diff --git a/CITATION.cff b/CITATION.cff
index d1aecba..c00fd22 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -2,6 +2,6 @@ message: "If you use this software, please cite it as below."
 authors:
 - family-names: "Cook County Assessor's Office"
 title: "AssessR"
-version: 0.6.0
+version: 1.0.0
 date-released: 2019-01-01
 url: "https://github.com/ccao-data/assessr"
diff --git a/DESCRIPTION b/DESCRIPTION
index 23ad8f3..253643e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,13 +1,14 @@
 Package: assessr
 Type: Package
 Title: Measure Property Assessment Performance
-Version: 0.6.0
+Version: 1.0.0
 Authors@R: c(
     person(given = "Dan", family = "Snow", email="daniel.snow@cookcountyil.gov", role=c("aut", "cre")),
     person(given = "William", family = "Ridgeway", email="william.ridgeway@cookcountyil.gov", role=c("ctb")),
     person(given = "Rob", family = "Ross", role=c("ctb")),
     person(given = "Nathan", family = "Dignazio", role=c("ctb")),
-    person(given = "Damon", family = "Major", role=c("ctb"))
+    person(given = "Damon", family = "Major", role=c("ctb")),
+    person(given = "Jean", family = "Cochrane", role=c("ctb"))
     )
 Date: 2023-08-07
 Description: An R package to measure the performance of property assessments
@@ -19,7 +20,7 @@ Encoding: UTF-8
 LazyData: true
 Imports:
     stats
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 Suggests:
     covr,
     devtools,
@@ -37,5 +38,5 @@ Suggests:
     testthat,
     tibble,
     tidyr
-Depends: 
+Depends:
     R (>= 3.5.0)
diff --git a/NAMESPACE b/NAMESPACE
index be398f2..96d3996 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -4,8 +4,8 @@ export(boot_ci)
 export(cod)
 export(cod_ci)
 export(cod_met)
-export(detect_chasing)
 export(is_outlier)
+export(is_sales_chased)
 export(ki)
 export(mki)
 export(mki_met)
diff --git a/R/ci.R b/R/ci.R
index f6c6edb..9c1a1a3 100644
--- a/R/ci.R
+++ b/R/ci.R
@@ -4,14 +4,17 @@
 #'   for a given numeric input and a chosen function.
 #'
 #' @param FUN Function to bootstrap. Must return a single value.
-#' @param nboot Default 100. Number of iterations to use to estimate
+#' @param estimate A numeric vector of estimated values. Must be the same
+#'   length as \code{sale_price}.
+#' @param sale_price A numeric vector of sale prices. Must be the same
+#'   length as \code{estimate}.
+#' @param nboot Default 1000. Number of iterations to use to estimate
 #'   the output statistic confidence interval.
 #' @param alpha Default 0.05. Numeric value indicating the confidence
 #'   interval to return. 0.05 will return the 95\% confidence interval.
 #' @param na.rm Default FALSE. A boolean value indicating whether or not to
 #'   remove NA values. If missing values are present but not removed the
 #'   function will output NA.
-#' @param ... Named arguments passed on to \code{FUN}.
 #'
 #' @return A two-long numeric vector containing the bootstrapped confidence
 #'   interval of the input vector(s).
@@ -19,30 +22,39 @@
 #' @examples
 #'
 #' # Calculate COD confidence interval
-#' boot_ci(cod, nboot = 100, ratio = ratios_sample$ratio)
+#' boot_ci(cod, ratios_sample$estimate, ratios_sample$sale_price, nboot = 100)
 #'
 #' # Calculate PRD confidence interval
 #' boot_ci(
 #'   prd,
 #'   nboot = 100,
-#'   assessed = ratios_sample$assessed,
+#'   estimate = ratios_sample$estimate,
 #'   sale_price = ratios_sample$sale_price,
 #'   na.rm = FALSE
 #' )
 #' @export
-boot_ci <- function(FUN = NULL, nboot = 100, alpha = 0.05, na.rm = FALSE, ...) { # nolint
+boot_ci <- function(
+    FUN,
+    estimate,
+    sale_price,
+    nboot = 1000,
+    alpha = 0.05,
+    na.rm = FALSE) {
+  # Input checking and error handling
+  check_inputs(estimate, sale_price)
 
   # Check that the input function returns a numeric vector
-  est <- FUN(...)
+  est <- FUN(estimate, sale_price)
   stopifnot(
     length(est) == 1,
     is.numeric(est),
-    is.logical(na.rm)
+    is.logical(na.rm),
+    nboot > 0
   )
 
   # Create an index of missing values, where TRUE when missing.
   # If na.rm is FALSE and index contains TRUE, return NA
-  missing_idx <- index_na(...)
+  missing_idx <- index_na(estimate, sale_price)
   if (any(missing_idx) && !na.rm) {
     return(NA_real_)
   }
@@ -62,7 +74,10 @@ boot_ci <- function(FUN = NULL, nboot = 100, alpha = 0.05, na.rm = FALSE, ...) {
     # For each of the input vectors to FUN, subset by first removing any
     # index positions that have a missing value, then take a random sample of
     # each vector using the sample index
-    sampled <- lapply(list(...), function(x) x[!missing_idx][idx])
+    sampled <- lapply(
+      list(estimate, sale_price),
+      function(x) x[!missing_idx][idx]
+    )
 
