integer evenly divisible by 5. Upper bound of evaluated age range, defaults to highest age evenly divisible by 10.}
 }
 \value{
 numeric, ratio of 0s to 5s. If > 1 then the pattern is present.
 }
 \description{
-Ages ending in 0 often have higher apparent heaping than ages ending in 5. In this case, data in 5-year age bins might show a sawtooth pattern. If heaping ocurrs in roughly the same amount on 0s and 5s, then it may be sufficient to group data into 5-year age groups and then graduate back to single ages. However, if heaping is worse on 0s, then this procedure tends to produce a wavy pattern in count data, with 10-year periodicity. In this case it is recommended to use one of the methods of \code{smooth_age_5()} as an intermediate step before graduation.
+Ages ending in 0 often have higher apparent heaping than ages ending in 5. In this case, data in 5-year age bins might show a sawtooth pattern. If heaping occurs in roughly the same amount on 0s and 5s, then it may be sufficient to group data into 5-year age groups and then graduate back to single ages. However, if heaping is worse on 0s, then this procedure tends to produce a wavy pattern in count data, with 10-year periodicity. In this case it is recommended to use one of the methods of \code{smooth_age_5()} as an intermediate step before graduation.
 }
 \details{
 Data is grouped to 5-year age bins. The ratio of each value to the average of its neighboring values is calculated. If 0s have stronger attraction than 5s then we expect these ratios to be >1 for 0s and <1 for 5s. Ratios are compared within each 10-year age group in the evaluated age range. If in the evaluated range there are at most two exceptions to this rule (0s>5s), then the ratio of the mean of these ratios is returned, and it is recommended to use a smoother method. Higher values suggest use of a more aggressive method. This approach is only slightly different from that of Feeney, as implemented in the \code{smooth_age_5_zigzag_inner()} functions. This is not a general measure of roughness, but rather an indicator of this particular pattern of age attraction.
diff --git a/man/downloadAsfr.Rd b/man/downloadAsfr.Rd
index 609808be2..91b544745 100644
--- a/man/downloadAsfr.Rd
+++ b/man/downloadAsfr.Rd
@@ -19,7 +19,7 @@ downloadAsfr(Asfrmat, location = NULL, AsfrDatesIn, method = "linear")
 numeric matrix interpolated asfr
 }
 \description{
-We extract \code{ASFRx} from \code{wpp2019}, interpolated to exact dates. Different methods availables.
+We extract \code{ASFRx} from \code{wpp2019}, interpolated to exact dates. Different methods available.
 A vector of countries can handle, but with an unique sex. Row names are not indicative of countries.
 }
 \examples{
diff --git a/man/downloadnLx.Rd b/man/downloadnLx.Rd
index 0b5f18d67..ba1dfed2a 100644
--- a/man/downloadnLx.Rd
+++ b/man/downloadnLx.Rd
@@ -18,10 +18,10 @@ downloadnLx(nLx, location, gender, nLxDatesIn, method = "linear")
 \item{method}{character. Could be \code{"linear"}, \code{"exponential"}, or \code{"power"}}
 }
 \value{
-numeric matrix of \code{nLx} with \code{length(nLxDatesIn)} and abrdiged ages in rows.
+numeric matrix of \code{nLx} with \code{length(nLxDatesIn)} and abridged ages in rows.
 }
 \description{
-We extract \code{Lx} from \code{wpp2019}, interpolated to exact dates. Different methods availables.
+We extract \code{Lx} from \code{wpp2019}, interpolated to exact dates. Different methods available.
 A vector of countries can handle, but with an unique sex. Row names are not indicative of countries.
 }
 \examples{
diff --git a/man/graduate.Rd b/man/graduate.Rd
index 9b57bfdd0..8753033ae 100644
--- a/man/graduate.Rd
+++ b/man/graduate.Rd
@@ -10,8 +10,7 @@ graduate(
   AgeInt = age2int(Age),
   OAG = TRUE,
   OAnew = max(Age),
-  method = c("sprague", "beers(ord)", "beers(mod)", "grabill", "pclm", "mono",
-    "uniform"),
+  method = c("sprague", "beers(ord)", "beers(mod)", "grabill", "pclm", "mono", "uniform"),
   keep0 = FALSE,
   constrain = FALSE,
   ...
@@ -50,7 +49,7 @@ This wrapper standardizes some inconsistencies in how open ages are dealt with.
 
 \code{OAnew} cannot be higher than \code{max(Age)+4} for \code{"sprague"} or \code{"beers"} methods. For \code{"uniform","mono","pclm"} it can be higher than this, and in each case the open age group is completely redistributed within this range, meaning it's not really open anymore.
 
-For all methods, negative values are detected in output. If present, we deal with these in the following way: we take the geometric mean between the given output (with negative imputed with 0s) and the output of \code{graduate_mono()}, which is guaranteed non-negative. This only affects age groups where negatives were produced in the first pass. In our experience this only arises when using sprague, beers, or grabill methods, whereas all others are guarateed non-negative.
+For all methods, negative values are detected in output. If present, we deal with these in the following way: we take the geometric mean between the given output (with negative imputed with 0s) and the output of \code{graduate_mono()}, which is guaranteed non-negative. This only affects age groups where negatives were produced in the first pass. In our experience this only arises when using Sprague, Beers, or Grabill methods, whereas all others are guaranteed non-negative.
 
 For any case where input data are in single ages, constraining results to sum to values in the original age groups will simply return the original input data, which is clearly not your intent. This might arise when using graduation as an implicit two-step smoother (group + graduate). In this case, separate the steps, first group using \code{groupAges()} then use \code{graduate(..., constrain = TRUE)}.
 }
diff --git a/man/graduate_beers.Rd b/man/graduate_beers.Rd
index 2d4618773..5744d038b 100644
--- a/man/graduate_beers.Rd
+++ b/man/graduate_beers.Rd
@@ -26,7 +26,7 @@ A numeric vector of single age data.
 This method offers both ordinary and modified Beers splitting, with an optional \href{https://www.census.gov/data/software/dapps.html}{Demographic Analysis & Population Projection System Software} adjustment \code{johnson} for ages under 10.
 }
 \details{
-Ages should refer to lower age bounds. \code{Value} must be labelled with ages unless \code{Age} is given separately. There must be at least six 5-year age groups (including the open group, 5 otherwise). If you want the \code{johnson} adjustment then \code{Value} must contain a single-year estimate of the population count in age 0. That means \code{Value} must come either as standard abridged or single age data.
+Ages should refer to lower age bounds. \code{Value} must be labeled with ages unless \code{Age} is given separately. There must be at least six 5-year age groups (including the open group, 5 otherwise). If you want the \code{johnson} adjustment then \code{Value} must contain a single-year estimate of the population count in age 0. That means \code{Value} must come either as standard abridged or single age data.
 
 \code{method} option \code{"ord"} conserves sums in 5-year age groups, whereas \code{"mod"} does some smoothing between 5-year age groups too, and is not constrained.
 
diff --git a/man/graduate_grabill.Rd b/man/graduate_grabill.Rd
index beff7cb91..bdd223f93 100644
--- a/man/graduate_grabill.Rd
+++ b/man/graduate_grabill.Rd
@@ -23,7 +23,7 @@ This method uses Grabill's redistribution of middle ages and blends into
 Sprague estimated single-age population counts for the first and final ten ages. Open age groups are preserved, as are annual totals.
 }
 \details{
-Dimension labelling is necessary. There must be at least six age groups (including the open group). One year of data will work as well, as long as it's given as a single-column matrix. Data may be given in either single or grouped ages. If the highest age does not end in a 0 or 5, and \code{OAG == TRUE}, then the open age will be grouped down to the next highest age ending in 0 or 5. If the highest age does not end in a 0 or 5, and \code{OAG == FALSE}, then results extend to single ages covering the entire 5-year age group.
+Dimension labeling is necessary. There must be at least six age groups (including the open group). One year of data will work as well, as long as it's given as a single-column matrix. Data may be given in either single or grouped ages. If the highest age does not end in a 0 or 5, and \code{OAG == TRUE}, then the open age will be grouped down to the next highest age ending in 0 or 5. If the highest age does not end in a 0 or 5, and \code{OAG == FALSE}, then results extend to single ages covering the entire 5-year age group.
 }
 \examples{
 a5 <- as.integer(rownames(pop5_mat))
diff --git a/man/graduate_mono.Rd b/man/graduate_mono.Rd
index ace844684..36637b510 100644
--- a/man/graduate_mono.Rd
+++ b/man/graduate_mono.Rd
@@ -22,7 +22,7 @@ Numeric. vector of single smoothed age counts.
 Take the cumulative sum of \code{Value} and then run a monotonic spline through it. The first differences split back single-age estimates of \code{Value}. Optionally keep the open age group untouched.
 }
 \details{
-The \code{"hyman"} method of \code{stats::splinefun()} is used to fit the spline because 1) it passes exactly through the points, 2) it is monotonic and therefore guarantees positive counts, and 3) it seems to be a bit less wiggly (lower average first differences of split counts). Single-age data is returned as-is. If you want to use this function as a smoother you first need to group to non-single ages.
+The \code{"hyman"} method of \code{stats::splinefun()} is used to fit the spline because 1) it passes exactly through the points, 2) it is monotonic and therefore guarantees positive counts, and 3) it seems to be a bit less wiggly (lower average first differences of split counts). Single-age data is returned as-is. If you want to use this function as a smoother you first need to group to non-single ages. \code{Age} be any age range, it does not need to start at 0.
 }
 \examples{
 Value                <- structure(c(88623, 90842, 93439, 96325, 99281, 102051, 104351,
diff --git a/man/graduate_mono_closeout.Rd b/man/graduate_mono_closeout.Rd
index 90829643a..a841b3cdc 100644
--- a/man/graduate_mono_closeout.Rd
+++ b/man/graduate_mono_closeout.Rd
@@ -55,7 +55,7 @@ grabill.closed.out   <- graduate_mono_closeout(Value = popvec, Age = a5, pops =
 # one may wish to instead rescale results colSums() of
 # popg at age pivotAge and higher.
 sum(grabill.closed.out) - sum(popvec)
-# also works on an age-labelled vector of data
+# also works on an age-labeled vector of data
 closed.vec           <- graduate_mono_closeout(popvec, Age = a5, OAG = TRUE)
 # let's compare this one with sprague()
 simple.vec           <- graduate_sprague(popvec, Age = a5, OAG = TRUE)
diff --git a/man/graduate_pclm.Rd b/man/graduate_pclm.Rd
index 7a26f364e..5e0f8951e 100644
--- a/man/graduate_pclm.Rd
+++ b/man/graduate_pclm.Rd
@@ -26,6 +26,8 @@ This is exactly the function \code{pclm()} from the \code{ungroup} package, exce
 The PCLM method can also be used to graduate rates using an offset if both numerators and denominators are available. In this case \code{Value} is the event count and \code{offset} is person years of exposure. The denominator must match the length of \code{Value} or else the length of the final single age result \code{length(min(Age):OAnew)}.  This method can be used to redistribute counts in the open age group if \code{OAnew} gives sufficient space. Likewise, it can give a rate extrapolation beyond the open age.
 
 If there are 0s in \code{Value}, these are replaced with a small value prior to fitting. If negatives result from the pclm fit, we retry after multiplying \code{Value} by 10, 100, or 1000, as sometimes a temporary rescale for fitting can help performance.
+
+\code{Age} be any age range, it does not need to start at 0.
 }
 \examples{
 a5  <- seq(0,100,by=5)
diff --git a/man/graduate_sprague.Rd b/man/graduate_sprague.Rd
index 45173f286..8162a5d6d 100644
--- a/man/graduate_sprague.Rd
+++ b/man/graduate_sprague.Rd
@@ -22,7 +22,7 @@ Numeric vector of counts split into single ages.
 This method is used to interpolate counts based on the Sprague formula. It is based on the first stage of the Sprague R script prepared by Thomas Buettner and Patrick Gerland, itself based on the description in Siegel and Swanson, 2004, p. 727.
 }
 \details{
-Ages should refer to lower age bounds, ending in the open age group in the last row (not a closed terminal age). Dimension labelling is necessary. There must be at least six age groups (including the open group). One year of data will work as well, as long as it's given as or coercible to a single-column matrix. This method may produce negative values, most likely in the youngest or oldest ages. This case is dealt with in the \code{graduate()} wrapper function but not in this function.
+Ages should refer to lower age bounds, ending in the open age group in the last row (not a closed terminal age). Dimension labeling is necessary. There must be at least six age groups (including the open group). One year of data will work as well, as long as it's given as or coercible to a single-column matrix. This method may produce negative values, most likely in the youngest or oldest ages. This case is dealt with in the \code{graduate()} wrapper function but not in this function.
 
 If the highest age does not end in a 0 or 5, and \code{OAG == TRUE}, then the open age will be grouped down to the next highest age ending in 0 or 5. If the highest age does not end in a 0 or 5, and \code{OAG == FALSE}, then results extend to single ages covering the entire 5-year age group.
 }
diff --git a/man/graduate_uniform.Rd b/man/graduate_uniform.Rd
index 7edc5c588..48c7aacc0 100644
--- a/man/graduate_uniform.Rd
+++ b/man/graduate_uniform.Rd
@@ -24,7 +24,7 @@ Numeric vector of counts for single year age groups.
 Uniformly splits aggregate counts in age groups into single year age groups.
 }
 \details{
-Assumes that the population is uniformly distributed across each age interval, and that initial age intervals are integers greater than or equal to 1. If \code{AgeInt} is given, its final value is used as the interval for the final age group. If \code{AgeInt} is missing, then \code{Age} must be given, and the open age group is by default preserved \code{OAvalue} rather than split. To instead split the final age group into, e.g., a 5-year age class, either give \code{AgeInt}, \emph{or} give \code{Age}, \code{OAG = TRUE}, and \code{OAvalue = 5}.
+Assumes that the population is uniformly distributed across each age interval, and that initial age intervals are integers greater than or equal to 1. If \code{AgeInt} is given, its final value is used as the interval for the final age group. If \code{AgeInt} is missing, then \code{Age} must be given, and the open age group is by default preserved \code{OAvalue} rather than split. To instead split the final age group into, e.g., a 5-year age class, either give \code{AgeInt}, \emph{or} give \code{Age}, \code{OAG = TRUE}, and \code{OAvalue = 5}.  \code{Age} be any age range, it does not need to start at 0.
 }
 \examples{
 MalePop <- c(9544406,7471790,11590109,11881844,11872503,12968350,
diff --git a/man/groupAges.Rd b/man/groupAges.Rd
index 04345a9a0..9ed9c5e63 100644
--- a/man/groupAges.Rd
+++ b/man/groupAges.Rd
@@ -34,7 +34,7 @@ This can be useful to check constrained sums, or as an intermediate step for smo
 }
 \details{
 If you shift the groupings, then the first age groups may have a negative lower bound
-(for example of -5). These counts would be discarded for the osculatory version of Sprague smoothing,
+(for example of -5). These counts would be discarded for the oscillatory version of Sprague smoothing,
 for example, but they are preserved in this function. The important thing to know is that if you shift
 the groups, the first and last groups will not be N years wide. For example if \code{shiftdown} is 1, the first age group is 4-ages wide. The ages themselves are not returned,
 but they are the name attribute of the output count vector. Note this will also correctly group abridged ages
diff --git a/man/interp.Rd b/man/interp.Rd
index 727df227b..f059301b5 100644
--- a/man/interp.Rd
+++ b/man/interp.Rd
@@ -11,6 +11,7 @@ interp(
   method = c("linear", "exponential", "power"),
   power = 2,
   extrap = FALSE,
+  negatives = FALSE,
   ...
 )
 }
@@ -25,7 +26,9 @@ interp(
 
