Import and export

DESCRIPTION

@@ -33,6 +33,7 @@ Imports:
     dplyr (>= 1.0.0),
     future.apply,
     generics,
+    ggplot2,
     lubridate,
     magrittr,
     Matrix,
@@ -47,7 +48,6 @@ RoxygenNote: 7.3.1
 Suggests: 
     testthat (>= 3.0.0),
     knitr,
-    rmarkdown,
-    ggplot2
+    rmarkdown
 Language: en-AU
 Config/testthat/edition: 3

NAMESPACE

@@ -20,30 +20,48 @@ importFrom(dplyr,all_of)
 importFrom(dplyr,any_of)
 importFrom(dplyr,arrange)
 importFrom(dplyr,bind_rows)
+importFrom(dplyr,case_when)
 importFrom(dplyr,distinct)
+importFrom(dplyr,ends_with)
 importFrom(dplyr,filter)
 importFrom(dplyr,group_by)
 importFrom(dplyr,group_split)
 importFrom(dplyr,lag)
 importFrom(dplyr,lead)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
+importFrom(dplyr,n)
 importFrom(dplyr,pull)
 importFrom(dplyr,rename)
 importFrom(dplyr,rename_with)
+importFrom(dplyr,rowwise)
 importFrom(dplyr,select)
 importFrom(dplyr,ungroup)
 importFrom(generics,augment)
+importFrom(ggplot2,aes)
 importFrom(ggplot2,autoplot)
+importFrom(ggplot2,facet_grid)
+importFrom(ggplot2,geom_line)
+importFrom(ggplot2,ggplot)
+importFrom(lubridate,"day<-")
+importFrom(lubridate,day)
 importFrom(lubridate,days)
+importFrom(lubridate,month)
+importFrom(lubridate,wday)
+importFrom(lubridate,years)
+importFrom(lubridate,ymd)
 importFrom(magrittr,"%>%")
 importFrom(rlang,"!!")
 importFrom(rlang,"%||%")
 importFrom(rlang,":=")
 importFrom(rlang,.data)
 importFrom(rlang,enexpr)
+importFrom(rlang,enexprs)
 importFrom(rlang,sym)
+importFrom(rlang,syms)
+importFrom(stats,runif)
 importFrom(stats,var)
 importFrom(tibble,as_tibble)
 importFrom(tibble,tibble)
+importFrom(tidyr,unnest)
 useDynLib(ycevo)

R/augment.R

@@ -1,4 +1,5 @@
 
+#' Augment data with predicted discount function and yield curve
 #' @param ... Additional arguments required for generic consistency. Currently not used.
 #' Warning: A misspelled argument will not raise an error. 
 #' The misspelled argument will be either disregarded, or the default value will be applied if one exists.
@@ -23,7 +24,7 @@ augment.ycevo <- function(
 
   # newdata <- tibble(qdatetime, tupq, tau)
   if(!loess) {
-    norow <- anti_join(newdata, df_flat, by = setdiff(c("tau"), colnames(newdata)))
+    norow <- dplyr::anti_join(newdata, df_flat, by = setdiff(c("tau"), colnames(newdata)))
     if(nrow(norow)>0) {
       stop("If loess = FALSE, newdata should be a subset of the xgrid and tau used to fit ycevo (e.g. the default).")
     }
@@ -62,7 +63,7 @@ interpolate <- function(object, newdata, qdate_label){
       stats::loess(.discount ~ tau, 
                    # interpolate on the log of discount to avoid negative values
                    data = mutate(data, .discount = log(.discount)),
-                   control = loess.control(surface = "direct")))) %>% 
+                   control = stats::loess.control(surface = "direct")))) %>% 
     select(-.est)
 
   # list of unique values of covariates (including time)
@@ -77,7 +78,7 @@ interpolate <- function(object, newdata, qdate_label){
   find_near <- function(x){
     # x <- seq(ymd("2023-02-01"), ymd("2023-07-01"), by = "1 month")
     # browser()
-    target <- getElement(ls_x, cur_column())
+    target <- getElement(ls_x, dplyr::cur_column())
     l <- length(target)
 
     int <- match(x, target)
@@ -108,14 +109,14 @@ interpolate <- function(object, newdata, qdate_label){
 
   df_predict <- df_near %>%
     # nest by loess to speed up prediction 
-    nest(.by = ends_with(near.)) %>%
+    tidyr::nest(.by = ends_with(near.)) %>%
     # rename back to match loess name
     rename_with(function(x) gsub(near.,"", x),  ends_with(near.)) %>%
     # match loess
     left_join(df_loess, by = c(qdate_label, xnames)) %>%
     # predict discount rate
     mutate(.discount = mapply(function(data, loess){
-      predict(loess, data$tau)
+      stats::predict(loess, data$tau)
     }, data = data, loess = loess, SIMPLIFY = FALSE)) %>% 
     # drop loess
     select(-loess) %>% 
@@ -126,13 +127,13 @@ interpolate <- function(object, newdata, qdate_label){
 