     # For each bootstrap sample, apply the function and output an estimate for
     # that sample
@@ -85,16 +100,21 @@ boot_ci <- function(FUN = NULL, nboot = 100, alpha = 0.05, na.rm = FALSE, ...) {
 #' @examples
 #'
 #' # Calculate COD confidence interval
-#' cod_ci(ratios_sample$ratio)
+#' cod_ci(ratios_sample$estimate, ratios_sample$sale_price)
 #' @export
-cod_ci <- function(ratio, nboot = 100, alpha = 0.05, na.rm = FALSE) { # nolint
-
+cod_ci <- function(
+    estimate,
+    sale_price,
+    nboot = 1000,
+    alpha = 0.05,
+    na.rm = FALSE) {
   cod_ci <- boot_ci(
     cod,
+    estimate = estimate,
+    sale_price = sale_price,
     nboot = nboot,
     alpha = alpha,
-    na.rm = na.rm,
-    ratio = ratio
+    na.rm = na.rm
   )
 
   return(cod_ci)
@@ -108,17 +128,21 @@ cod_ci <- function(ratio, nboot = 100, alpha = 0.05, na.rm = FALSE) { # nolint
 #' @examples
 #'
 #' # Calculate PRD confidence interval
-#' prd_ci(ratios_sample$assessed, ratios_sample$sale_price)
+#' prd_ci(ratios_sample$estimate, ratios_sample$sale_price)
 #' @export
-prd_ci <- function(assessed, sale_price, nboot = 100, alpha = 0.05, na.rm = FALSE) { # nolint
-
+prd_ci <- function(
+    estimate,
+    sale_price,
+    nboot = 1000,
+    alpha = 0.05,
+    na.rm = FALSE) {
   prd_ci <- boot_ci(
     prd,
+    estimate = estimate,
+    sale_price = sale_price,
     nboot = nboot,
     alpha = alpha,
-    na.rm = na.rm,
-    assessed = assessed,
-    sale_price = sale_price
+    na.rm = na.rm
   )
 
   return(prd_ci)
@@ -132,25 +156,24 @@ prd_ci <- function(assessed, sale_price, nboot = 100, alpha = 0.05, na.rm = FALS
 #' @examples
 #'
 #' # Calculate PRD confidence interval
-#' prb_ci(ratios_sample$assessed, ratios_sample$sale_price)
+#' prb_ci(ratios_sample$estimate, ratios_sample$sale_price)
 #' @export
-prb_ci <- function(assessed, sale_price, alpha = 0.05, na.rm = FALSE) { # nolint
-
+prb_ci <- function(estimate, sale_price, alpha = 0.05, na.rm = FALSE) {
   # Input checking and error handling
-  check_inputs(assessed, sale_price)
+  check_inputs(estimate, sale_price)
 
   # Remove NAs from input vectors. Otherwise, return NA if the input vectors
   # contain any NA values
-  idx <- index_na(assessed, sale_price)
+  idx <- index_na(estimate, sale_price)
   if (na.rm) {
-    assessed <- assessed[!idx]
+    estimate <- estimate[!idx]
     sale_price <- sale_price[!idx]
   } else if (any(idx) && !na.rm) {
     return(NA_real_)
   }
 
   # Calculate PRB model
-  prb_model <- calc_prb(assessed, sale_price)
+  prb_model <- calc_prb(estimate, sale_price)
 
   # Extract PRB CI from model
   prb_ci <- stats::confint(prb_model, level = (1 - alpha))[2, ]
diff --git a/R/data.R b/R/data.R
index 550efdd..8f2824f 100644
--- a/R/data.R
+++ b/R/data.R
@@ -3,13 +3,30 @@
 #' This sample was take from Evanston and New Trier in 2019. Ratios are
 #' calculated using assessor certified (post-appeal) fair market values.
 #'
-#' @format A data frame with 979 observation and 4 variables:
+#' @format A data frame with 979 observation and 3 variables:
 #' \describe{
-#'   \item{assessed}{The fair market assessed value predicted by CCAO assessment
+#'   \item{estimate}{The fair market assessed value predicted by CCAO assessment
 #'     models, including any successful appeals}
 #'   \item{sale_price}{The recorded sale price of this property}
-#'   \item{ratio}{Sales ratio representing fair market value / sale price}
 #'   \item{town}{Township name the property is in}
 #' }
 #'
 "ratios_sample"
+
+#' Sample of sales and estimated market values provided by Quintos in the
+#'    following MKI papers:
+#'
+#' @references
+#' Quintos, C. (2020). A Gini measure for vertical equity in property
+#' assessments. <https://researchexchange.iaao.org/jptaa/vol17/iss2/2>
+#'
+#' Quintos, C. (2021). A Gini decomposition of the sources of inequality in
+#' property assessments. <https://researchexchange.iaao.org/jptaa/vol18/iss2/6>
+#'
+#' @format A data frame with 30 observation and 2 variables:
+#' \describe{
+#'   \item{estimate}{Assessed fair market value}
+#'   \item{sale_price}{Recorded sale price of this property}
+#' }
+#'
+"quintos_sample"
diff --git a/R/formulas.R b/R/formulas.R
index 3df4495..50a8954 100644
--- a/R/formulas.R
+++ b/R/formulas.R
@@ -20,9 +20,10 @@
 #'   \href{https://www.iaao.org/media/standards/Standard_on_Ratio_Studies.pdf}{IAAO Standard on Ratio Studies}
 #'   Appendix B.1.
 #'
-#' @param ratio A numeric vector of ratios centered around 1, where the
-#'   numerator of the ratio is the estimated fair market value and the
-#'   denominator is the actual sale price.
+#' @param estimate A numeric vector of assessed values. Must be the same
+#'   length as \code{sale_price}.
+#' @param sale_price A numeric vector of sale prices. Must be the same length
+#'   as \code{estimate}.
 #' @param na.rm Default FALSE. A boolean value indicating whether or not to
 #'   remove NA values. If missing values are present but not removed the
 #'   function will output NA.
@@ -32,13 +33,15 @@
 #'
 #' @examples
 #' # Calculate COD
-#' cod(ratios_sample$ratio)
+#' cod(ratios_sample$estimate, ratios_sample$sale_price)
 #' @family formulas
 #' @export
-cod <- function(ratio, na.rm = FALSE) {
+cod <- function(estimate, sale_price, na.rm = FALSE) {
   # nolint end
 