 \item{power}{numeric power to interpolate by, if \code{method = "power"}. Default 2.}
 
-\item{extrap}{logical. In case \code{datesOut} is out of range of datesIn, do extrapolation using slope in extreme pairwise. Deafult \code{FALSE}.}
+\item{extrap}{logical. In case \code{datesOut} is out of range of \code{datesIn}, do extrapolation using slope in extreme pairwise. Default \code{FALSE}.}
+
+\item{negatives}{logical. In case negative output are accepted, set to \code{TRUE}. Default \code{FALSE}.}
 
 \item{...}{arguments passed to \code{stats::approx}. For example, \code{rule}, which controls extrapolation behavior.}
 }
diff --git a/man/interp_coh.Rd b/man/interp_coh.Rd
index ee79198e8..5c2aa5e2e 100644
--- a/man/interp_coh.Rd
+++ b/man/interp_coh.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/interp_coh.R
 \name{interp_coh}
 \alias{interp_coh}
-\title{component-free intercensal cohort interpolation}
+\title{Cohort component intercensal interpolation}
 \usage{
 interp_coh(
   c1,
@@ -60,7 +60,10 @@ interp_coh(
 \item{...}{optional arguments passed to}
 }
 \description{
-Cohorts between two censuses are interpolated flexibly using linear, exponential, or power rules. The lower and upper intercensal triangles are filled using within-age interpolation. This function is experimental and still in development.
+Cohorts between two censuses are interpolated using a cohort component approach.
+}
+\details{
+The basic approach is to i) align the censuses to single-year cohorts by blending adjacent ages assuming that the birthdays in each age group are uniformly distributed through the year ii) decrement the first census forward within cohorts using period-cohort survival probabilities calculated from (supplied or downloaded) \code{l(x)} values, iii) redistribute the residual at the time of the second census uniformly over time within cohorts. These steps are always done on Jan 1 reference dates. If \code{midyear = TRUE}, then we do within-age band arithmetic interpolation to July 1 reference dates.
 }
 \examples{
 
diff --git a/man/interp_lc_lim.Rd b/man/interp_lc_lim.Rd
index 682133e8e..a1728aa04 100644
--- a/man/interp_lc_lim.Rd
+++ b/man/interp_lc_lim.Rd
@@ -23,7 +23,7 @@ interp_lc_lim(
 
 \item{dates_out}{numeric. Vector of decimal years to interpolate or extrapolate.}
 
-\item{Single}{logical. Wheter or not the lifetable output is by single ages.}
+\item{Single}{logical. Whether or not the lifetable output is by single ages.}
 
 \item{dates_e0}{numeric. Vector of decimal years where \code{"e_0"} should be fitted when apply method.}
 
@@ -67,12 +67,12 @@ List with:
 }
 }
 \description{
-Given a data frame with dates, sex and mortality data by age (rates, conditionated probabilities of death
+Given a data frame with dates, sex and mortality data by age (rates, conditioned probabilities of death
 or survival function), this function interpolate/extrapolate life tables
 using the method for limited data suggested by  Li et. al (2004) (at least three observed years).
 }
 \details{
-Based on spreedsheet "Li_2018_Limited_Lee-Carter-v4.xlsm" from UN.
+Based on spreadsheet "Li_2018_Limited_Lee-Carter-v4.xlsm" from UN.
 Useful for abridged or single ages, and allows output in both formats also.
 One option is the use of non-divergent method for sex coherency (Li & Lee, 2005).
 The other is the possibility of fitting \code{"k"} to replicate \code{"e_0"} at some given dates.
diff --git a/man/interp_lc_lim_estimate.Rd b/man/interp_lc_lim_estimate.Rd
index d05a2ec12..a6ade6ffc 100644
--- a/man/interp_lc_lim_estimate.Rd
+++ b/man/interp_lc_lim_estimate.Rd
@@ -2,24 +2,24 @@
 % Please edit documentation in R/interp_lc_lim.R
 \name{interp_lc_lim_estimate}
 \alias{interp_lc_lim_estimate}
-\title{Estimate LC with limited data params}
+\title{Estimate LC with limited data parameters}
 \usage{
 interp_lc_lim_estimate(M, dates_in, dates_out, SVD = F)
 }
 \arguments{
-\item{M}{numeric. Matrix with many rows as ages and columns as dates_in.}
+\item{M}{numeric. Matrix with many rows as ages and columns as \code{dates_in}.}
 
 \item{dates_in}{numeric. Vector of dates with input rates.}
 
 \item{dates_out}{numeric. Vector of dates for estimate a set of rates.}
 
-\item{SVD}{logical. Use Singular Value Decomposition for estimate b and k or Maximum Likelihood Estimation. Default \code{FALSE} for Maximum Likelihood Estimation.}
+\item{SVD}{logical. Use Singular Value Decomposition for estimate \code{b} and \code{k} or Maximum Likelihood Estimation. Default \code{FALSE} for Maximum Likelihood Estimation.}
 }
 \description{
 Estimate LC with limited data from a matrix of rates (age by dates).
 }
 \details{
-SVD for ax and bx. Fit a simmple linear model for k and interp/extrapolate for objective dates.
+SVD for \code{ax} and \code{bx.} Fit a simple linear model for \code{k} and interpolate/extrapolate for objective dates.
 }
 \references{
 \insertRef{Li2004}{DemoTools}
diff --git a/man/interp_lc_lim_group.Rd b/man/interp_lc_lim_group.Rd
index 26c72120a..a7b774f83 100644
--- a/man/interp_lc_lim_group.Rd
+++ b/man/interp_lc_lim_group.Rd
@@ -19,11 +19,11 @@ interp_lc_lim_group(
 }
 \arguments{
 \item{input}{data.frame. Columns: id, Date, Sex, Age, nMx (opt), nqx (opt), lx (opt).
-The first column (id) cn be a numeric index or charcter vector identifying each group.}
+The first column (id) can be a numeric index or character vector identifying each group.}
 
 \item{dates_out}{numeric. Vector of decimal years to interpolate or extrapolate.}
 
-\item{Single}{logical. Wheter or not the lifetable output is by single ages.}
+\item{Single}{logical. Whether or not the lifetable output is by single ages.}
 
 \item{input_e0}{data.frame with cols: id, Date, Sex and \code{"e_0"}. This should be fitted when apply method.}
 
@@ -65,12 +65,12 @@ List with:
 }
 }
 \description{
-Given a data frame with groups (country, region, sex), dates, sex and mortality data by age (rates, conditionated probabilities of death
+Given a data frame with groups (country, region, sex), dates, sex and mortality data by age (rates, conditioned probabilities of death
 or survival function), this function interpolate/extrapolate life tables
 using the method for limited data suggested by  Li et. al (2004) (at least three observed years).
 }
 \details{
-Based on spreedsheet "Li_2018_Limited_Lee-Carter-v4.xlsm" from UN.
+Based on spreadsheet "Li_2018_Limited_Lee-Carter-v4.xlsm" from UN.
 Useful for abridged or single ages, and allows output in both formats also.
 One option is the use of non-divergent method for sex coherency (Li & Lee, 2005).
 The other is the possibility of fitting \code{"k"} to replicate \code{"e_0"} at some given dates.
diff --git a/man/is_age_coherent.Rd b/man/is_age_coherent.Rd
index 9a12912fc..04525278f 100644
--- a/man/is_age_coherent.Rd
+++ b/man/is_age_coherent.Rd
@@ -20,7 +20,7 @@ logical. \code{TRUE} if the arguments are considered consistent.
 A few checks are carried out to test if \code{Age} is internally consistent, that \code{OAG} is consistent with \code{AgeInt}, and that \code{Age} and \code{AgeInt} are consistent with one another. For \code{Age} to be internally consistent, we cannot have redundant values, and values must be sequential.
 }
 \details{
-If \code{OAG} is \code{TRUE} then \code{AgeInt} must be coded as \code{NA}. If \code{Age} is not sorted then we sort both \code{Age} and \code{AgeInt}, assuming that they are in matched order. This isn't incoherence per se, but a message is returned to the console.
+If \code{OAG} is \code{TRUE} then \code{AgeInt} must be coded as \code{NA}. If \code{Age} is not sorted then we sort both \code{Age} and \code{AgeInt}, assuming that they are in matched order. This isn't incoherence in itself, but a message is returned to the console.
 }
 \examples{
 Age    <- 0:99
diff --git a/man/logquad_augmented.Rd b/man/logquad_augmented.Rd
new file mode 100644
index 000000000..af9923a42
--- /dev/null
+++ b/man/logquad_augmented.Rd
@@ -0,0 +1,35 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/log_quad_augm.R
+\name{logquad_augmented}
+\alias{logquad_augmented}
+\title{Augmented logquad}
+\usage{
+logquad_augmented(coeffs, k, q0_5, q60_15, Sex = "b", Age, ...)
+}
+\arguments{
+\item{coeffs}{data.frame. Columns \code{a(x)}, \code{b(x)}, \code{c(x)} and \code{v(x)} from fitted logquad model. See \code{fitted_logquad_b}.}
+
+\item{k}{numeric. Adult mortality related value from log-quad estimatation based on one or two input parameters. See \code{lt_model_lq}.}
+
+\item{q0_5}{numeric. Probability of death from born to age 5.}
+
+\item{q60_15}{numeric. Probability of death from age 60 to age 75.}
+
+\item{Sex}{character. Either male \code{"m"}, female \code{"f"}, or both \code{"b"}.}
+
+\item{Age}{integer. Abridged lower bound ages. Same length than rows in \code{coeffs}.}
+
+\item{...}{Other arguments to be passed on to the \code{lt_abridged} function.}
+}
+\value{
+life table as in \code{lt_abridged} function.
+}
+\description{
+Adjust rates in oldest ages that comes from a HMD model, using an external estimate of 15q60 (Li, 2014). As an example see\code{\link[DemoTools]{HMD_old_logquad}}.
+}
+\details{
+Parameter \code{a(x)} is augmented based on an external estimate of 15q60.
+}
+\references{
+See \href{https://www.un.org/development/desa/pd/content/estimating-life-tables-developing-countries}{Li (2014)}.
+}
diff --git a/man/lt_abridged.Rd b/man/lt_abridged.Rd
index 99801cc57..13c8e76c5 100644
--- a/man/lt_abridged.Rd
+++ b/man/lt_abridged.Rd
@@ -57,7 +57,7 @@ lt_abridged(
 
 \item{mod}{logical. If \code{"un"} specified for \code{axmethod}, whether or not to use Nan Li's modification for ages 5-14. Default \code{TRUE}.}
 
-\item{SRB}{the sex ratio at birth (boys / girls), detault 1.05}
+\item{SRB}{the sex ratio at birth (boys / girls), default 1.05}
 
 \item{OAG}{logical. Whether or not the last element of \code{nMx} (or \code{nqx} or \code{lx}) is an open age group. Default \code{TRUE}.}
 
@@ -109,7 +109,7 @@ starting with the input open age, but you can lower this age using the
 is aligned with the other columns in all 5-year age groups, but note the
 first two values have a slightly different age-interval interpretation:
 In Age 0, the interpretation is survival from birth until interval 0-4.
-In Age 1, it is survival from 0-4 into 5-9. Therafter the age groups align.
+In Age 1, it is survival from 0-4 into 5-9. Thereafter the age groups align.
 This column is required for population projections.
 }
 \examples{
diff --git a/man/lt_abridged2single.Rd b/man/lt_abridged2single.Rd
index d9cbfb378..4c68540fc 100644
--- a/man/lt_abridged2single.Rd
+++ b/man/lt_abridged2single.Rd
@@ -54,7 +54,7 @@ lt_abridged2single(
 
 \item{mod}{logical. If \code{"un"} specified for \code{axmethod}, whether or not to use Nan Li's modification for ages 5-14. Default \code{TRUE}.}
 
-\item{SRB}{the sex ratio at birth (boys / girls), detault 1.05}
+\item{SRB}{the sex ratio at birth (boys / girls), default 1.05}
 
 \item{OAG}{logical. Whether or not the last element of \code{nMx} (or \code{nqx} or \code{lx}) is an open age group. Default \code{TRUE}.}
 
@@ -67,7 +67,7 @@ lt_abridged2single(
 
 \item{extrapFit}{integer vector. Ages to include in model fitting. Defaults to all ages \code{>          =60}.}
 
-\item{...}{optional arguments passed to \code{pclm()}. For example, if you pass an expicit \code{lambda} parameter via the \code{control} argument, you can speed up estimation}
+\item{...}{optional arguments passed to \code{pclm()}. For example, if you pass an explicit \code{lambda} parameter via the \code{control} argument, you can speed up estimation}
 }
 \value{
 Single-year lifetable in data.frame with columns
diff --git a/man/lt_ambiguous.Rd b/man/lt_ambiguous.Rd
index 532075238..1b60868de 100644
--- a/man/lt_ambiguous.Rd
+++ b/man/lt_ambiguous.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/lt_regroup_age.R
 \name{lt_ambiguous}
 \alias{lt_ambiguous}
-\title{calculate an abidged or single age lifetable from abridged or sinlge age data}
+\title{calculate an abridged or single age lifetable from abridged or single age data}
 \usage{
 lt_ambiguous(
   nMx_or_nqx_or_lx = NULL,
diff --git a/man/lt_id_Ll_S.Rd b/man/lt_id_Ll_S.Rd
index 6baabc54e..5f2ee3b55 100644
--- a/man/lt_id_Ll_S.Rd
+++ b/man/lt_id_Ll_S.Rd
@@ -4,19 +4,21 @@
 \alias{lt_id_Ll_S}
 \title{Calculate survivor ratios}
 \usage{
-lt_id_Ll_S(nLx, lx, AgeInt, N = c(5, 1))
+lt_id_Ll_S(nLx, lx = NULL, Age, AgeInt = NULL, N = 5)
 }
 \arguments{
 \item{nLx}{numeric vector of lifetable exposure.}
 
-\item{lx}{numeric. Vector of lifetable survivorship at abridged ages.}
+\item{lx}{numeric vector of lifetable survivors from same lifetable than \code{nLx}. Infered radix from nLx in case is \code{NULL}.}
 
-\item{AgeInt}{integer. Vector of age class widths. Default \code{inferAgeIntAbr(Age = Age)}.}
+\item{Age}{integer vector of starting ages.}
+
+\item{AgeInt}{integer vector of age intervals.}
 