   df_temp <- df_predict %>% 
     group_by(!!!syms(c(ax, "tau"))) %>% 
-    summarise(.discount = interp(list(!!!syms(ax.)), 
+    dplyr::summarise(.discount = interp(list(!!!syms(ax.)), 
                                  .discount, 
                                  lapply(list(!!!syms(ax)), unique)), 
               .groups = "drop") %>% 
     # the interpolation was done on the log of discount
     # to prevent negative values
-    mutate(.discount = exp(.discount)) %>% 
+    mutate(.discount = exp(.data$.discount)) %>% 
     mutate(.yield = discount2yield(.discount, tau))
   left_join(newdata, df_temp, by = c(qdate_label, xnames, "tau"))
 }
@@ -181,7 +182,9 @@ interp2 <- function(x, y, xout) {
     if(is.vector(g)) g <- t(g)
     # skip when there is only one value for that dimension
     if(dim(g)[[ds[[d]]]] == 1) next
-    g <- apply(g, d, function(y) approx(x = ux[[ds[[d]]]], y = y, xout = xout[[ds[[d]]]], rule = 2)$y)
+    g <- apply(g, d, function(y) stats::approx(x = ux[[ds[[d]]]], y = y, xout = xout[[ds[[d]]]], rule = 2)$y)
   }
   unname(as.vector(g))
 }
+
+

R/autoplot.R

@@ -1,4 +1,4 @@
-#' @importFrom ggplot2 autoplot
+#' @importFrom ggplot2 autoplot ggplot aes geom_line facet_grid
 #' @export
 autoplot.ycevo <- function(
     object, est = c("both", "discount", "yield"),
@@ -14,7 +14,7 @@ autoplot.ycevo <- function(
   df_plot <- augment(object, loess = loess) %>% 
     mutate(!!sym(qdate_label)) %>% 
     select(!all_of(which_drop)) %>% 
-    pivot_longer(any_of(c(".discount", ".yield")), 
+    tidyr::pivot_longer(any_of(c(".discount", ".yield")), 
                  names_to = ".est", 
                  values_to = ".value")
   against <- match.arg(against)

R/simulation.R

@@ -1,9 +1,8 @@
+#' @importFrom lubridate wday day month day<- years
 #' @export
 ycevo_data <- function() {
   first_qdate <- ymd("20230101")
-  last_qdate <- first_qdate
-  month(last_qdate) <- 12
-  day(last_qdate) <- 31
+  last_qdate <- ymd("20231231")
 
   ad <- seq(ymd("2023-01-01"), ymd("2023-12-31"), by = "1 day")
   wd <- ad[wday(ad, week_start = 1) < 6]
@@ -39,7 +38,7 @@ ycevo_data <- function() {
     }
     out
   }
-  bond_meta <- tribble(~ type, ~ n, ~ arg_range_issued, ~ maturity,
+  bond_meta <- tibble::tribble(~ type, ~ n, ~ arg_range_issued, ~ maturity,
                        1, 40, c(20, -1), 20,
                        2, 40, c(2, -1), 2,
                        2, 40, c(3, -1), 3,

R/ycevo-package.R

@@ -19,21 +19,29 @@
 #' @importFrom dplyr any_of
 #' @importFrom dplyr arrange
 #' @importFrom dplyr bind_rows
+#' @importFrom dplyr case_when
 #' @importFrom dplyr distinct
+#' @importFrom dplyr ends_with
 #' @importFrom dplyr filter left_join mutate select group_by lead lag group_split ungroup
+#' @importFrom dplyr n
 #' @importFrom dplyr pull
 #' @importFrom dplyr rename
 #' @importFrom dplyr rename_with
+#' @importFrom dplyr rowwise
+#' @importFrom lubridate ymd
 #' @importFrom magrittr %>%
 #' @importFrom Matrix colSums rowSums sparseMatrix t
 #' @importFrom Rcpp evalCpp
 #' @importFrom Rcpp sourceCpp
+#' @importFrom rlang :=
 #' @importFrom rlang !! sym .data
 #' @importFrom rlang %||%
-#' @importFrom rlang :=
+#' @importFrom rlang syms
+#' @importFrom stats runif
 #' @importFrom stats var
 #' @importFrom tibble as_tibble
 #' @importFrom tibble tibble
+#' @importFrom tidyr unnest
 ## usethis namespace: end
 #' @useDynLib ycevo
 #' @references Koo, B., La Vecchia, D., & Linton, O. (2021). Estimation of a nonparametric model for bond prices from cross-section and time series information. Journal of Econometrics, 220(2), 562-588.

R/ycevo.R

@@ -97,7 +97,7 @@
 #' 
 #' @references Koo, B., La Vecchia, D., & Linton, O. (2021). Estimation of a nonparametric model for bond prices from cross-section and time series information. Journal of Econometrics, 220(2), 562-588.
 #' @order 1
-#' @importFrom rlang enexpr
+#' @importFrom rlang enexpr enexprs
 #' @importFrom lubridate days
 #' @export
 ycevo <- function(data, 
@@ -154,7 +154,7 @@ ycevo <- function(data,
     stop(paste0(names(dots)[temp], collapse = ", "), " column(s) not found in the data")
   }
 
-  xgrid <- ecdf(data$qdate)(x)
+  xgrid <- stats::ecdf(data$qdate)(x)
 
   # Handle interest rate
   interest <- NULL
@@ -275,7 +275,6 @@ check_hx <- function(xgrid, hx, data){
   hx
 }
 
-#' @export
 seq_tau <- function(max_tau) {
   tau <-  c(seq(30, 6 * 30, 30),  # Monthly up to six months
             seq(240, 2 * 365, 60),  # Two months up to two years