-  check_inputs(ratio)
+  check_inputs(estimate, sale_price)
+  ratio <- estimate / sale_price
+
   if (na.rm) ratio <- stats::na.omit(ratio)
 
   # Calculate median ratio
@@ -71,10 +74,10 @@ cod <- function(ratio, na.rm = FALSE) {
 #'   as it is extremely sensitive to large outliers. PRD is being deprecated in
 #'   favor of PRB, which is less sensitive to outliers and easier to interpret.
 #'
-#' @param assessed A numeric vector of assessed values. Must be the same
+#' @param estimate A numeric vector of assessed values. Must be the same
 #'   length as \code{sale_price}.
 #' @param sale_price A numeric vector of sale prices. Must be the same length
-#'   as \code{assessed}.
+#'   as \code{estimate}.
 #'
 #' @inheritParams cod
 #' @describeIn prd Returns a numeric vector containing the PRD of the
@@ -83,23 +86,23 @@ cod <- function(ratio, na.rm = FALSE) {
 #'
 #' @examples
 #' # Calculate PRD
-#' prd(ratios_sample$assessed, ratios_sample$sale_price)
+#' prd(ratios_sample$estimate, ratios_sample$sale_price)
 #' @family formulas
 #' @export
-prd <- function(assessed, sale_price, na.rm = FALSE) {
+prd <- function(estimate, sale_price, na.rm = FALSE) {
   # nolint end
 
-  check_inputs(assessed, sale_price)
-  idx <- index_na(assessed, sale_price)
+  check_inputs(estimate, sale_price)
+  idx <- index_na(estimate, sale_price)
   if (na.rm) {
-    assessed <- assessed[!idx]
+    estimate <- estimate[!idx]
     sale_price <- sale_price[!idx]
   } else if (any(idx) && !na.rm) {
     return(NA_real_)
   }
 
   # Calculate ratio of assessed values to sale price
-  ratio <- assessed / sale_price
+  ratio <- estimate / sale_price
 
   # Calculate PRD
   prd <- mean(ratio) / stats::weighted.mean(ratio, sale_price)
@@ -112,11 +115,11 @@ prd <- function(assessed, sale_price, na.rm = FALSE) {
 ##### PRB #####
 
 # Calculate PRB and return model object
-calc_prb <- function(assessed, sale_price) {
-  ratio <- assessed / sale_price
+calc_prb <- function(estimate, sale_price) {
+  ratio <- estimate / sale_price
   med_ratio <- stats::median(ratio)
   lhs <- (ratio - med_ratio) / med_ratio
-  rhs <- log(((assessed / med_ratio) + sale_price) * 0.5) / log(2)
+  rhs <- log(((estimate / med_ratio) + sale_price) * 0.5) / log(2)
   prb_model <- stats::lm(formula = lhs ~ rhs)
 
   return(prb_model)
@@ -146,24 +149,24 @@ calc_prb <- function(assessed, sale_price) {
 #'
 #' @examples
 #' # Calculate PRB
-#' prb(ratios_sample$assessed, ratios_sample$sale_price)
+#' prb(ratios_sample$estimate, ratios_sample$sale_price)
 #' @family formulas
 #' @export
-prb <- function(assessed, sale_price, na.rm = FALSE) {
+prb <- function(estimate, sale_price, na.rm = FALSE) {
   # nolint end
 
-  check_inputs(assessed, sale_price)
+  check_inputs(estimate, sale_price)
 
-  idx <- index_na(assessed, sale_price)
+  idx <- index_na(estimate, sale_price)
   if (na.rm) {
-    assessed <- assessed[!idx]
+    estimate <- estimate[!idx]
     sale_price <- sale_price[!idx]
   } else if (any(idx) && !na.rm) {
     return(NA_real_)
   }
 