 \item{N}{integer, the age width for survivor ratios, either 5 or 1. Default 5.}
 }
 \description{
-An extra lifetable column for use in projections, which require uniform time steps both both age and period. Intervals are either single age (\code{N=1}) or five-year ages (\code{N=5}). Input vectors are assumed to come from either single or standard abridged ages.
+An extra lifetable column for use in projections, which require uniform time steps both both age and period. Intervals are either single age or five-year ages. Input vectors are assumed to come from either single or standard abridged ages. Note that the ages of the output Sx are the ages the population would be after the N-year projection.
 }
 \details{
 This function does not account for \code{nLx} having been pre-binned into uniform 5-year age widths, which will throw an error. Just leave them in abridged ages instead. Note that in the case of abridged ages, the interpretation for the first and second value don't follow the original abridged age intervals: the first value in the probability of surviving from birth into ages 0-4 in the first five years, and the second value is the probability of surviving from 0-4 to 5-9. This represents a slight misalignment with the rest of the lifetable, user beware.
diff --git a/man/lt_id_l_q.Rd b/man/lt_id_l_q.Rd
index 93d6f8e3c..94fbba82e 100644
--- a/man/lt_id_l_q.Rd
+++ b/man/lt_id_l_q.Rd
@@ -10,15 +10,14 @@ lt_id_l_q(lx)
 \item{lx}{numeric.  Vector of age-specific lifetable survivorship.}
 }
 \value{
-ndx vector of lifetable deaths.
+\code{qx} values of age-specific mortality rates.  The last value is always 1.0
 }
 \description{
 This lifetable identity is the same no matter what kind of lifetable is required.
 You can find it in any demography textbook.
 }
 \details{
-The vector returned is the same length as \code{lx} and it sums to the lifetable radix.
-If the radix is one then this is the discrete deaths distribution.
+The vector returned is the same length as \code{lx}.
 }
 \references{
 \insertRef{preston2000demography}{DemoTools}
diff --git a/man/lt_model_lq.Rd b/man/lt_model_lq.Rd
index 23919935d..8b541644f 100644
--- a/man/lt_model_lq.Rd
+++ b/man/lt_model_lq.Rd
@@ -70,7 +70,7 @@ for case 7 and 8 (e0 and 45q15 or 35q15 are known);}
 
 \item{mod}{logical. If \code{"un"} specified for \code{axmethod}, whether or not to use Nan Li's modification for ages 5-14. Default \code{TRUE}.}
 
-\item{SRB}{the sex ratio at birth (boys / girls), detault 1.05}
+\item{SRB}{the sex ratio at birth (boys / girls), default 1.05}
 }
 \value{
 The output is of class \code{lt_model_lq} with the components:
@@ -79,7 +79,7 @@ The output is of class \code{lt_model_lq} with the components:
 and \code{e0}.}
 }
 \description{
-Construct model life tables based on the Log-Quadratic (wilmoth) estimates
+Construct model life tables based on the Log-Quadratic (Wilmoth) estimates
 with various choices of 2 input parameters:
 \code{q0_5, q0_1, q15_45, q15_35} and \code{e0}. There are 8 possible
 combinations (see examples below).
@@ -90,7 +90,7 @@ of dying \code{nqx} is written \code{qx_n}, where \code{x} and \code{n} are
 integers. For example \code{45q15} is represented as \code{q45_15}.
 }
 \note{
-This function is ported from \code{MortalityEstimate::wilmothLT} experimental package by Marius Pascariu. The package is no longe maintained. The latest version can be found here: \url{https://github.com/mpascariu/MortalityEstimate}
+This function is ported from \code{MortalityEstimate::wilmothLT} experimental package by Marius Pascariu. The package is no longer maintained. The latest version can be found here: \url{https://github.com/mpascariu/MortalityEstimate}
 }
 \examples{
 
diff --git a/man/lt_rule_1a0.Rd b/man/lt_rule_1a0.Rd
index a5c2e749a..059574ba1 100644
--- a/man/lt_rule_1a0.Rd
+++ b/man/lt_rule_1a0.Rd
@@ -27,13 +27,13 @@ lt_rule_1a0(
 
 \item{region}{character. \code{"n"}, \code{"e"}, \code{"s"} or \code{"w"} for North, East, South, or West.}
 
-\item{SRB}{the sex ratio at birth (boys / girls), detault 1.05}
+\item{SRB}{the sex ratio at birth (boys / girls), default 1.05}
 }
 \value{
 a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values.
 }
 \description{
-This function wraps the Coale-Demeny and Andreev-Kingkade approximations for a0, which can come from M0, qo, or IMR.
+This function wraps the Coale-Demeny and Andreev-Kingkade approximations for \code{a0}, which can come from \code{M0}, \code{q0}, or \code{IMR.}
 }
 \details{
 If sex is given as both, \code{"b"}, then we calculate the male and female results separately, then weight them together using SRB. This is bad in theory, but the leverage is trivial, and it's better than using male or female coefs for the total population.
diff --git a/man/lt_rule_1a0_ak.Rd b/man/lt_rule_1a0_ak.Rd
index c036c0d5c..9f7539b4b 100644
--- a/man/lt_rule_1a0_ak.Rd
+++ b/man/lt_rule_1a0_ak.Rd
@@ -7,11 +7,11 @@
 lt_rule_1a0_ak(M0 = NULL, q0 = NULL, Sex)
 }
 \arguments{
-\item{M0}{a value or vector of values of m0, the death probability in the first year of life.}
+\item{M0}{a value or vector of values of `1m0``, the death risk in the first year of life.}
 
-\item{q0}{a value or vector of values of m0, the death risk in the first year of life.}
+\item{q0}{a value or vector of values of `1q0``, the death probability in the first year of life, sometimes approximated with IMR.}
 
-\item{Sex}{either "m" or "f"}
+\item{Sex}{either \code{"m"} or \code{"f"}}
 }
 \value{
 a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values.
diff --git a/man/lt_rule_4m0_D0.Rd b/man/lt_rule_4m0_D0.Rd
index 347ca5834..6d7bea4dd 100644
--- a/man/lt_rule_4m0_D0.Rd
+++ b/man/lt_rule_4m0_D0.Rd
@@ -4,7 +4,7 @@
 \alias{lt_rule_4m0_D0}
 \title{rule of thumb for splitting infants from deaths under 5}
 \usage{
-lt_rule_4m0_D0(D04, M04, P04, Sex = c("m", "f"))
+lt_rule_4m0_D0(D04, M04, P04, Sex = "m")
 }
 \arguments{
 \item{D04}{numeric. Deaths under age 5.}
diff --git a/man/lt_rule_4m0_m0.Rd b/man/lt_rule_4m0_m0.Rd
index 40269ae43..89fc85768 100644
--- a/man/lt_rule_4m0_m0.Rd
+++ b/man/lt_rule_4m0_m0.Rd
@@ -4,7 +4,7 @@
 \alias{lt_rule_4m0_m0}
 \title{rule of thumb for estimating infant mortality rate from under 5 mortality}
 \usage{
-lt_rule_4m0_m0(M04, D04, P04, Sex = c("m", "f"))
+lt_rule_4m0_m0(M04, D04, P04, Sex = "m")
 }
 \arguments{
 \item{M04}{numeric. Death rate under age 5.}
diff --git a/man/lt_rule_ak_q0_a0.Rd b/man/lt_rule_ak_q0_a0.Rd
index d1611408f..683130cbf 100644
--- a/man/lt_rule_ak_q0_a0.Rd
+++ b/man/lt_rule_ak_q0_a0.Rd
@@ -15,5 +15,5 @@ lt_rule_ak_q0_a0(q0, Sex)
 a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of a_0 values.
 }
 \description{
-\code{AKq02a0} Andreev Kingkade a0 method. This version has a 3-part segemented linear model, based on cutpoints in q0. Code ported from HMDLifeTables.
+\code{AKq02a0} Andreev Kingkade a0 method. This version has a 3-part segmented linear model, based on cut points in q0. Code ported from HMDLifeTables.
 }
diff --git a/man/lt_rule_m_extrapolate.Rd b/man/lt_rule_m_extrapolate.Rd
index 129763202..bcd4668f0 100644
--- a/man/lt_rule_m_extrapolate.Rd
+++ b/man/lt_rule_m_extrapolate.Rd
@@ -45,13 +45,16 @@ The following options are available: \itemize{
 \value{
 An object of class \code{lt_rule_m_extrapolate} with the following components:
 \item{input}{List with arguments provided in input. Saved for convenience.}
-\item{call}{An unevaluated function call, that is, an unevaluated expressionwhich consists of the named function applied to the given arguments.}
+\item{call}{An unevaluated function call, that is, an unevaluated expression that consists of the named function applied to the given arguments.}
 \item{fitted.model}{An object of class \code{\link[MortalityLaws]{MortalityLaw}}. Here one can find fitted values, residuals, goodness of fit measures etc.}
 \item{values}{A vector or matrix containing the complete mortality data, that is the modified input data following the extrapolation procedure.}
 }
 \description{
 Extrapolate old-age human mortality curve using mortality laws
 }
+\details{
+If fitting fails to converge, then we refit assuming Gompertz mortality with explicit starting parameters of \code{parS = c(A = 0.005, B = 0.13)} and a warning is issued.
+}
 \examples{
 # Example 1 - abridged data
 
@@ -70,7 +73,8 @@ f1 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "kannisto")
 f2 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "kannisto_makeham")
 f3 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "gompertz")
 f4 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "ggompertz")
-f5 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "makeham")
+ # makeham falls back to gompertz for this data
+suppressWarnings(f5 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "makeham"))
 f6 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "beard")
 f7 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "beard_makeham")
 f8 <- lt_rule_m_extrapolate(mx, x, x_fit, x_extr, law = "quadratic")
@@ -142,17 +146,6 @@ legend("topleft", bty = "n",
        lty = c(NA, NA, 1:8), pch = c(16, 16, rep(NA, 8)),
        col = c(1, 4, 2:9), lwd = 2, pt.cex = 2)
 }
-# ----------------------------------------------
-# Example 3 - Extrapolate mortality for multiple years at once
-
-# Create some data
-mx_matrix <- matrix(rep(mx1, 3), ncol = 3) \%*\% diag(c(1, 1.05, 1.1))
-dimnames(mx_matrix) <- list(age = x1, year = c("year1", "year2", "year3"))
-
-F1 <- lt_rule_m_extrapolate(mx_matrix, x = x1, x_fit, x_extr, law = "kannisto")
-F1
-ls(F1)
-coef(F1)
 }
 \seealso{
 \code{\link[MortalityLaws]{MortalityLaw}}
diff --git a/man/lt_single_mx.Rd b/man/lt_single_mx.Rd
index 4f7ea4b7c..9a3232c72 100644
--- a/man/lt_single_mx.Rd
+++ b/man/lt_single_mx.Rd
@@ -39,7 +39,7 @@ lt_single_mx(
 
 \item{mod}{logical. If \code{"un"} specified for \code{axmethod}, whether or not to use Nan Li's modification for ages 5-14. Default \code{TRUE}.}
 
-\item{SRB}{the sex ratio at birth (boys / girls), detault 1.05}
+\item{SRB}{the sex ratio at birth (boys / girls), default 1.05}
 
 \item{OAG}{logical. Whether or not the last element of \code{nMx} (or \code{nqx} or \code{lx}) is an open age group. Default \code{TRUE}.}
 
diff --git a/man/lt_single_qx.Rd b/man/lt_single_qx.Rd
index 54d90efe5..121f99b01 100644
--- a/man/lt_single_qx.Rd
+++ b/man/lt_single_qx.Rd
@@ -39,7 +39,7 @@ lt_single_qx(
 
 \item{mod}{logical. If \code{"un"} specified for \code{axmethod}, whether or not to use Nan Li's modification for ages 5-14. Default \code{TRUE}.}
 
-\item{SRB}{the sex ratio at birth (boys / girls), detault 1.05}
+\item{SRB}{the sex ratio at birth (boys / girls), default 1.05}
 
 \item{OAG}{logical. Whether or not the last element of \code{nMx} (or \code{nqx} or \code{lx}) is an open age group. Default \code{TRUE}.}
 
@@ -66,7 +66,7 @@ Lifetable in data.frame with columns
 \item{lx}{numeric. Lifetable survivorship}
 \item{ndx}{numeric. Lifetable deaths distribution.}
 \item{nLx}{numeric. Lifetable exposure.}
-\item{Sx}{numeric. Survivor ratios in uniform 5-year age groups.}
+\item{Sx}{numeric. Survivor ratios in uniform single-year age groups.}
 \item{Tx}{numeric. Lifetable total years left to live above age x.}
 \item{ex}{numeric. Age-specific remaining life expectancy.}
 }
diff --git a/man/lt_smooth_ambiguous.Rd b/man/lt_smooth_ambiguous.Rd
index beff389bc..c1df4397e 100644
--- a/man/lt_smooth_ambiguous.Rd
+++ b/man/lt_smooth_ambiguous.Rd
@@ -13,7 +13,7 @@ lt_smooth_ambiguous(input, ...)
 }
 \description{
 Considering different mortality input for each sex/year data,
-smooth olders with makeham or kannisto in case no law was specified,
+smooth older ages with makeham or kannisto in case no law was specified,
 and return a data.frame with standard LT.
 }
 \details{
diff --git a/man/lthat.logquad.Rd b/man/lthat.logquad.Rd
index 180a06ca2..36810d5ac 100644
--- a/man/lthat.logquad.Rd
+++ b/man/lthat.logquad.Rd
@@ -47,7 +47,7 @@ The following options are available: \itemize{
 
 \item{mod}{logical. If \code{"un"} specified for \code{axmethod}, whether or not to use Nan Li's modification for ages 5-14. Default \code{TRUE}.}
 
-\item{SRB}{the sex ratio at birth (boys / girls), detault 1.05}
+\item{SRB}{the sex ratio at birth (boys / girls), default 1.05}
 }
 \description{
 Estimated life table using the log-quadratic model
diff --git a/man/mA_swe.Rd b/man/mA_swe.Rd
index 56b227515..f47c3582a 100644
--- a/man/mA_swe.Rd
+++ b/man/mA_swe.Rd
@@ -8,7 +8,7 @@
 A data frame with:
 \describe{
 \item{Date}{Reference time for the rates estimate.}
-\item{Age}{Inferior age for abridged groups. Carefull: last age 100 is not an OAG}
+\item{Age}{Inferior age for abridged groups. Careful: last age 100 is not an OAG}
 \item{Sex}{Male \code{m} and female \code{m}.}
 \item{nMx}{Mortality rates.}
 }
diff --git a/man/mav.Rd b/man/mav.Rd
index 10f785be1..78106f0c0 100644
--- a/man/mav.Rd
+++ b/man/mav.Rd
@@ -4,7 +4,7 @@
 \alias{mav}
 \title{Calculate the moving average (mav) over 3 or 5 years.}
 \usage{
-mav(Value, Age, n = 3, OAG = TRUE)
+mav(Value, Age, n = 3, OAG = TRUE, tails = FALSE)
 }
 \arguments{
 \item{Value}{numeric. A vector of demographic counts in single age groups.}
@@ -14,6 +14,10 @@ mav(Value, Age, n = 3, OAG = TRUE)
 \item{n}{integer. A single number, (often 3 or 5), indicating the number of years taken to smooth the population distribution by single ages.}
 
 \item{OAG}{logical. Whether or not the top age group is open. Default \code{TRUE}.}
+
+\item{tails}{logical. If set to \code{TRUE}, smaller-n moving averages are applied on both tails
+such that all values are non-NA. If \code{FALSE} (default), tails are set to NA
+due to the lag of moving averages.}
 }
 \value{
 Vector with the smoothed demographic counts.
@@ -22,9 +26,9 @@ Vector with the smoothed demographic counts.
 This arithmetic smoothing technique aims to eliminate irregularities of the population pyramid by averaging values in a moving window of user-defined width.
 }
 \details{
-The moving window is applied symmetrically. Data endpoints are imputed with \code{NA}s in output: the is nothing under 0 or over the highest closed age group to average with. The open age group is imputed with \code{NA} prior to calculations, since it cannot be averaged into the next lowest group. For example, for \code{n=3}, age 0 will be \code{NA}, as will the open age group and next lowest age. Age intervals are assumed uniform. This function could be used with either single or 5-year age groups.
+The moving window is applied symmetrically. By default (\code{tails = FALSE}) data endpoints are imputed with \code{NA}s in output: the is nothing under 0 or over the highest closed age group to average with. The open age group is not used in averaging, and it is returned as-is. Age intervals are assumed uniform. This function could be used with either single or 5-year age groups.
 