   # Calculate PRB
-  prb_model <- calc_prb(assessed, sale_price)
+  prb_model <- calc_prb(estimate, sale_price)
 
   # Extract PRB from model
   prb <- unname(stats::coef(prb_model)[2])
@@ -176,22 +179,22 @@ prb <- function(assessed, sale_price, na.rm = FALSE) {
 ##### MKI_KI #####
 
 # Calculate the Gini cofficients needed for KI and MKI
-calc_gini <- function(assessed, sale_price) {
-  df <- data.frame(av = assessed, sp = sale_price)
+calc_gini <- function(estimate, sale_price) {
+  df <- data.frame(av = estimate, sp = sale_price)
   df <- df[order(df$sp), ]
   assessed_price <- df$av
   sale_price <- df$sp
   n <- length(assessed_price)
 
   av_sum <- sum(assessed_price * seq_len(n))
-  g_assessed <- 2 * av_sum / sum(assessed_price) - (n + 1L)
-  gini_assessed <- g_assessed / n
+  g_estimate <- 2 * av_sum / sum(assessed_price) - (n + 1L)
+  gini_estimate <- g_estimate / n
 
   sale_sum <- sum(sale_price * seq_len(n))
   g_sale <- 2 * sale_sum / sum(sale_price) - (n + 1L)
   gini_sale <- g_sale / n
 
-  result <- list(gini_assessed = gini_assessed, gini_sale = gini_sale)
+  result <- list(gini_estimate = gini_estimate, gini_sale = gini_sale)
 
   return(result)
 }
@@ -240,25 +243,25 @@ calc_gini <- function(assessed, sale_price) {
 #' @examples
 #'
 #' # Calculate KI
-#' ki(ratios_sample$assessed, ratios_sample$sale_price)
+#' ki(ratios_sample$estimate, ratios_sample$sale_price)
 #' @family formulas
 #' @export
 #' @md
-ki <- function(assessed, sale_price, na.rm = FALSE) {
+ki <- function(estimate, sale_price, na.rm = FALSE) {
   # nolint end
 
-  check_inputs(assessed, sale_price)
+  check_inputs(estimate, sale_price)
 
-  idx <- index_na(assessed, sale_price)
+  idx <- index_na(estimate, sale_price)
   if (na.rm) {
-    assessed <- assessed[!idx]
+    estimate <- estimate[!idx]
     sale_price <- sale_price[!idx]
   } else if (any(idx) && !na.rm) {
     return(NA_real_)
   }
 
-  g <- calc_gini(assessed, sale_price)
-  ki <- g$gini_assessed - g$gini_sale
+  g <- calc_gini(estimate, sale_price)
+  ki <- g$gini_estimate - g$gini_sale
 
   return(ki)
 }
@@ -270,21 +273,21 @@ ki <- function(assessed, sale_price, na.rm = FALSE) {
 #'
 #' @examples
 #' # Calculate MKI
-#' mki(ratios_sample$assessed, ratios_sample$sale_price)
+#' mki(ratios_sample$estimate, ratios_sample$sale_price)
 #' @export
-mki <- function(assessed, sale_price, na.rm = FALSE) {
-  check_inputs(assessed, sale_price)
+mki <- function(estimate, sale_price, na.rm = FALSE) {
+  check_inputs(estimate, sale_price)
 
-  idx <- index_na(assessed, sale_price)
+  idx <- index_na(estimate, sale_price)
   if (na.rm) {
-    assessed <- assessed[!idx]
+    estimate <- estimate[!idx]
     sale_price <- sale_price[!idx]
   } else if (any(idx) && !na.rm) {
     return(NA_real_)
   }
 
-  g <- calc_gini(assessed, sale_price)
-  mki <- g$gini_assessed / g$gini_sale
+  g <- calc_gini(estimate, sale_price)
+  mki <- g$gini_estimate / g$gini_sale
 
   return(mki)
 }
diff --git a/R/outliers.R b/R/outliers.R
index 1abaec3..fe2cdd3 100644
--- a/R/outliers.R
+++ b/R/outliers.R
@@ -7,22 +7,24 @@
 #'   outliers. As such, it is often necessary to remove outliers before
 #'   performing a sales ratio study.
 #'
-#'   Standard method is to remove outliers that are 3 * IQR. Warnings are thrown
+#'   The IAAO standard method is to remove outliers that are 3 * IQR. Warnings are thrown
 #'   when sample size is extremely small or when the IQR is extremely narrow. See
 #'   \href{https://www.iaao.org/media/standards/Standard_on_Ratio_Studies.pdf}{IAAO Standard on Ratio Studies}
 #'   Appendix B. Outlier Trimming Guidelines for more information.
 #'
-#' @param x A numeric vector. Must be longer than 2 and not contain
-#'   \code{Inf} or \code{NaN}.
-#' @param method Default "iqr". String indicating outlier detection method.
+#' @param x A numeric vector, typically sales ratios. Must be longer than 2 and
+#'   cannot contain \code{Inf} or \code{NaN}.
+#' @param method Default \code{iqr}. String indicating outlier detection method.
 #'   Options are \code{iqr} or \code{quantile}.
-#' @param ... Named arguments passed on to methods.
+#' @param probs Upper and lower percentiles denoting outlier boundaries for
+#'   the \code{quantile} method.
+#' @param mult Multiplier for IQR to determine outlier boundaries. Default 3.
 #'
 #' @return A logical vector this same length as \code{x} indicating whether or
 #'   not each value of \code{x} is an outlier.
 #'
 #' @export
-is_outlier <- function(x, method = "iqr", ...) {
+is_outlier <- function(x, method = "iqr", probs = c(0.05, 0.95), mult = 3) {
   # nolint end
 
   # Check that inputs are well-formed numeric vector
@@ -35,10 +37,11 @@ is_outlier <- function(x, method = "iqr", ...) {
     all(is.finite(x) | is.na(x)) # All values are finite OR are NA
   })
 
-  out <- switch(method,
-    "quantile" = quantile_outlier(x, na.rm = TRUE, ...),
-    "iqr" = iqr_outlier(x, na.rm = TRUE, ...)
-  )
+  if (method == "quantile") {
+    out <- quantile_outlier(x, probs = probs)
+  } else {
+    out <- iqr_outlier(x, mult = mult)
+  }
 
   # Warn about removing data from small samples, as it can severely distort
   # ratio study outcomes
@@ -55,8 +58,7 @@ is_outlier <- function(x, method = "iqr", ...) {
 
 #' @describeIn is_outlier Quantile method for identifying outliers.
 #' @param probs Upper and lower percentiles denoting outlier boundaries.
-quantile_outlier <- function(x, probs = c(0.05, 0.95), ...) { # nolint
-
+quantile_outlier <- function(x, probs = c(0.05, 0.95)) {
   # Determine valid range of the data
   range <- stats::quantile(x, probs = probs, na.rm = TRUE)
 
@@ -69,8 +71,7 @@ quantile_outlier <- function(x, probs = c(0.05, 0.95), ...) { # nolint
 
 #' @describeIn is_outlier IQR method for identifying outliers.
 #' @param mult Multiplier for IQR to determine outlier boundaries.
-iqr_outlier <- function(x, mult = 3, ...) { # nolint
-
+iqr_outlier <- function(x, mult = 3) {
   # Check that inputs are well-formed numeric vector
   stopifnot(is.numeric(mult), sign(mult) == 1)
 
diff --git a/R/sales_chasing.R b/R/sales_chasing.R
index f06ef25..b9b9a59 100644
--- a/R/sales_chasing.R
+++ b/R/sales_chasing.R
@@ -2,12 +2,18 @@
 #' Detect sales chasing in a vector of sales ratios
 #'
 #' @description Sales chasing is when a property is selectively reappraised to
-#'   shift its assessed value toward its actual sale price. Sales chasing is
+#'   shift its assessed value toward its recent sale price. Sales chasing is
 #'   difficult to detect. This function is NOT a statistical test and does
 #'   not provide the probability of the given result. Rather, it combines two
-#'   novel methods to roughly estimate if sales chasing has occurred.
+#'   heuristic methods to roughly estimate if sales chasing has occurred.
 #'
-#'   The first method (dist) uses the technique outlined in the
+#'   The first method (cdf) detects discontinuities in the cumulative
+#'   distribution function (CDF) of the input vector. Ratios that are not sales
+#'   chased should have a fairly smooth CDF. Discontinuous jumps in the CDF,
+#'   particularly around 1, may indicate sales chasing. This can usually be seen
+#'   visually as a "flat spot" on the CDF.
+#'
+#'   The second method (dist) uses the technique outlined in the
 #'   \href{https://www.iaao.org/media/standards/Standard_on_Ratio_Studies.pdf}{IAAO Standard on Ratio Studies}
 #'   Appendix E, Section 4. It compares the percentage of real data within +-2%
 #'   of the mean ratio to the percentage of data within the same bounds given a
@@ -15,23 +21,24 @@
 #'   The intuition here is that ratios that are sales chased may be more
 #'   "bunched up" in the center of the distribution.
 #'
-#'   The second method (cdf) detects discontinuities in the cumulative
-#'   distribution function (CDF) of the input vector. Ratios that are not sales
-#'   chased should have a fairly smooth CDF. Discontinuous jumps in the CDF,
-#'   particularly around 1, may indicate sales chasing. This can usually be seen
-#'   visually as a "flat spot" on the CDF.
-#'
-#' @param ratio A numeric vector of ratios centered around 1, where the
-#'   numerator of the ratio is the estimated fair market value and the
-#'   denominator is the actual sale price.
-#' @param method Default "both". String indicating sales chasing detection
+#' @param x A numeric vector. Must be longer than 2 and cannot contain
+#'   \code{inf} or \code{NA} values.
+#' @param method Default \code{both}. String indicating sales chasing detection
 #'   method. Options are \code{cdf}, \code{dist}, or \code{both}.
 #' @param na.rm Default FALSE. A boolean value indicating whether or not to
 #'   remove NA values. If missing values are present but not removed the
 #'   function will output NA for those values.
-#' @param ... Named arguments passed on to methods.
+#' @param bounds Default \code{(0.98, 1.02)}. Lower and upper bounds of the
+#'   range of ratios to consider when detecting sales chasing. Setting this to
+#'   a narrow band at the center of the ratio distribution prevents detecting
+#'   false positives at the tails.
+#' @param gap Default \code{0.05}. Float tuning factor. For the CDF method, it
+#'   sets the maximum percentage difference between two adjacent ratios. For the
+#'   distribution method, it sets the maximum percentage point difference
+#'   between the percentage of the data between the \code{bounds} in the real
+#'   distribution compared to the ideal distribution.
 #'
-#' @return A logical value indicating whether or not the input ratios may
+#' @return A logical value indicating whether or not the input values may
 #'   have been sales chased.
 #'
 #' @examples
@@ -42,26 +49,32 @@
 #'
 #' # Plot to view discontinuity
 #' plot(stats::ecdf(normal_ratios))
-#' detect_chasing(normal_ratios)
+#' is_sales_chased(normal_ratios)
 #'
 #' plot(stats::ecdf(chased_ratios))
-#' detect_chasing(chased_ratios)
+#' is_sales_chased(chased_ratios)
 #' @export
-detect_chasing <- function(ratio, method = "both", na.rm = FALSE, ...) {
+is_sales_chased <- function(x, method = "both", bounds = c(0.98, 1.02), gap = 0.03, na.rm = FALSE) {
   # nolint end
 