-Ages may be single or grouped, but all age intervals are assumed equal.
+If \code{tails} is set to \code{TRUE}, then tails have been imputed using moving averages with successively smaller values of \code{n}, the cascade method.
 }
 \examples{
 Pop  <-c(303583,390782,523903,458546,517996,400630,485606,325423,471481,189710,
@@ -37,8 +41,15 @@ Pop  <-c(303583,390782,523903,458546,517996,400630,485606,325423,471481,189710,
 Age  <- 0:70
 # final age group assumed open
 mav(Pop, n = 3, Age = Age)
+
 \dontrun{
-nwindows <- sapply(seq(3, 11, by = 2),mav, Value = Pop, Age = Age)
+ odds <- seq(3, 11, by = 2)
+nwindows <- sapply(odds,
+                   mav, 
+                   Value = Pop, 
+                   Age = Age,
+                   OAG = TRUE,
+                   tails = FALSE)
 cols     <- gray(seq(.8, 0, length = 5))
 lwds     <- seq(3, 1, length = 5)
 plot(Age,Pop, col = "red", xlab = "Age", ylab = "The counts", pch=16,
@@ -50,10 +61,31 @@ legend("topright",
 		lwd = lwds,
 		legend = paste0("n=",seq(3,11,by=2)))
 }
+
+# For cascading smoothing on the tails:
+mav(Pop, Age, tails = TRUE)
+
+\dontrun{
+# Compare
+nwindows_tails <- sapply(odds,
+                         mav, 
+                         Value = Pop, 
+                         Age = Age, 
+                         OAG = TRUE, 
+                         tails = TRUE)
+
+colnames(nwindows)        <- odds
+colnamaes(nwindows_tails) <- odds
+
+# NA triangles are completed with
+# successively smaller ns.
+head(nwindows)
+head(nwindows_tails)
+
+tail(nwindows)
+tail(nwindows_tails)
+}
 }
 \references{
 \insertRef{GDA1981IREDA}{DemoTools}
 }
-\author{
-Juan Galeano
-}
diff --git a/man/mig_beta.Rd b/man/mig_beta.Rd
index 31fdf791f..e7b8e7d91 100644
--- a/man/mig_beta.Rd
+++ b/man/mig_beta.Rd
@@ -22,6 +22,11 @@ mig_beta(
   sex = "both",
   midyear = FALSE,
   verbose = TRUE,
+  child_adjust = c("none", "cwr", "constant"),
+  childage_max = NULL,
+  cwr_factor = 0.3,
+  oldage_adjust = c("none", "beers", "mav"),
+  oldage_min = 65,
   ...
 )
 }
@@ -58,6 +63,32 @@ mig_beta(
 
 \item{verbose}{logical. Shall we send informative messages to the console?}
 
+\item{child_adjust}{The method with which to adjust the youngest age groups.
+If \code{"none"}, no adjustment is applied (default). If
+child-woman ratio (\code{"cwr"}) is chosen, the first cohorts reflecting the
+difference between \code{date2 - date1} are adjusted (plus age 0). If
+child constant ratio (\code{"constant"}) is chosen, the first 15 age groups
+are adjusted.}
+
+\item{childage_max}{The maximum age from which to apply \code{child_adjust}.
+By default, set to \code{NULL}, which gets translated into all the cohorts
+between \code{date2} and \code{date1}. If \code{date2} is 2010 and
+\code{date1} is 2002, the first 8 cohorts are adjusted. Otherwise, the user
+can supply an integer.}
+
+\item{cwr_factor}{A numeric between 0 and 1 to which adjust the CWR method
+for the young ages from \code{child_adjust}. \strong{This is only used
+when \code{child_adjust} is \code{"cwr"}}.}
+
+\item{oldage_adjust}{The type of adjustment to apply to ages at and above
+\code{oldage_min}. \code{'beers'} applies a beers graduation method
+while \code{'mav'} applies a moving average with cascading on the tails.
+For more information see \code{?mav} and \code{?graduation_beers}.}
+
+\item{oldage_min}{The minimum age from which to apply \code{oldage_adjust}.
+By default, set to 65, so any adjustment from \code{oldage_adjust} will be
+applied for 65+.}
+
 \item{...}{optional arguments passed to \code{lt_single_qx}}
 }
 \value{
@@ -71,11 +102,13 @@ Census - Estimate by age from projection. It then distributes the NCE over
 the cohort parallelogram assuming uniform distribution assuming it is all
 migration. It finalizes by summing the estimate by age groups across the entire
 intercensal period to have a total migration during the entire period.
+Alternatively, a child adjustment and an old age adjustment can be applied.
 }
 \examples{
 
 \dontrun{
-  mig_beta(
+
+mig_beta(
   location = "Russian Federation",
   sex = "male",
   c1 = pop1m_rus2002,
diff --git a/man/mig_calculate_rc.Rd b/man/mig_calculate_rc.Rd
index 1c60f91da..c56805242 100644
--- a/man/mig_calculate_rc.Rd
+++ b/man/mig_calculate_rc.Rd
@@ -17,7 +17,7 @@ Choose between a 7,9,11 or 13 parameter model.
 }
 \details{
 In the full 13 parameter model, the migration rate at age x, \eqn{m(x)} is defined as
-\deqn{m(x) = a1*exp(-1*alpha1*x) + a2*exp(-1*alpha2*(x - mu2) - exp(-1*lambda2*(x - mu2))) + a3*exp(-1*alpha3*(x - 3) - exp(-1*lambda3*(x - mu3))) + a4*exp(lambda4*x) + c}
+\deqn{m(x) = a1*exp(-1*alpha1*x) + a2*exp(-1*alpha2*(x - mu2) - exp(-1*lambda2*(x - mu2))) + a3*exp(-1*alpha3*(x - mu3) - exp(-1*lambda3*(x - mu3))) + a4*exp(lambda4*x) + c}
 
 The first, second, third and fourth pieces of the equation represent pre-working age, working age, retirement and post-retirement age patterns, respectively.
 Models with less parameters gradually remove terms at the older ages. Parameters in each family are:
diff --git a/man/mig_un_fam.Rd b/man/mig_un_fam.Rd
index 72b2665d9..e23dad504 100644
--- a/man/mig_un_fam.Rd
+++ b/man/mig_un_fam.Rd
@@ -7,11 +7,11 @@
 mig_un_fam(NM, family, Single = TRUE, OAnew = 100)
 }
 \arguments{
-\item{NM}{numeric. Total net migration to distribuite between ages and sex.}
+\item{NM}{numeric. Total net migration to distribute between ages and sex.}
 
 \item{family}{character. Could be "Family", "Female Labor", "Male Labor".}
 
-\item{Single}{logical. Results by simple age. Default \code{FALSE}.
+\item{Single}{logical. Results by simple age. Default \code{TRUE}.
 Typically from pre-working age and working age parts of in Roger-Castro formula.}
 
 \item{OAnew}{The age from which to group all ages into an open ended age group.
diff --git a/man/pop1m_ind.Rd b/man/pop1m_ind.Rd
index 86aa9242e..0c57fbae2 100644
--- a/man/pop1m_ind.Rd
+++ b/man/pop1m_ind.Rd
@@ -3,7 +3,7 @@
 \docType{data}
 \name{pop1m_ind}
 \alias{pop1m_ind}
-\title{Indian male population 1991}
+\title{Indian male population 1971}
 \format{
 A numeric vector of length 101
 }
@@ -16,6 +16,6 @@ Part II-C(ii), New Delhi
 pop1m_ind
 }
 \description{
-Indian male population 1991
+Indian male population 1971
 }
 \keyword{datasets}
diff --git a/man/pop_f_mat_five.Rd b/man/pop_f_mat_five.Rd
index 3780519ee..223efc7cf 100644
--- a/man/pop_f_mat_five.Rd
+++ b/man/pop_f_mat_five.Rd
@@ -8,7 +8,7 @@
 A matrix of dimensions 21 x 21
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 pop_f_mat_five
diff --git a/man/pop_f_mat_single.Rd b/man/pop_f_mat_single.Rd
index 406f57146..685881d14 100644
--- a/man/pop_f_mat_single.Rd
+++ b/man/pop_f_mat_single.Rd
@@ -8,7 +8,7 @@
 A matrix of dimensions 101 x 21
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 pop_f_mat_single
diff --git a/man/pop_m_mat_five.Rd b/man/pop_m_mat_five.Rd
index 01b693491..408873743 100644
--- a/man/pop_m_mat_five.Rd
+++ b/man/pop_m_mat_five.Rd
@@ -8,7 +8,7 @@
 A matrix of dimensions 21 x 21
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 pop_m_mat_five
diff --git a/man/pop_m_mat_single.Rd b/man/pop_m_mat_single.Rd
index f9d0d9e4e..8f5781e5f 100644
--- a/man/pop_m_mat_single.Rd
+++ b/man/pop_m_mat_single.Rd
@@ -8,7 +8,7 @@
 A matrix of dimensions 101 x 21
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 pop_m_mat_single
diff --git a/man/smooth_age_5.Rd b/man/smooth_age_5.Rd
index 59e401326..a249b1e02 100644
--- a/man/smooth_age_5.Rd
+++ b/man/smooth_age_5.Rd
@@ -13,7 +13,7 @@ smooth_age_5(
   ageMin = 10,
   ageMax = 65,
   n = 3,
-  young.tail = c("Original", "Arriaga", "Strong", NA),
+  young.tail = c("Original", "Arriaga", "Strong", "Cascade", NA),
   old.tail = young.tail
 )
 }
@@ -22,7 +22,7 @@ smooth_age_5(
 
 \item{Age}{integer vector of ages corresponding to the lower integer bound of the counts.}
 
-\item{method}{character string. Options include \code{"Carrier-Farrag"},\code{"Arriaga"},\code{"KKN"},\code{"United Nations"}, \code{"Strong"}, and \code{"Zigzag"}. See details. Default \code{"Carrier-Farrag"}.}
+\item{method}{character string. Options include \code{"Carrier-Farrag"},\code{"Arriaga"},\code{"KKN"},\code{"United Nations"}, \code{"Strong"}, \code{MAV} and \code{"Zigzag"}. See details. Default \code{"Carrier-Farrag"}.}
 
 \item{OAG}{logical. Whether or not the top age group is open. Default \code{TRUE}.}
 
@@ -41,7 +41,7 @@ numeric vector of smoothed counts in 5-year age groups.
 }
 \description{
 Smooth population counts in 5-year age groups using the Carrier-Farrag,
-Karup-King-Newton, Arriaga, United Nations, Stong, or Zigzag methods. Allows for imputation
+Karup-King-Newton, Arriaga, United Nations, Strong, MAV or Zigzag methods. Allows for imputation
 of values in the youngest and oldest age groups for the Carrier-Farrag, Karup-King-Newton,
 and United Nations methods.
 }
@@ -54,8 +54,8 @@ operate based on 10-year age group totals, excluding the open age group.
 
 The Carrier-Farrag, Karup-King-Newton, and United Nations methods do not produce estimates
 for the first and final 10-year age groups. By default, these are imputed with the original 5-year age group totals, but
-you can also specify to impute with \code{NA}, or the results of the Arriaga or
-Strong methods. If the terminal digit of the open age group is 5, then the terminal 10-year
+you can also specify to impute with \code{NA}, or the results of the Arriaga,
+Strong and Cascade methods. If the terminal digit of the open age group is 5, then the terminal 10-year
 age group shifts down, so imputations may affect more ages in this case. Imputation can follow
 different methods for young and old ages.
 
diff --git a/man/smooth_age_5_mav.Rd b/man/smooth_age_5_mav.Rd
index 42f1094f1..f5871706b 100644
--- a/man/smooth_age_5_mav.Rd
+++ b/man/smooth_age_5_mav.Rd
@@ -4,7 +4,7 @@
 \alias{smooth_age_5_mav}
 \title{Smooth in 5-year age groups using a moving average}
 \usage{
-smooth_age_5_mav(Value, Age, OAG = TRUE, n = 3)
+smooth_age_5_mav(Value, Age, OAG = TRUE, n = 3, tails = FALSE)
 }
 \arguments{
 \item{Value}{numeric vector of (presumably) counts in 5-year age groups.}
@@ -14,6 +14,9 @@ smooth_age_5_mav(Value, Age, OAG = TRUE, n = 3)
 \item{OAG}{logical. Whether or not the top age group is open. Default \code{TRUE}.}
 
 \item{n}{integer. The width of the moving average. Default 3 intervals (x-5 to x+9).}
+
+\item{tails}{logical. If tails is \code{FALSE}, both tails are left untouched.
+Otherwise, the tails are filled out using a cascade method.}
 }
 \value{
 numeric vector of smoothed counts in 5-year age groups.
@@ -24,7 +27,10 @@ Smooth data in 5-year age groups.
 \details{
 This function calls \code{smooth_age_5_zigzag_inner()}, but prepares data in a way consistent with other methods called by \code{smooth_age_5()}. It is probably preferable to call \code{zigzag()} from the top level, or else call this method from \code{agesmth()} for more control over tail imputations.
 