   # Check that inputs are well-formed numeric vector
   stopifnot(exprs = {
     method %in% c("cdf", "dist", "both")
-    is.vector(ratio)
-    is.numeric(ratio)
-    !is.nan(ratio)
-    length(ratio) > 2
-    all(is.finite(ratio) | is.na(ratio)) # All values are finite OR are NA
+    is.vector(x)
+    is.numeric(x)
+    !is.nan(x)
+    length(x) > 2
+    all(is.finite(x) | is.na(x)) # All values are finite OR are NA
+    is.numeric(gap)
+    gap > 0
+    gap < 1
+    is.vector(bounds)
+    is.numeric(bounds)
+    bounds[2] > bounds[1]
   })
 
   # Warn about small sample sizes
-  if (length(ratio) < 30) {
+  if (length(x) < 30) {
     warning(paste(
       "Sales chasing detection can be misleading when applied to small",
       "samples (N < 30). Increase N or use a different statistical test."
@@ -69,41 +82,22 @@ detect_chasing <- function(ratio, method = "both", na.rm = FALSE, ...) {
   }
 
   # Can't calculate ideal distribution if ratio input contains NA, so output NA
-  if (any(is.na(ratio)) && !na.rm) {
+  if (any(is.na(x)) && !na.rm) {
     return(NA)
   }
 
   out <- switch(method,
-    "cdf" = detect_chasing_cdf(ratio, ...),
-    "dist" = detect_chasing_dist(ratio, na.rm = na.rm, ...),
-    "both" = detect_chasing_cdf(ratio, ...) &
-      detect_chasing_dist(ratio, na.rm = na.rm, ...)
+    "cdf" = cdf_sales_chased(x, bounds, gap),
+    "dist" = dist_sales_chased(x, bounds, gap, na.rm = na.rm),
+    "both" = cdf_sales_chased(x, bounds, gap) &
+      dist_sales_chased(x, bounds, gap, na.rm = na.rm)
   )
 
   return(out)
 }
 
 
-#' @describeIn detect_chasing CDF gap method for detecting sales chasing.
-#' @param bounds Ratio boundaries to use for detection. The CDF method will
-#'   return TRUE if the CDF gap exceeding the threshold is found within these
-#'   bounds. The distribution method will calculate the percentage of ratios
-#'   within these bounds for the actual data and an ideal normal distribution.
-#'   Expanding these bounds increases likelihood of detection.
-#' @param cdf_gap Ratios that have bunched up around a particular value
-#'   (typically 1) will appear as a flat spot on the CDF. The longer this flat
-#'   spot, the worse the potential sales chasing. This variable indicates the
-#'   length of that flat spot and can be thought of as the proportion of ratios
-#'   that have the same value. For example, 0.03 means that 3% of ratios share
-#'   the same value.
-detect_chasing_cdf <- function(ratio, bounds = c(0.98, 1.02), cdf_gap = 0.03, ...) { # nolint
-
-  # Check that inputs are well-formed numeric vector
-  stopifnot(
-    cdf_gap > 0 & cdf_gap < 1, is.numeric(cdf_gap),
-    length(bounds) == 2, is.numeric(bounds)
-  )
-
+cdf_sales_chased <- function(ratio, bounds = c(0.98, 1.02), gap = 0.03) {
   # Sort the ratios AND REMOVE NAs
   sorted_ratio <- sort(ratio)
 