-This function calls \code{mav()}, which itself relies on the more general \code{ma()}. We lose the lowest and highest ages with this method, unless \code{n=1}, in which case data is returned in the original 5-year age groups. The total population count is not constrained to sum to the orignal total.
+If tails is set to \code{FALSE}, this function calls \code{mav()}, which itself relies on the more general \code{ma()}. We lose the lowest and highest ages with this method, unless \code{n=1}, in which case data is returned in the original 5-year age groups. The total population count is not constrained to sum to the original total.
+
+If tails is \code{TRUE}, the same results are expected but the tails are
+filled in using a cascading method.
 }
 \examples{
 Age <- c(0,1,seq(5,90,by=5))
diff --git a/man/sr_f_mat_five.Rd b/man/sr_f_mat_five.Rd
index 1fe523133..20fee5116 100644
--- a/man/sr_f_mat_five.Rd
+++ b/man/sr_f_mat_five.Rd
@@ -8,7 +8,7 @@
 A matrix of dimensions 21 x 20
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 sr_f_mat_five
diff --git a/man/sr_f_mat_single.Rd b/man/sr_f_mat_single.Rd
index 2b11cb431..1f8ca2604 100644
--- a/man/sr_f_mat_single.Rd
+++ b/man/sr_f_mat_single.Rd
@@ -8,7 +8,7 @@
 A matrix of dimensions 101 x 20
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 sr_f_mat_single
diff --git a/man/sr_m_mat_five.Rd b/man/sr_m_mat_five.Rd
index 1d70cae7f..ce8f57874 100644
--- a/man/sr_m_mat_five.Rd
+++ b/man/sr_m_mat_five.Rd
@@ -8,7 +8,7 @@
 A matrix of dimensions 21 x 20
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 sr_m_mat_five
diff --git a/man/sr_m_mat_single.Rd b/man/sr_m_mat_single.Rd
index 7ae3b16e7..5eaa95a6d 100644
--- a/man/sr_m_mat_single.Rd
+++ b/man/sr_m_mat_single.Rd
@@ -8,7 +8,7 @@
 A matrix of dimensions 101 x 20
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 sr_m_mat_single
diff --git a/man/srb_vec_five.Rd b/man/srb_vec_five.Rd
index fb24db727..127635dca 100644
--- a/man/srb_vec_five.Rd
+++ b/man/srb_vec_five.Rd
@@ -8,7 +8,7 @@
 A vector of length 20
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 srb_vec_five
diff --git a/man/srb_vec_single.Rd b/man/srb_vec_single.Rd
index 43d246020..9728816f7 100644
--- a/man/srb_vec_single.Rd
+++ b/man/srb_vec_single.Rd
@@ -8,7 +8,7 @@
 A vector of length 20
 }
 \source{
-Migration residual PAS spreadhseet
+Migration residual PAS spreadsheet
 }
 \usage{
 srb_vec_single
diff --git a/tests/testthat/test-OPAG.R b/tests/testthat/test-OPAG.R
index c41a5ed47..025d771e1 100644
--- a/tests/testthat/test-OPAG.R
+++ b/tests/testthat/test-OPAG.R
@@ -153,21 +153,22 @@ r <-0
 age_Lx     <- c(0,1,seq(5,100,by=5))
 names(Lx)  <- age_Lx
 
-test_that("OPAG_nLx_warp_r works", {
-  res_stationary <- OPAG_nLx_warp_r(
-    nLx = c(Lx),
-    Age = age_Lx,
-    r = r,
-    continuous = TRUE,
-    method = "uniform"
-  )
-  c_St <- res_stationary/sum(res_stationary)
-  c_Lx <- c(Lx/sum(Lx))
-
-  res <- (c_St - c_Lx) %>% abs() %>% max()
-  expect_true(res < 0.0001)
-  }
-)
+# TR: this test on hold: now OPAG_nLx_warp_r uses single age only
+# test_that("OPAG_nLx_warp_r works", {
+#   res_stationary <- OPAG_nLx_warp_r(
+#     nLx = c(Lx),
+#     Age = age_Lx,
+#     r = r,
+#     continuous = TRUE,
+#     method = "uniform"
+#   )
+#   c_St <- res_stationary/sum(res_stationary)
+#   c_Lx <- c(Lx/sum(Lx))
+# 
+#   res <- (c_St - c_Lx) %>% abs() %>% max()
+#   expect_true(res < 0.0001)
+#   }
+# )
 
 
 # Pop_fit was generated this way
@@ -259,15 +260,16 @@ test_that("Age intervals of standard population and population still works even
     
   
   expect_output(OPAG(Pop,
-                                     Age_Pop = Age_Pop,
-                                     AgeInt_Pop = AgeInt_Pop,
-                                     nLx = nLx,
-                                     Age_nLx = Age_nLx,
-                                     AgeInt_nLx,
-                                     Age_fit =  c(60,70),
-                                     AgeInt_fit = c(10,10),
-                                     Redistribute_from = 80,
-                                     OAnew = max(Age_nLx)), regexp = "\nAge_Pop and Age_nLx age intervals are different!")
+                Age_Pop = Age_Pop,
+                #AgeInt_Pop = AgeInt_Pop,
+                nLx = nLx,
+                Age_nLx = Age_nLx,
+                # AgeInt_nLx,
+                Age_fit =  c(60,70),
+                AgeInt_fit = c(10,10),
+                Redistribute_from = 80,
+                OAnew = max(Age_nLx),
+                method = "mono"), regexp = "\nAge_Pop and Age_nLx age intervals are different!")
 })
 
 
@@ -276,10 +278,10 @@ test_that("Check if r returned is between -0.5 and 0.5", {
   output <-
     OPAG(Pop,
          Age_Pop = Age_Pop,
-         AgeInt_Pop = AgeInt_Pop,
+         # AgeInt_Pop = AgeInt_Pop,
          nLx = nLx,
          Age_nLx = Age_nLx,
-         AgeInt_nLx,
+         # AgeInt_nLx,
          Age_fit =  c(60,70),
          AgeInt_fit = c(10,10),
          Redistribute_from = 80,
diff --git a/tests/testthat/test-graduate.R b/tests/testthat/test-graduate.R
index 99c941a73..8e018edc6 100644
--- a/tests/testthat/test-graduate.R
+++ b/tests/testthat/test-graduate.R
@@ -54,5 +54,36 @@ test_that("beers works", {
   )
 })
 
+test_that("age ranges flexible for uniform, mono, and pclm", {
+  Pop5    <- c(88962, 73756, 51990, 55395, 48562)
+  Age5    <- seq(0,20,5)
+  Age5alt <- seq(45,65,5)
+  
+  m0      <- graduate_mono( Pop5, Age5 )
+  m45     <- graduate_mono( Pop5, Age5alt ) 
+  p0      <- graduate_pclm( Pop5, Age5 )
+  p45     <- graduate_pclm( Pop5, Age5alt ) 
+  u0      <- graduate_uniform( Pop5, Age5 )
+  u45     <- graduate_uniform( Pop5, Age5alt ) 
+  
+  expect_true(all(abs(m0 - m45) < 1e-9))
+  expect_true(all(abs(p0 - p45) < 1e-4))
+  expect_true(all(abs(u0 - u45) < 1e-9))
+  
+  ma0     <- names2age(m0)
+  ma45    <- names2age(m45)
+  pa0     <- names2age(p0)
+  pa45    <- names2age(p45)
+  ua0     <- names2age(u0)
+  ua45    <- names2age(u45)
+  
+  expect_equal(ma0, 0:20, tolerance = 0)
+  expect_equal(pa0, 0:20, tolerance = 0)
+  expect_equal(ua0, 0:20, tolerance = 0)
+  expect_equal(ma45, 45:65, tolerance = 0)
+  expect_equal(pa45, 45:65, tolerance = 0)
+  expect_equal(ua45, 45:65, tolerance = 0)
+  
+})
 
 
diff --git a/tests/testthat/test-lt_abridged.R b/tests/testthat/test-lt_abridged.R
index 2b2c9e939..93275576f 100644
--- a/tests/testthat/test-lt_abridged.R
+++ b/tests/testthat/test-lt_abridged.R
@@ -9,9 +9,19 @@ context("test-lt_abridged")
 
 # testing function --------------------------------------------------------
 
-lt_test_all_positive_plus_qx_lt_1 <- function(LT) {
+lt_consistent <- function(LT) {
     # check positive values
+    nAx    <- LT$nAx
+    qx     <- LT$qx
+    lx     <- LT$lx
+    ndx    <- LT$ndx
+    Lx     <- LT$Lx
+    Sx     <- LT$Sx
+    ex     <- LT$ex
+    Age    <- LT$Age
+    AgeInt <- LT$AgeInt
   
+    # no negatives allows
     expect_equal(
         LT %>% 
           # TR: open age AgeInt is NA, not handled well with logicals
@@ -23,13 +33,12 @@ lt_test_all_positive_plus_qx_lt_1 <- function(LT) {
     )
     
     # check qx less than 1
-    expect_equal(
-        LT %>% 
-          '['("nqx") %>% 
-            '>'(1) %>% 
-            sum(),
-        0
-    )
+    expect_equal( sum(qx > 1), 0 )
+    
+    # check monotonicity of lx, Lx, Tx, x + ex
+    # check sum(dx) = lx[1]
+    
+    # ds <- diff(Age + ex) %>% sign()
 }
 
 
@@ -80,7 +89,7 @@ test_that("lt_abridged works on PAS example", {
     )
     
     # positive, qx =< 1
-    PASLT %>% lt_test_all_positive_plus_qx_lt_1()
+    PASLT %>% lt_consistent()
 
 })
 
@@ -126,8 +135,8 @@ test_that("lt_abridged works on UN 1982 (p. 34) example", {
     )
     
     # positive, qx =< 1
-    UNLT1 %>% lt_test_all_positive_plus_qx_lt_1()
-    UNLT2 %>% lt_test_all_positive_plus_qx_lt_1()
+    UNLT1 %>% lt_consistent()
+    UNLT2 %>% lt_consistent()
 
 })
 
@@ -225,8 +234,32 @@ test_that("lt_abridged works on Mortpak example (United Nations 1988, p. 82)", {
     )
     
     # positive, qx =< 1
-    MP_UNLT100 %>% lt_test_all_positive_plus_qx_lt_1()
-    MP_UNLT80 %>% lt_test_all_positive_plus_qx_lt_1()
-    MP_UNLT60 %>% lt_test_all_positive_plus_qx_lt_1()
+    MP_UNLT100 %>% lt_consistent()
+    MP_UNLT80 %>% lt_consistent()
+    MP_UNLT60 %>% lt_consistent()
 })
 
+
+test_that("lt_abridged does not fail when extrapFit is > 2", {
+  lx <- c(1.0000000, 0.8352893, 0.6128018, 0.5733119, 0.5723181, 0.5568574, 0.5477342, 0.5244361, 0.5120798, 0.4926618, 0.4628227, 0.4390118, 0.4100229, 0.3840418)
+
+  Age <- c(0, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60)
+
+  Sex <- "m"
+
+
+  res <- lt_abridged(
+    lx = lx,
+    Age = Age,
+    radix = 1e+05,
+    axmethod = "un",
+    a0rule = "cd",
+    Sex = Sex,
+    region = "w",
+    mod = TRUE,
+    OAG = TRUE,
+    extrapFit = seq(20, 60, by = 5)
+  )
+
+  expect_s3_class(res, "data.frame")
+})
diff --git a/tests/testthat/test-mig_beta.R b/tests/testthat/test-mig_beta.R
index fd1751373..2a14abd45 100644
--- a/tests/testthat/test-mig_beta.R
+++ b/tests/testthat/test-mig_beta.R
@@ -1,6 +1,6 @@
 check_form <- function(x) {
   expect_is(x, "numeric")
-  expect_true(length(x) == 103)
+  expect_true(length(x) == 102)
   expect_true(all(!is.na(x)))
   expect_named(x)
 }
@@ -87,13 +87,13 @@ test_that("Births are pulled from post-processed WPP2019", {
       date2 = "2010-10-25",
       age1 = 0:100
     ))
-    
+
   expect_true(any(outp == "births not provided. Downloading births for Russian Federation (LocID = 643), gender: `male`, years: 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010" #nolintr
   ))
 })
 
 test_that("mig_beta works well with different time points", {
-  # 3) mortality (abridged, 2 and 3 time points) and fertility given: 
+  # 3) mortality (abridged, 2 and 3 time points) and fertility given:
   mortdate1 <- 2003
   mortdate2 <- 2006
   mortdate3 <- 2010
@@ -138,7 +138,7 @@ test_that("mig_beta works well with different time points", {
    lxMat = lxmat3,
    dates_lx = c(mortdate1,mortdate2,mortdate3),
    age_lx = age_lx,
-   births = c(719511L, 760934L, 772973L, 749554L, 
+   births = c(719511L, 760934L, 772973L, 749554L,
               760831L, 828772L, 880543L, 905380L, 919639L),
    years_births = 2002:2010)
 
@@ -153,7 +153,7 @@ test_that("mig_beta works well with different time points", {
    lxMat = lxmat3,
    dates_lx = c(mortdate1,mortdate2,mortdate3),
    age_lx = age_lx,
-   births = c(719511L, 760934L, 772973L, 749554L, 
+   births = c(719511L, 760934L, 772973L, 749554L,
               760831L, 828772L, 880543L, 905380L, 919639L,1e6),
    years_births = 2002:2011)
 
@@ -228,7 +228,7 @@ test_that("mig_beta fails when lxmat is not correct", {
       lxMat = lxmat[, 1, drop = FALSE],
       dates_lx = c(mortdate1,mortdate2,mortdate3),
       age_lx = age_lx,
-      births = c(719511L, 760934L, 772973L, 749554L, 
+      births = c(719511L, 760934L, 772973L, 749554L,
                  760831L, 828772L, 880543L, 905380L, 919639L),
       years_births = 2002:2010),
     regexp = "lxMat should have at least two or more dates as columns. lxMat contains only one column" #nolintr
@@ -594,7 +594,7 @@ test_that("mig_beta throws download messages when verbose = TRUE", {
 
 
 test_that("mig_beta throws download messages when verbose = TRUE and LocID used", {
-  
+
   # 1) lx is downloaded
   outp <- capture_output_lines(
     mig_beta(
@@ -611,7 +611,7 @@ test_that("mig_beta throws download messages when verbose = TRUE and LocID used"
       verbose = TRUE
     ))
   expect_true(any(outp == "lxMat not provided. Downloading lxMat for Russian Federation (LocID = 643), gender: `both`, for years between 2002.8 and 2010.8"))
-  
+
   # 2) births are downloaded
   outp <- capture_output_lines(
     mig_beta(
@@ -627,7 +627,7 @@ test_that("mig_beta throws download messages when verbose = TRUE and LocID used"
       verbose = TRUE
     ))
   expect_true(any(outp == "births not provided. Downloading births for Russian Federation (LocID = 643), gender: `both`, years: 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010"))
-  
+
   # 3) dates_lx or years_births are being assumed anything
   expect_output(
     mig_beta(
@@ -648,3 +648,184 @@ test_that("mig_beta throws download messages when verbose = TRUE and LocID used"
     fixed = TRUE
   )
 })
+
+
+test_that("mig_beta applies child_adjustment correctly", {
+  res_none <-
+    mig_beta(
+      location = "Russian Federation",
+      sex = "male",
+      c1 = pop1m_rus2002,
+      c2 = pop1m_rus2010,
+      date1 = "2002-10-16",
+      date2 = "2010-10-25",
+      age1 = 0:100,
+      births = births,
+      child_adjust = "none"
+    )
+
+
+  res_cwr <-
+    mig_beta(
+      location = "Russian Federation",
+      sex = "male",
+      c1 = pop1m_rus2002,
+      c2 = pop1m_rus2010,
+      date1 = "2002-10-16",
+      date2 = "2010-10-25",
+      age1 = 0:100,
+      births = births,
+      child_adjust = "cwr"
+    )
+
+  res_cwr_high <-
+    mig_beta(
+      location = "Russian Federation",
+      sex = "male",
+      c1 = pop1m_rus2002,
+      c2 = pop1m_rus2010,
+      date1 = "2002-10-16",
+      date2 = "2010-10-25",
+      age1 = 0:100,
+      births = births,
+      child_adjust = "cwr",
+      cwr_factor = 0.9
+    )
+
+  res_constant <-
+    mig_beta(
+      location = "Russian Federation",
+      sex = "male",
+      c1 = pop1m_rus2002,
+      c2 = pop1m_rus2010,
+      date1 = "2002-10-16",
+      date2 = "2010-10-25",
+      age1 = 0:100,
+      births = births,
+      child_adjust = "constant"
+    )
+
+  # Check we don't mess up the format of migs (length, type, etc..)
+  for (i in list(res_none, res_cwr, res_constant)) check_form(i)
+
+  # Since cwr and constant can change, we only test that they adjust a certain
+  # number of ages rather than test the exact equality of results.
+
+  # CWR:
+  # Why 9? Because date1 and date2 differ by 9 years, so only the first 9
+  # cohorts are adjusted.
+  # Test that the first 9 are adjusted:
+  expect_true(all((res_none - res_cwr)[1:9] != 0))
+
+
+  # Constant:
+  # Testhat that the first 9 are adjusted.
+  expect_true(all((res_none - res_constant)[1:9] != 0))
+
+
+  # Test that CWR with high cwr_factor returns higher younger ages than with 0.3,
+  # the default:
+  expect_true(all(res_cwr_high[1:9] > res_cwr[1:9]))
+
+})
+
+
+
+test_that("mig_beta applies oldage_adjustment correctly", {
+  res_none <-
+    mig_beta(
+      location = "Russian Federation",
+      sex = "male",
+      c1 = pop1m_rus2002,
+      c2 = pop1m_rus2010,
+      date1 = "2002-10-16",
+      date2 = "2010-10-25",
+      age1 = 0:100,
+      births = births,
+      oldage_adjust = "none"
+    )
+
+  res_beers <-
+    mig_beta(
+      location = "Russian Federation",
+      sex = "male",
+      c1 = pop1m_rus2002,
+      c2 = pop1m_rus2010,
+      date1 = "2002-10-16",
+      date2 = "2010-10-25",
+      age1 = 0:100,
+      births = births,
+      oldage_adjust = "beers"
+    )
+
+
+  res_mav <-
+    mig_beta(
+      location = "Russian Federation",
+      sex = "male",
+      c1 = pop1m_rus2002,
+      c2 = pop1m_rus2010,
+      date1 = "2002-10-16",
+      date2 = "2010-10-25",
+      age1 = 0:100,
+      births = births,
+      oldage_adjust = "mav"
+    )
+
+  # Check we don't mess up the format of migs (length, type, etc..)
+  for (i in list(res_none, res_beers, res_mav)) check_form(i)
+
+  # beers:
+  # expect that the 65+ are different because they're adjusted
+  # Why 66? Because first age is 0 and total length is 101
+  expect_true(all((res_none - res_beers)[66:100] != 0))
+
+
+  # mav:
+  # expect that the 65+ are different because they're adjusted
+  # Why 66? Because first age is 0 and total length is 101
+  expect_true(all((res_none - res_mav)[66:100] != 0))
+
+
+  # Test that oldage_min controls the age from which to adjust
+  # old ages
+  res_beers <-
+    mig_beta(
+      location = "Russian Federation",
+      sex = "male",
+      c1 = pop1m_rus2002,
+      c2 = pop1m_rus2010,
+      date1 = "2002-10-16",
+      date2 = "2010-10-25",
+      age1 = 0:100,
+      births = births,
+      oldage_adjust = "beers",
+      oldage_min = 70
+    )
+
+
+  res_mav <-
+    mig_beta(
+      location = "Russian Federation",
+      sex = "male",
+      c1 = pop1m_rus2002,
+      c2 = pop1m_rus2010,
+      date1 = "2002-10-16",
+      date2 = "2010-10-25",
+      age1 = 0:100,
+      births = births,
+      oldage_adjust = "mav",
+      oldage_min = 70
+    )
+
+  # beers:
+  # expect that the 65+ are different because they're adjusted
+  # Why 71? Because first age is 0 and total length is 101
+  expect_true(all((res_none - res_beers)[71:100] != 0))
+
+
+  # mav:
+  # expect that the 65+ are different because they're adjusted
+  # Why 71? Because first age is 0 and total length is 101
+  expect_true(all((res_none - res_mav)[71:100] != 0))
+})
diff --git a/tests/testthat/test-smooth_age_5.R b/tests/testthat/test-smooth_age_5.R
index 3984ae87b..4a72c39c4 100644
--- a/tests/testthat/test-smooth_age_5.R
+++ b/tests/testthat/test-smooth_age_5.R
@@ -5,108 +5,151 @@ context("test-agesmth")
 
 # smooth_age_5_cf -----------------------------------------------------
 
-test_that("smooth_age_5_cf works",{
-    
-    Ages         <- seq(0, 80, by = 5)
-    
-    CFmales <- smooth_age_5_cf(pop5m_pasex, Ages, OAG = TRUE)
-    
-    CFtest <- c(NA, NA, 346290, 287083, 285855, 261082, 237937,
-                202809, 162973, 125720, 88730, 67352, 55187, 40657, NA, NA, NA)
-    
-    # test
-    expect_equivalent(CFmales, CFtest, tolerance = 1)
-    expect_equal(CFmales %>% sum, CFtest %>% sum, tolerance = 1)
-    expect_true(all(CFmales > 0, na.rm = T))
+test_that("smooth_age_5_cf works", {
+  Ages <- seq(0, 80, by = 5)
 
+  CFmales <- smooth_age_5_cf(pop5m_pasex, Ages, OAG = TRUE)
+
+  CFtest <- c(
+    NA, NA, 346290, 287083, 285855, 261082, 237937,
+    202809, 162973, 125720, 88730, 67352, 55187, 40657, NA, NA, NA
+  )
+
+  # test
+  expect_equivalent(CFmales, CFtest, tolerance = 1)
+  expect_equal(CFmales %>% sum(), CFtest %>% sum(), tolerance = 1)
+  expect_true(all(CFmales > 0, na.rm = T))
 })
 
 
 
 # smooth_age_5_kkn ----------------------------------------------------------------
 
-test_that("smooth_age_5_kkn works",{
-    
-    Ages <- seq(0, 80, by = 5)
-    
-    KKNtest <- c(NA, NA, 354871, 278502, 285508, 261429, 236513 ,
-                 204233, 162138, 126555, 90094, 65988, 54803, 41041, NA, NA, NA)
-    
-    KKNmales <- smooth_age_5_kkn(pop5m_pasex, Ages, OAG = TRUE)
-    
-    # test
-    expect_equivalent(KKNmales, KKNtest, tolerance = 1)
-    expect_equal(KKNmales %>% sum, KKNtest %>% sum, tolerance = 1)
-    expect_true(all(KKNmales > 0, na.rm = T))
-    
+test_that("smooth_age_5_kkn works", {
+  Ages <- seq(0, 80, by = 5)
+
+  KKNtest <- c(
+    NA, NA, 354871, 278502, 285508, 261429, 236513,
+    204233, 162138, 126555, 90094, 65988, 54803, 41041, NA, NA, NA
+  )
+
+  KKNmales <- smooth_age_5_kkn(pop5m_pasex, Ages, OAG = TRUE)
+
+  # test
+  expect_equivalent(KKNmales, KKNtest, tolerance = 1)
+  expect_equal(KKNmales %>% sum(), KKNtest %>% sum(), tolerance = 1)
+  expect_true(all(KKNmales > 0, na.rm = T))
 })
 
 
 # smooth_age_5_arriaga ----------------------------------------------------------------
 
-test_that("smooth_age_5_arriaga works",{
-    
-    Ages <- seq(0, 80, by = 5)
-    
-    Amales <- smooth_age_5_arriaga(Value = pop5m_pasex, Age = Ages, OAG = TRUE)
-    
-    #' # PAS spreadsheet result:
-    Atest <- c(662761, 495126, 345744, 287629, 285919, 261018, 237469, 203277,
-    161733, 126960, 88586, 67496, 54587, 41257, 28790, 17189, 34729)
-    
-    # test
-    expect_equivalent(Amales, Atest, tolerance = 1)
-    expect_equal(Amales %>% sum, Atest %>% sum, tolerance = 1)
-    expect_true(all(Amales > 0, na.rm = T))
-    
+test_that("smooth_age_5_arriaga works", {
+  Ages <- seq(0, 80, by = 5)
+
+  Amales <- smooth_age_5_arriaga(Value = pop5m_pasex, Age = Ages, OAG = TRUE)
+
+  #' # PAS spreadsheet result:
+  Atest <- c(
+    662761, 495126, 345744, 287629, 285919, 261018, 237469, 203277,
+    161733, 126960, 88586, 67496, 54587, 41257, 28790, 17189, 34729
+  )
+
+  # test
+  expect_equivalent(Amales, Atest, tolerance = 1)
+  expect_equal(Amales %>% sum(), Atest %>% sum(), tolerance = 1)
+  expect_true(all(Amales > 0, na.rm = T))
 })
 
 
 # smooth_age_5_strong ----------------------------------------------------------------
 
-test_that("smooth_age_5_strong works",{
-    
-    Ages <- seq(0, 80, by = 5)
-    
-    Stest <- c(646617, 511270, 386889, 317345, 273736, 240058, 218645, 188297, 
-               153931, 124347, 93254, 71858, 53594, 39721, 27887, 18092, 34729)
-    
-    Smales <- smooth_age_5_strong(pop5m_pasex, Ages, OAG = TRUE)
-    
-    # test
-    expect_equivalent(Smales, Stest, tolerance = 1)
-    expect_equal(Smales %>% sum, Stest %>% sum, tolerance = 1)
-    expect_true(all(Smales > 0, na.rm = T))
-    
+test_that("smooth_age_5_strong works", {
+  Ages <- seq(0, 80, by = 5)
+
+  Stest <- c(
+    646617, 511270, 386889, 317345, 273736, 240058, 218645, 188297,
+    153931, 124347, 93254, 71858, 53594, 39721, 27887, 18092, 34729
+  )
+
+  Smales <- smooth_age_5_strong(pop5m_pasex, Ages, OAG = TRUE)
+
+  # test
+  expect_equivalent(Smales, Stest, tolerance = 1)
+  expect_equal(Smales %>% sum(), Stest %>% sum(), tolerance = 1)
+  expect_true(all(Smales > 0, na.rm = T))
 })
 
+test_that("smooth_age_5_mav works", {
+
+  # Without tails
+  Ages <- seq(0, 80, by = 5)
+
+  Mtest <- c(
+    NA, 505239, 382964, 300570, 274160, 263151, 239782, 202228,
+    162308, 128489, 92946, 73183, 51717, 41880, 26119, NA, 34729
+  )
+
+  Mmales <- smooth_age_5_mav(pop5m_pasex, Ages, OAG = TRUE, tail = FALSE)
+
+  # test
+  expect_equivalent(Mmales, Mtest, tolerance = 1)
+  expect_equal(Mmales %>% sum(), Mtest %>% sum(), tolerance = 1)
+  expect_true(all(Mmales > 0, na.rm = T))
+
+  # With tails
+  Mtest_tail <- c(
+    642367, 507810, 382964, 300570, 274160, 263151, 239782, 202228,
+    162308, 128489, 92946, 73183, 51717, 41880, 27038, 29796, 34729
+  )
+
+  Mmales_tail <- smooth_age_5_mav(pop5m_pasex, Ages, OAG = TRUE, tail = TRUE)
+
+  # test
+  expect_equivalent(Mmales_tail, Mtest_tail, tolerance = 1)
+  expect_equal(Mmales_tail %>% sum(), Mtest_tail %>% sum(), tolerance = 1)
+  expect_true(all(Mmales_tail > 0, na.rm = T))
+
+  # Check tail/no_tail are the same except
+  # for the tails:
+  expect_equivalent(Mmales[3:15], Mmales_tail[3:15], tolerance = 1)
+  # Exclue first/last pair of values because cascading alters both
+  # numbers.
+
+})
+
+
 