@@ -117,19 +111,17 @@ detect_chasing_cdf <- function(ratio, bounds = c(0.98, 1.02), cdf_gap = 0.03, ..
   # Check if the largest different is greater than the threshold and make sure
   # it's within the specified boundaries
   diff_loc <- sorted_ratio[which.max(diffs)]
-  out <- max(diffs) > cdf_gap & (diff_loc > bounds[1] & diff_loc < bounds[2])
+  out <- max(diffs) > gap & (diff_loc > bounds[1] & diff_loc < bounds[2])
 
   return(out)
 }
 
 
-#' @describeIn detect_chasing Distribution comparison method
-#'   for detecting sales chasing.
-detect_chasing_dist <- function(ratio, bounds = c(0.98, 1.02), na.rm = FALSE, ...) { # nolint
-
-  # Check that inputs are well-formed numeric vector
-  stopifnot(length(bounds) == 2, is.numeric(bounds))
-
+dist_sales_chased <- function(
+    ratio,
+    bounds = c(0.98, 1.02),
+    gap = 0.03,
+    na.rm = FALSE) {
   # Return the percentage of x within the specified range
   pct_in_range <- function(x, min, max) mean(x >= min & x <= max, na.rm = na.rm)
 
@@ -147,5 +139,5 @@ detect_chasing_dist <- function(ratio, bounds = c(0.98, 1.02), na.rm = FALSE, ..
   # Determine what percentage of the data is actually within the bounds
   pct_actual <- pct_in_range(ratio, bounds[1], bounds[2])
 
-  return(pct_actual > pct_ideal)
+  return(abs(pct_actual - pct_ideal) > gap)
 }
diff --git a/README.Rmd b/README.Rmd
index 2ff176b..7af8452 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -44,7 +44,7 @@ renv::install("ccao-data/assessr")
 pak::pak("ccao-data/assessr")
 
 # Append the @ symbol for a specific version
-remotes::install_github("ccao-data/assessr@0.4.4")
+remotes::install_github("ccao-data/assessr@1.0.0")
 ```
 
 Once it is installed, you can use it just like any other package. Simply call `library(assessr)` at the beginning of your script.
diff --git a/README.md b/README.md
index 6b54d96..25ce620 100644
--- a/README.md
+++ b/README.md
@@ -46,7 +46,7 @@ renv::install("ccao-data/assessr")
 pak::pak("ccao-data/assessr")
 
 # Append the @ symbol for a specific version
-remotes::install_github("ccao-data/assessr@0.4.4")
+remotes::install_github("ccao-data/assessr@1.0.0")
 ```
 
 Once it is installed, you can use it just like any other package. Simply
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 07d0562..30e5009 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -58,10 +58,11 @@ reference:
   desc: Functions for other assessment-related tasks
 - contents:
   - boot_ci
-  - detect_chasing
+  - is_sales_chased
   - is_outlier
 
 - title: Data
   desc: Sample data used for testing and demonstrations
 - contents:
   - ratios_sample
+  - quintos_sample
diff --git a/data/quintos_sample.rda b/data/quintos_sample.rda
new file mode 100644
index 0000000..6e1de34
Binary files /dev/null and b/data/quintos_sample.rda differ
diff --git a/data/ratios_sample.rda b/data/ratios_sample.rda
index 0d31e20..1589305 100644
Binary files a/data/ratios_sample.rda and b/data/ratios_sample.rda differ
diff --git a/man/boot_ci.Rd b/man/boot_ci.Rd
index a03c05f..f591022 100644
--- a/man/boot_ci.Rd
+++ b/man/boot_ci.Rd
@@ -4,12 +4,18 @@
 \alias{boot_ci}
 \title{Calculate bootstrapped confidence intervals}
 \usage{
-boot_ci(FUN = NULL, nboot = 100, alpha = 0.05, na.rm = FALSE, ...)
+boot_ci(FUN, estimate, sale_price, nboot = 1000, alpha = 0.05, na.rm = FALSE)
 }
 \arguments{
 \item{FUN}{Function to bootstrap. Must return a single value.}
 
-\item{nboot}{Default 100. Number of iterations to use to estimate
+\item{estimate}{A numeric vector of estimated values. Must be the same
+length as \code{sale_price}.}
+
+\item{sale_price}{A numeric vector of sale prices. Must be the same
+length as \code{estimate}.}
+
+\item{nboot}{Default 1000. Number of iterations to use to estimate
 the output statistic confidence interval.}
 
 \item{alpha}{Default 0.05. Numeric value indicating the confidence
@@ -18,8 +24,6 @@ interval to return. 0.05 will return the 95\% confidence interval.}
 \item{na.rm}{Default FALSE. A boolean value indicating whether or not to
 remove NA values. If missing values are present but not removed the
 function will output NA.}
-
-\item{...}{Named arguments passed on to \code{FUN}.}
 }
 \value{
 A two-long numeric vector containing the bootstrapped confidence
@@ -32,13 +36,13 @@ Calculate the non-parametric bootstrap confidence interval
 \examples{
 
 # Calculate COD confidence interval
-boot_ci(cod, nboot = 100, ratio = ratios_sample$ratio)
+boot_ci(cod, ratios_sample$estimate, ratios_sample$sale_price, nboot = 100)
 