 test_that("smooth_age_5_feeney works", {
-    
-    Pop <- c(2337, 3873, 3882, 3952, 4056, 3685, 3687, 3683, 3611, 3175,
-             3457, 2379, 3023, 2375, 2316, 2586, 2014, 2123, 2584, 1475,
-             3006, 1299, 1236, 1052, 992, 3550, 1334, 1314, 1337, 942,
-             3951, 1128, 1108, 727, 610, 3919, 1221, 868, 979, 637,
-             3409, 887, 687, 533, 313, 2488, 677, 426, 524, 333,
-             2259, 551, 363, 290, 226, 1153, 379, 217, 223, 152,
-             1500, 319, 175, 143, 89, 670, 149, 96, 97, 69,
-             696, 170, 60, 38, 23, 745)
-    
-    Ages <- c(0:75)
-    
-    result <- smooth_age_5_feeney(Pop, Ages, maxit = 200, OAG = TRUE)
-    # inlcude original unstated age (15)
-    result <- rescale_vector(result,sum(result)+15)
-    tab2_answer <- c(18004, 17351, 14018, 10927, 8837, 8145, 7823, 7029,
-                     5748, 4326, 3289, 2415, 1794, 1197, 982, 741)
-    names(tab2_answer) <- seq(0,75,5)
-    
-    
-    testthat:::expect_equal(
-        result,
-        tab2_answer,
-        tolerance = .001) # TR: this on relative scale??
+  Pop <- c(
+    2337, 3873, 3882, 3952, 4056, 3685, 3687, 3683, 3611, 3175,
+    3457, 2379, 3023, 2375, 2316, 2586, 2014, 2123, 2584, 1475,
+    3006, 1299, 1236, 1052, 992, 3550, 1334, 1314, 1337, 942,
+    3951, 1128, 1108, 727, 610, 3919, 1221, 868, 979, 637,
+    3409, 887, 687, 533, 313, 2488, 677, 426, 524, 333,
+    2259, 551, 363, 290, 226, 1153, 379, 217, 223, 152,
+    1500, 319, 175, 143, 89, 670, 149, 96, 97, 69,
+    696, 170, 60, 38, 23, 745
+  )
+
+  Ages <- c(0:75)
+
+  result <- smooth_age_5_feeney(Pop, Ages, maxit = 200, OAG = TRUE)
+  # inlcude original unstated age (15)
+  result <- rescale_vector(result, sum(result) + 15)
+  tab2_answer <- c(
+    18004, 17351, 14018, 10927, 8837, 8145, 7823, 7029,
+    5748, 4326, 3289, 2415, 1794, 1197, 982, 741
+  )
+  names(tab2_answer) <- seq(0, 75, 5)
+
+
+  testthat:::expect_equal(
+    result,
+    tab2_answer,
+    tolerance = .001
+  ) # TR: this on relative scale??
 })
 #' Age <- c(0,1,seq(5,90,by=5))
 #' # defaults
-#' zz <- zigzag_smth(dth5_zigzag, Age, OAG = TRUE, ageMin = 40, ageMax = 90)
\ No newline at end of file
+#' zz <- zigzag_smth(dth5_zigzag, Age, OAG = TRUE, ageMin = 40, ageMax = 90)
diff --git a/tests/testthat/test_AGEINT.R b/tests/testthat/test_AGEINT.R
index a3b50e0af..9c2fbb0e2 100644
--- a/tests/testthat/test_AGEINT.R
+++ b/tests/testthat/test_AGEINT.R
@@ -62,5 +62,5 @@ test_that("basic interpolation function",{
                        datesIn = dates,
                        datesOut = dec.date(1900.5), 
                        extrap = T),
-                regexp = "Negative values were turned 0. No accepted in population counts, fertility rates or life table functions.")
+                regexp = "Negative values have been replaced with 0s")
 })
diff --git a/version_lookup.R b/version_lookup.R
new file mode 100644
index 000000000..95e84959b
--- /dev/null
+++ b/version_lookup.R
@@ -0,0 +1,138 @@
+# remotes::install_github("lorenzwalthert/gitsum")
+if (system("whoami",intern=TRUE) == "tim"){
+  library(gitsum)
+  library(tidyverse)
+  library(readr)
+  library(lubridate)
+  
+  get_versions <- function(hash){
+    D <- readLines(paste0("https://raw.githubusercontent.com/timriffe/DemoTools/",hash,"/DESCRIPTION") )
+    D[grepl(D,pattern = "Version: ")]  %>%  
+      gsub(pattern = "Version: ", replacement = "") 
+  }
+  
+  update_lookup <- function(){
+    DESC_changes <- 
+      parse_log_detailed() %>% 
+      unnest_log() %>% 
+      dplyr::filter(changed_file == "DESCRIPTION") %>% 
+      group_by(lubridate::as_date(date)) %>% 
+      slice(n()) %>% 
+      ungroup() %>% 
+      mutate(date = as_date(date))
+        #date = paste(year(date),month(date),day(date),sep="-"))
+    # DESC_changes %>% 
+    #   mutate(get_versions(hash))
+    vers <- list()
+    for (i in 1:nrow(DESC_changes)){
+      # Sys.sleep(1)
+      vers[[i]] <- try(get_versions(DESC_changes$hash[i]))
+    }
+    closeAllConnections()
+    errors <- lapply(vers,class) %>% unlist() 
+    errors <- errors == "try-error"
+    vers[errors]<-NA
+    DESC_changes <- cbind(DESC_changes,version=tibble(version=unlist(vers)))
+    
+    version_lookup <- 
+      DESC_changes %>% 
+      filter(!is.na(version)) %>% 
+      select(date,version, hash) 
+    
+    write_csv(version_lookup,"version_lookup.csv")
+  
+   }
+}
+
+get_DemoTools_versions <- function(){
+  readr::read_csv("https://raw.githubusercontent.com/timriffe/DemoTools/master/version_lookup.csv")
+}
+
+install_DemoTools_version <- function(version = NULL, date = NULL, hash = NULL){
+  
+  
+  
+  if (is.null(version) & is.null(date) & is.null(hash)){
+    cat("version identifiers include (one of):
+        1. Version entries in the DESCRIPTION file
+        2. Date (yyyy-mm-dd)
+        3. the hash key of the commit\n\n")
+    cat("no identifier given, you can view options with 
+    get_DemoTools_version()\n\n")
+    cat("to install the current head, try:
+        remotes::install_github('timriffe/DemoTools'")
+    }
+  
+  versions <- get_DemoTools_versions() %>% 
+    mutate(date = lubridate::as_date(date))
+  
+  out <- NULL
+  if (!is.null(hash)){
+    if (hash %in% version$hash){
+      remotes::install_github("timriffe/DemoTools", ref = hash)
+    } else {
+      stop("hash not found, try using one from the (incomplete) list returned by:\n get_DemoTools_versions()")
+    }
+    out <- 1
+  }
+  
+  # try date
+  if (is.null(out)){
+    if (!is.null(date)){
+      if (is.character(date)){
+        date = lubridate::ymd(date)
+      }
+      if (is.na(date)){
+        cat("date didn't parse. It should be yyyy-mm-dd format if given as character\n")
+        stop()
+      }
+      dateK <- date
+        dateL <- 
+          versions %>% 
+          mutate(dist = abs(dateK - date)) %>% 
+          dplyr::filter(dist == min(dist)) %>% 
+          dplyr::pull(date) %>% 
+          '['(1)
+        hash <- 
+          versions %>% 
+          dplyr::filter(date == dateL) %>% 
+          dplyr::pull(hash)
+        remotes::install_github("timriffe/DemoTools", ref = hash)
+        out <- 1
+        if (date != dateL){
+          cat("date not in the (incomplete) set returned by get_DemoTools_versions()
+              using the closest date in that subset instead:", as.character(dateL),"\n")
+        }
+      }
+  }
+  
+  # try version
+  if (is.null(out)){
+    if (!is.null(version)){
+      .version <- version
+      hash <- 
+        versions %>% 
+        dplyr::filter(version == .version) %>% 
+        dplyr::pull(hash)
+      if (length(hash) == 1){
+        remotes::install_github("timriffe/DemoTools", ref = hash)  
+        out <- 1
+      } else {
+        cat("version not in the set returned by get_DemoTools_versions()
+            we didn't try approximating. Have a look at the version snapshots available
+            in the lookup table.\n")
+      }
+    }
+  }
+  # catch-all
+  if (is.null(out)){
+    cat("Looks like no installation attempted. For the most recent version try:
+        remotes::install_github('timriffe/DemoTools')
+        otherwise easiest thing to roll back is to pick something 
+        out from the lookup table.")
+  }
+    
+  
+}
+  
+  
diff --git a/version_lookup.csv b/version_lookup.csv
new file mode 100644
index 000000000..c66c23c36
--- /dev/null
+++ b/version_lookup.csv
@@ -0,0 +1,188 @@
+date,version,hash
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+2021-05-31,01.13.55,59a0f4e50b7696c185a3c9d4e582426f88aac84f
+2021-06-16,01.13.57,39be92db9c217c29511e653b18ff97d921153605
+2021-06-17,01.13.58,cf2dda3ac1eee11cc32fa18bedfa429b5c7bafa8
+2021-06-23,01.13.59,0f8fff68ace12f07173156e979290db0e1578cea
+2021-07-13,01.13.60,0560660113fa5b79de87e982b755ccb734d3b6a6
+2021-07-14,01.13.61,9cc242cc4496c0798e367e9c77be1ed006f8ba18
+2021-08-18,01.13.62,a5977c76166914e6cc13f94a15c4412f4a735e10
+2021-08-20,01.13.63,2ba8f6aa4435388e400065afabba02992dd765ff
+2021-08-23,01.13.65,9d5b0e407aea81a90c34e400e1c7b755d0c361d5
+2021-08-25,01.13.66,3cfe9a80ec3728703f9009d9088ff9d94b1d8b26
+2021-09-01,01.13.67,a2ac0a223879fb67e0cade10284431262b218df2
+2021-09-03,01.13.68,d7262ee938798cf3317aaef8a51923a6669d849f
+2021-09-15,01.13.70,2f15be434ff7c6849bed9d4e4680c99e8145f71f
+2021-10-09,01.13.73,c5bd735e877cd6579fb1c0ff7d427051e8b6bc86
+2021-11-03,01.13.74,128959fcc11299787ed87d1f16898e741879b57f
+2021-11-23,01.13.75,5b016adde09351da36970a274090c63d809328db
diff --git a/vignettes/Age-heaping_quality_with_Demotools.Rmd b/vignettes/Age-heaping_quality_with_Demotools.Rmd
index c65fb80c4..db392d022 100644
--- a/vignettes/Age-heaping_quality_with_Demotools.Rmd
+++ b/vignettes/Age-heaping_quality_with_Demotools.Rmd
@@ -42,9 +42,9 @@ plot(Age, pop1m_ind, type = 'o')
 
 The figure above has some clear age irregularities. For example, there is a consistent clustering around ages ending in zero and five. In principle, a post-enumeration survey or a sample interview should give more information on the reasons why these irregularities appear in the data [@siegel2004methods]. However, not every country has the resources to conduct a survey or post-enumeration process. Therefore, various arithmetic techniques have been developed for measuring heaping on individual ages, terminal digits, and age ranges. These estimate the degree of bias, but they do not correct it [@booth2015demographic]. The simplest way to analyze age-heaping is by assuming that the true numbers are *rectangularly* distributed over some age range that includes centered the age in question [@siegel2004methods].
 
-Several indices of age-heaping exist. `DemoTools` implements Whipple [@spoorenberg2007quality], Myers, Bachi [@bachi1951tendency], Coale-Li [@coale1991effect], Noumbissi [@noumbissi1992indice], Spoorenberg [@spoorenberg2007quality], Jdanov [@jdanov2008beyond], and Kannisto [@kannisto1999assessing] age-heaping indices. Although the literal interpretation of these indices may vary, they tend to covary strongly when applied to the same data and age-ranges, so for making quick judgements of the degree of heaping over a large collection of data it may not be necessary to apply more than one or two. We also offer two new measures designed to test for irregularities in data in 5-year age bins. 
+Several indices of age-heaping exist. `DemoTools` implements Whipple [@spoorenberg2007quality], Myers, Bachi [@bachi1951tendency], Coale-Li [@coale1991effect], Noumbissi [@noumbissi1992indice], Spoorenberg [@spoorenberg2007quality], Jdanov [@jdanov2008beyond], and Kannisto [@kannisto1999assessing] age-heaping indices. Although the literal interpretation of these indices may vary, they tend to covary strongly when applied to the same data and age-ranges, so for making quick judgement of the degree of heaping over a large collection of data it may not be necessary to apply more than one or two. We also offer two new measures designed to test for irregularities in data in 5-year age bins. 
 
-Often the degree of detected heaping suggests that some smoothing procedure is warranted, but the kind of smoothing procedure may be a function of the particular way in which heaping is manifested. For example, if heaping is light, or just a matter of rounding to the nearest digit divisible by 5, then there will be no major difference between heaping on 0s versus heaping on 5s. In this case, grouping to 5-year age bins (see `groupAges()`) and then graduating in a constrained way (see `sprague()`) may suffice to remove the distortion while maintaining the broad underlying age pattern. However, if heaping is much worse on 0s than on 5s, the age pattern may still be distorted in a regular and pernicious way even after binning in 5-year age groups. In that case, it is advised to select a smoothing procedure designed for 5-year age groups (see `smooth_age_5()` and the vignette on smoothing) before graduating, or else some other more agressive smoothing option (see `agesmth1()`). The present vignette does not offer specific guidelines for such adjustments, but we do offer two new age quality indices that might be useful for deciding whether to resort to agressive smoothing: `zero_pref_sawtooth()` checks for and rates the mentioned jagged pattern in 5-year age groups. `five_year_roughness()` gives a total measure of noise for data in 5-year age groups, but does not look for a particular pattern to it. This second measure should not be used in isolation, but together with visual assessment. More details about such adjustments and decisions can be found in a second vignette on smoothing.
+Often the degree of detected heaping suggests that some smoothing procedure is warranted, but the kind of smoothing procedure may be a function of the particular way in which heaping is manifested. For example, if heaping is light, or just a matter of rounding to the nearest digit divisible by 5, then there will be no major difference between heaping on 0s versus heaping on 5s. In this case, grouping to 5-year age bins (see `groupAges()`) and then graduating in a constrained way (see `sprague()`) may suffice to remove the distortion while maintaining the broad underlying age pattern. However, if heaping is much worse on 0s than on 5s, the age pattern may still be distorted in a regular and pernicious way even after binning in 5-year age groups. In that case, it is advised to select a smoothing procedure designed for 5-year age groups (see `smooth_age_5()` and the vignette on smoothing) before graduating, or else some other more aggressive smoothing option (see `agesmth1()`). The present vignette does not offer specific guidelines for such adjustments, but we do offer two new age quality indices that might be useful for deciding whether to resort to aggressive smoothing: `zero_pref_sawtooth()` checks for and rates the mentioned jagged pattern in 5-year age groups. `five_year_roughness()` gives a total measure of noise for data in 5-year age groups, but does not look for a particular pattern to it. This second measure should not be used in isolation, but together with visual assessment. More details about such adjustments and decisions can be found in a second vignette on smoothing.
 
 
 ### Whipple Index
@@ -105,7 +105,7 @@ Bi
 This method, based on the underlying principles and assumptions of the original Whipple's index, improves by extending its basic principle to all ten digits. It compares single terminal digit numerators to denominators  consisting in 5-year age groups centered on the terminal digit of age in question [@noumbissi1992indice]. It relies on a weaker assumption of linearity over an age range of five years rather than ten. It is based once more on the underlying principles and assumptions of the original Whipple's index and introduces the following formulas to measure age heaping:
 
 \begin{align}
-\label{eq:Noumbussi}
+\label{eq:Noumbissi}
 W_0 =5\frac{P_{30}+P_{40}+P_{50}+P_{60}}{_5P_{28}+\, _5P_{38}+\, _5P_{48}+\, _5P_{58}} \\
 W_5 =5\frac{P_{25}+P_{35}+P_{45}+P_{55}}{_5P_{23}+\, _5P_{33}+\, _5P_{43}+\, _5P_{53}} \\
 \end{align}
@@ -201,4 +201,4 @@ r5
 
 Values greater than 0.1 coupled with a sawtooth value greater than 0 already suggest that some smoothing is warranted. If there is no detected sawtooth pattern, then five-year-roughness indices should probably need to be higher and visually confirmed before deciding to smooth. Further guidelines can be found in the vignette on smoothing. 
 
-##References
+# References
diff --git a/vignettes/case_study_1.Rmd b/vignettes/case_study_1.Rmd
index a14ef53f3..48404245e 100644
--- a/vignettes/case_study_1.Rmd
+++ b/vignettes/case_study_1.Rmd
@@ -22,7 +22,7 @@ knitr::opts_chunk$set(
 ```
 
 ## Intro
-Here the idea is use API to read in death count and pop count data, evaluate them, adjust them, get to single ages, and make a single age lifetable. Or Abrdiged lifetable. Or both and compare. Show different closeout options too.
+Here the idea is use API to read in death count and pop count data, evaluate them, adjust them, get to single ages, and make a single age lifetable. Or Abridged lifetable. Or both and compare. Show different closeout options too.
 
 ##
 
diff --git a/vignettes/graduation_with_demotools.Rmd b/vignettes/graduation_with_demotools.Rmd
index 40061955c..672b64d01 100644
--- a/vignettes/graduation_with_demotools.Rmd
+++ b/vignettes/graduation_with_demotools.Rmd
@@ -28,7 +28,7 @@ knitr::opts_chunk$set(
 ## How to use `DemoTools` to graduate counts in grouped ages
 
 ### What is graduation?
-Graduation is a practice used to derive figures for _n_-year age groups, for example 5-year age groups from census data, that are corrected for net reporting error [@siegel2004methods]. The basic idea is to fit different curves to the original  _n_-year and redistribute them into single-year values. These techniques are designed so that the sum of the interpolated single-year values is consistent with the total for the groups as a whole. Among the major graduation methods are the Sprague [@sprague1880explanation] and Beers [@beers1945modified] oscilatory methods, monotone spline and the uniform distribution. More recently, the penalized composite link model (pclm) was proposed to ungroup coarsely aggregated data [@rizzi2015efficient]. All of these methodologies are implemented in `DemoTools`.
+Graduation is a practice used to derive figures for _n_-year age groups, for example 5-year age groups from census data, that are corrected for net reporting error [@siegel2004methods]. The basic idea is to fit different curves to the original  _n_-year and redistribute them into single-year values. These techniques are designed so that the sum of the interpolated single-year values is consistent with the total for the groups as a whole. Among the major graduation methods are the Sprague [@sprague1880explanation] and Beers [@beers1945modified] oscillatory methods, monotone spline and the uniform distribution. More recently, the penalized composite link model (pclm) was proposed to ungroup coarsely aggregated data [@rizzi2015efficient]. All of these methodologies are implemented in `DemoTools`.
 
 ### Why graduate?
 One of the main purposes of graduation is to refine the detail of available data in published statistics or surveys. This allows to estimate numbers of persons in single years of age from data originally in 5-year age groups. In practice, graduation of mortality curves has been very important to estimate reliable life tables, but also in fertility studies graduation is useful for the analysis of data from populations where demographic records are defective [@brass1960graduation].
@@ -80,7 +80,7 @@ sum(Value)
 ```
 ### Beers
 
-Following a similar idea, Beers interpolated two overlapping curves minimizing the squares of the differences within the interpolation range [@beers1945modified]. Specifically, Beers did this by minimizing fifth differences for a six-term formula, refered to as the 'Ordinary' Beers method [@beers1945six]. Subsequently, the ordinay formula was modified to relax the requirement that the given value be reproduced and yield smoother interpolated results, refered to as 'Modified' Beers method [@beers1945modified].
+Following a similar idea, Beers interpolated two overlapping curves minimizing the squares of the differences within the interpolation range [@beers1945modified]. Specifically, Beers did this by minimizing fifth differences for a six-term formula, refered to as the 'Ordinary' Beers method [@beers1945six]. Subsequently, the ordinary formula was modified to relax the requirement that the given value be reproduced and yield smoother interpolated results, referred to as 'Modified' Beers method [@beers1945modified].
 
 ```{r,out.width='\\textwidth', fig.width= 6, fig.height=6, fig.align='center'}
 beers.ordinary <- graduate(Value, Age, method = "beers(ord)")
@@ -101,7 +101,7 @@ sum(Value)
 ```
 
 ### Monotone spline
-Graduation by a monotone spline is another option available in `DemoTools`. This option offers an algorithm to produce interpolants that preserve properties such as monotonicity or convexity that are present in the data [@fritsch1980monotone]. These are desirable conditions to not introduce biased details that cannot be ascertained from the data [@hyman1983accurate]. To run this algorithm in `DemoTools` the option `mono` should be selected as follows:
+Graduation by a monotone spline is another option available in `DemoTools`. This option offers an algorithm to produce interpolations that preserve properties such as monotonicity or convexity that are present in the data [@fritsch1980monotone]. These are desirable conditions to not introduce biased details that cannot be ascertained from the data [@hyman1983accurate]. To run this algorithm in `DemoTools` the option `mono` should be selected as follows:
 
 ```{r,out.width='\\textwidth', fig.width= 6, fig.height=6, fig.align='center'}
 mono.grad <- graduate(Value, Age, method = "mono")
diff --git a/vignettes/lifetables_with_demotools.Rmd b/vignettes/lifetables_with_demotools.Rmd
index 34dbf5c75..416b71329 100644
--- a/vignettes/lifetables_with_demotools.Rmd
+++ b/vignettes/lifetables_with_demotools.Rmd
@@ -32,14 +32,14 @@ The lifetable is one of the most used tools in demographic research. It is desig
 
 ### The column $_nA_x$
 
-The column $_nA_x$ refers to the average number of person-years lived in the interval $x$ to $x+n$ by those who died in the interval. This is a key column when calculating period lifetables because it allows to convert from age-specific death rates $_nM_x$ to the probabiliy of dying between ages $x$ and $x+n$ denoted as $_nq_x$. There are several ways to calculate this column. `DemoTools` provides two options to calculate this column from which the user can choose, `PAS` and `UN`. The `PAS` option relies on following the rule of thumb given by Coale and Demeny for ages 0, and 1-4, and assumes interval midpoints in higher ages. This choice relies on the fact that the lower the level of mortality, the more heavily will infant deaths be concentrated at the earliest stages of infancy [@preston2000demography], and therefore the values of $_1A_0$ and  $_4A_1$ depend on the values of $_1q_0$ and $_4q_1$ [@Coale1983]. For age 0, there are two choices for the rule of thumb to apply: Andreev-Kingkade is the default, but the Coale-Demeny model pattern is also available. The `UN` option also relies on the Coale-Demeny rule of thumb for ages 1-4. For ages 5-9 onwards, it uses the formula developed by @greville1977short. 
+The column $_nA_x$ refers to the average number of person-years lived in the interval $x$ to $x+n$ by those who died in the interval. This is a key column when calculating period lifetables because it allows to convert from age-specific death rates $_nM_x$ to the probability of dying between ages $x$ and $x+n$ denoted as $_nq_x$. There are several ways to calculate this column. `DemoTools` provides two options to calculate this column from which the user can choose, `PAS` and `UN`. The `PAS` option relies on following the rule of thumb given by Coale and Demeny for ages 0, and 1-4, and assumes interval midpoints in higher ages. This choice relies on the fact that the lower the level of mortality, the more heavily will infant deaths be concentrated at the earliest stages of infancy [@preston2000demography], and therefore the values of $_1A_0$ and  $_4A_1$ depend on the values of $_1q_0$ and $_4q_1$ [@Coale1983]. For age 0, there are two choices for the rule of thumb to apply: Andreev-Kingkade is the default, but the Coale-Demeny model pattern is also available. The `UN` option also relies on the Coale-Demeny rule of thumb for ages 1-4. For ages 5-9 onwards, it uses the formula developed by @greville1977short. 
 
 ## Graduation as a lifetable step
 Abridged lifetables can be constructed with their own consequential $_nA_x$ assumptions, or they can be constructed by single ages and make a simpler lifetables after graduation. In the latter case the $_nA_x$ assumption is partly outsourced to the graduation and partly simplified by assuming midpoints (usually). The basic idea is that graduation followed by single age lifetable will produce an implied $nAx$, which can be backed out. The alternative is to do something sophisticated to get nAx well behaved at the level of abridged lifetables. 
 
 ## Closing the lifetable
 
-An important step in the construction of lifetables is the choice to deal with the open-ended age interval. A standard way to deal with this final age group is by taking the inverse of the final $_nM_x$ as life expectancy for that age group. `DemoTools` instead offers variety of extrapolation methods implemented in the `MortalityLaws` package. For this, a desired open age must be specified, defaulting to the present open age group, but which can not exceed 110 in the present implementation. By default, the extrapolation model is fit to ages 60 and higher, but the user can control this using the `extrapFit` argument (give the vector of ages, which must be a subset of `Age`). By default extrapolated values are used  starting with the input open age, but you can lower this age using the  `extrapFrom` argument. The choices given in this implementation are: Kannisto, Kannisto-Makeham, Makeham, Gompertz, Gamma-Gompertz, Beard,	Beard-Makeham and the Quadratic models. Documentation for these models can be foun in `MortalityLaws`.
+An important step in the construction of lifetables is the choice to deal with the open-ended age interval. A standard way to deal with this final age group is by taking the inverse of the final $_nM_x$ as life expectancy for that age group. `DemoTools` instead offers variety of extrapolation methods implemented in the `MortalityLaws` package. For this, a desired open age must be specified, defaulting to the present open age group, but which can not exceed 110 in the present implementation. By default, the extrapolation model is fit to ages 60 and higher, but the user can control this using the `extrapFit` argument (give the vector of ages, which must be a subset of `Age`). By default extrapolated values are used  starting with the input open age, but you can lower this age using the  `extrapFrom` argument. The choices given in this implementation are: Kannisto, Kannisto-Makeham, Makeham, Gompertz, Gamma-Gompertz, Beard,	Beard-Makeham and the Quadratic models. Documentation for these models can be found in `MortalityLaws`.
 
 ## Example with `DemoTools`
 
@@ -66,9 +66,9 @@ library(DT)
  
 ```
 
-In this output, the `Age` column indicates the lower bound of each age group. The column `nMx` are the observed death rates, usually result from dividing the deaths observed in a calendar year by the mid-year population. The `nAX`, as described above, indicates the average number of person-years lived in the age-group by those who died. The column `lx` refers to the survival function. If divided by the initial value, i.e. 100,000, indicates the probability of surviving from birth to a given age. `ndx` is the age-at-death distribution and `Sx` are the survivorship ratios. Finally, `ex` is the life expectancy. For this example, males in Mexico had a life expectancty at birth of 52.58.
+In this output, the `Age` column indicates the lower bound of each age group. The column `nMx` are the observed death rates, usually result from dividing the deaths observed in a calendar year by the mid-year population. The `nAX`, as described above, indicates the average number of person-years lived in the age-group by those who died. The column `lx` refers to the survival function. If divided by the initial value, i.e. 100,000, indicates the probability of surviving from birth to a given age. `ndx` is the age-at-death distribution and `Sx` are the survivorship ratios. Finally, `ex` is the life expectancy. For this example, males in Mexico had a life expectancy at birth of 52.58.
 
 
 
 
-## References
+# References
diff --git a/vignettes/migration_with_demotools.Rmd b/vignettes/migration_with_demotools.Rmd
index 6ee13469f..db005db39 100644
--- a/vignettes/migration_with_demotools.Rmd
+++ b/vignettes/migration_with_demotools.Rmd
@@ -206,6 +206,6 @@ rc_res[["fit_df"]] %>%
 
 #### Comments about warnings
 
-When running `mc_estimate_rc` it is common to see warnings from Stan, particularly when the retirement and post-retirement families are included in the model. Warnings about divergent transitions, low ESS and maximum treedepth can often be eliminated by changing the Stan settings to have a larger `adapt_delta`, `iter` and `maximum_treedepth`, respectively. If warnings persist it is not the end of the world; the results are probably still fine. The warnings are largely a consequence of the complexity of the function being fitted and the strong correlations that exist between estimates of different parameters. Work is still on going investigating different prior set-ups to improve the efficiency of fit.
+When running `mc_estimate_rc` it is common to see warnings from Stan, particularly when the retirement and post-retirement families are included in the model. Warnings about divergent transitions, low ESS and maximum tree depth can often be eliminated by changing the Stan settings to have a larger `adapt_delta`, `iter` and `maximum_treedepth`, respectively. If warnings persist it is not the end of the world; the results are probably still fine. The warnings are largely a consequence of the complexity of the function being fitted and the strong correlations that exist between estimates of different parameters. Work is still on going investigating different prior set-ups to improve the efficiency of fit.
 
 ## References
diff --git a/vignettes/smoothing_with_demotools.Rmd b/vignettes/smoothing_with_demotools.Rmd
index fff36a8e6..8206937d9 100644
--- a/vignettes/smoothing_with_demotools.Rmd
+++ b/vignettes/smoothing_with_demotools.Rmd
@@ -23,7 +23,7 @@ knitr::opts_chunk$set(
 
 ## How to use `DemoTools` to smooth population counts
 
-Smoothing data over age is traditionally intended to have plausible/corrected estimates of population counts from census data. Smoothing procedures help to derive figures that are corrected primarly for net error by fitting different curves to the original 5 or 10-year totals, modifying the original counts [@siegel2004methods]. Several methods have been developed for this aim and the major smoothing methods are included in `DemoTools`. Including the Carrier-Farrag [@carrier1959reduction], Arriaga [@arriaga1994population], Karup-King-Newton, United Stations [@united1955manual], Spencer [@spencer1987improvements] and Zelnik methods. Below we briefly give an overview of the method and apply them to the male Indian population in 1991.
+Smoothing data over age is traditionally intended to have plausible/corrected estimates of population counts from census data. Smoothing procedures help to derive figures that are corrected primarily for net error by fitting different curves to the original 5 or 10-year totals, modifying the original counts [@siegel2004methods]. Several methods have been developed for this aim and the major smoothing methods are included in `DemoTools`. Including the Carrier-Farrag [@carrier1959reduction], Arriaga [@arriaga1994population], Karup-King-Newton, United Stations [@united1955manual], Spencer [@spencer1987improvements] and Zelnik methods. Below we briefly give an overview of the method and apply them to the male Indian population in 1991.
 
 ```{r,out.width='\\textwidth', fig.width= 6, fig.height=6, fig.align='center'}
 library(DemoTools)
@@ -42,7 +42,7 @@ plot(Age, Value/sum(Value), type = 'l',
 
 ### Carrier-Farrag
 
-This method considers the ratio, $K$, of the population in one quinary age-group to the next one [@carrier1959reduction]. If $v_0$ is a denary age-group, and $v_{-2}$ and $v_2$ are the preceding and succeeding age-groups, respectively, and if $K^4 = v_{-2}/v_2$. Then, the older quinary group $v_1$ can be estimated by $v_0/(1+K)$. This equation connects the population in two ten-year age groups separated by an interval of ten years. Therefore the value $K$ can be seen as the middle point between the two ten-year age groups. To run this method in `DemoTools` the function `afesmth` is used with the option 'Carrier-Farrag'. The figure below  shows the smoothed population by five-year age groups.
+This method considers the ratio, $K$, of the population in one five-year age-group to the next one [@carrier1959reduction]. If $v_0$ is a ten-year age-group, and $v_{-2}$ and $v_2$ are the preceding and succeeding age-groups, respectively, and if $K^4 = v_{-2}/v_2$. Then, the older five-year group $v_1$ can be estimated by $v_0/(1+K)$. This equation connects the population in two ten-year age groups separated by an interval of ten years. Therefore the value $K$ can be seen as the middle point between the two ten-year age groups. To run this method in `DemoTools` the function `afesmth` is used with the option 'Carrier-Farrag'. The figure below  shows the smoothed population by five-year age groups.
 
 ```{r,out.width='\\textwidth', fig.width= 6, fig.height=6, fig.align='center'}
 
@@ -122,7 +122,7 @@ and
 _{5}P_{x+5} = _{10}P_{x} - _{5}P_{x}.
 \end{equation*}
 
-To implement this smoothing process select the `KKN` inthe `agesmth` function.
+To implement this smoothing process select the `KKN` in the `agesmth` function.
 
 ```{r,out.width='\\textwidth', fig.width= 6, fig.height=6, fig.align='center', eval = FALSE}
 # TODO smooth_age_5 is throwing an internal error
@@ -168,7 +168,7 @@ The Strong formula adjusts proportionally the smoothed 10-year age groups to the
 _{10}\hat{P}_x = \frac{_{10}P_{x-10} + 2\, _10P_{x} + _{10}P_{x+10}}{4}
 \end{equation}
 
-where $_{10}\hat{P}_x$ represents the smoothed poopulation ages $x$ to $x+9$. It is implemented in `DemoTools` as follows
+where $_{10}\hat{P}_x$ represents the smoothed population ages $x$ to $x+9$. It is implemented in `DemoTools` as follows
 
 
 ```{r,out.width='\\textwidth', fig.width= 6, fig.height=6, fig.align='center'}
@@ -183,7 +183,7 @@ axis(1, labels = paste0(seq(0,100,5),'-',seq(4,104,5)), at =seq(0,100,5))
 
 ### Zigzag
 
-The implementatios of this methods assumes that persons incorrectly reported in peak age groups are evenly divided between the two adjacent age groups [@feeney2013]. It relies in minimizing a measure of "roughness":
+The implementations of this methods assumes that persons incorrectly reported in peak age groups are evenly divided between the two adjacent age groups [@feeney2013]. It relies in minimizing a measure of "roughness":
 consider the difference $R[i]$ between the number of persons in the $i$-th age group and the
 average of the numbers in adjacent age groups, $R[i] = N[i] - (N[i -1] + N[i + 1])/2$.
 If the distribution displays zigzag, the $R[i]$ will relatively large. If the distribution is smooth,
@@ -203,7 +203,7 @@ axis(1, labels = paste0(seq(0,100,5),'-',seq(4,104,5)), at =seq(0,100,5))
 
 
 ### Polynomial
-A more general way is to smooth trhough linear models. Polynomial  fitting is used to mooth data over age or time fitting linear models. It can be tweaked by changing the degree and by either log or power transforming and can be used on any age groups, including irregularly spaced, single age, or 5-year age groups. It can be implemented in `DemoTools` with the function `agesmth1` and the option `poly` as follows
+A more general way is to smooth through linear models. Polynomial  fitting is used to smooth data over age or time fitting linear models. It can be tweaked by changing the degree and by either log or power transforming and can be used on any age groups, including irregularly spaced, single age, or 5-year age groups. It can be implemented in `DemoTools` with the function `agesmth1` and the option `poly` as follows
 
 ```{r,out.width='\\textwidth', fig.width= 6, fig.height=6, fig.align='center'}