 # Calculate PRD confidence interval
 boot_ci(
   prd,
   nboot = 100,
-  assessed = ratios_sample$assessed,
+  estimate = ratios_sample$estimate,
   sale_price = ratios_sample$sale_price,
   na.rm = FALSE
 )
diff --git a/man/cod.Rd b/man/cod.Rd
index 906bb3e..c48624a 100644
--- a/man/cod.Rd
+++ b/man/cod.Rd
@@ -6,22 +6,24 @@
 \alias{cod_met}
 \title{Calculate Coefficient of Dispersion (COD)}
 \usage{
-cod(ratio, na.rm = FALSE)
+cod(estimate, sale_price, na.rm = FALSE)
 
-cod_ci(ratio, nboot = 100, alpha = 0.05, na.rm = FALSE)
+cod_ci(estimate, sale_price, nboot = 1000, alpha = 0.05, na.rm = FALSE)
 
 cod_met(x)
 }
 \arguments{
-\item{ratio}{A numeric vector of ratios centered around 1, where the
-numerator of the ratio is the estimated fair market value and the
-denominator is the actual sale price.}
+\item{estimate}{A numeric vector of assessed values. Must be the same
+length as \code{sale_price}.}
+
+\item{sale_price}{A numeric vector of sale prices. Must be the same length
+as \code{estimate}.}
 
 \item{na.rm}{Default FALSE. A boolean value indicating whether or not to
 remove NA values. If missing values are present but not removed the
 function will output NA.}
 
-\item{nboot}{Default 100. Number of iterations to use to estimate
+\item{nboot}{Default 1000. Number of iterations to use to estimate
 the output statistic confidence interval.}
 
 \item{alpha}{Default 0.05. Numeric value indicating the confidence
@@ -60,10 +62,10 @@ COD is the average absolute percent deviation from the
 }}
 \examples{
 # Calculate COD
-cod(ratios_sample$ratio)
+cod(ratios_sample$estimate, ratios_sample$sale_price)
 
 # Calculate COD confidence interval
-cod_ci(ratios_sample$ratio)
+cod_ci(ratios_sample$estimate, ratios_sample$sale_price)
 }
 \seealso{
 Other formulas: 
diff --git a/man/detect_chasing.Rd b/man/detect_chasing.Rd
deleted file mode 100644
index 70cd37d..0000000
--- a/man/detect_chasing.Rd
+++ /dev/null
@@ -1,87 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/sales_chasing.R
-\name{detect_chasing}
-\alias{detect_chasing}
-\alias{detect_chasing_cdf}
-\alias{detect_chasing_dist}
-\title{Detect sales chasing in a vector of sales ratios}
-\usage{
-detect_chasing(ratio, method = "both", na.rm = FALSE, ...)
-
-detect_chasing_cdf(ratio, bounds = c(0.98, 1.02), cdf_gap = 0.03, ...)
-
-detect_chasing_dist(ratio, bounds = c(0.98, 1.02), na.rm = FALSE, ...)
-}
-\arguments{
-\item{ratio}{A numeric vector of ratios centered around 1, where the
-numerator of the ratio is the estimated fair market value and the
-denominator is the actual sale price.}
-
-\item{method}{Default "both". String indicating sales chasing detection
-method. Options are \code{cdf}, \code{dist}, or \code{both}.}
-
-\item{na.rm}{Default FALSE. A boolean value indicating whether or not to
-remove NA values. If missing values are present but not removed the
-function will output NA for those values.}
-
-\item{...}{Named arguments passed on to methods.}
-
-\item{bounds}{Ratio boundaries to use for detection. The CDF method will
-return TRUE if the CDF gap exceeding the threshold is found within these
-bounds. The distribution method will calculate the percentage of ratios
-within these bounds for the actual data and an ideal normal distribution.
-Expanding these bounds increases likelihood of detection.}
-
-\item{cdf_gap}{Ratios that have bunched up around a particular value
-(typically 1) will appear as a flat spot on the CDF. The longer this flat
-spot, the worse the potential sales chasing. This variable indicates the
-length of that flat spot and can be thought of as the proportion of ratios
-that have the same value. For example, 0.03 means that 3% of ratios share
-the same value.}
-}
-\value{
-A logical value indicating whether or not the input ratios may
-  have been sales chased.
-}
-\description{
-Sales chasing is when a property is selectively reappraised to
-  shift its assessed value toward its actual sale price. Sales chasing is
-  difficult to detect. This function is NOT a statistical test and does
-  not provide the probability of the given result. Rather, it combines two
-  novel methods to roughly estimate if sales chasing has occurred.
-
-  The first method (dist) uses the technique outlined in the
-  \href{https://www.iaao.org/media/standards/Standard_on_Ratio_Studies.pdf}{IAAO Standard on Ratio Studies}
-  Appendix E, Section 4. It compares the percentage of real data within +-2%
-  of the mean ratio to the percentage of data within the same bounds given a
-  constructed normal distribution with the same mean and standard deviation.
-  The intuition here is that ratios that are sales chased may be more
-  "bunched up" in the center of the distribution.
-
-  The second method (cdf) detects discontinuities in the cumulative
-  distribution function (CDF) of the input vector. Ratios that are not sales
-  chased should have a fairly smooth CDF. Discontinuous jumps in the CDF,
-  particularly around 1, may indicate sales chasing. This can usually be seen
-  visually as a "flat spot" on the CDF.
-}
-\section{Functions}{
-\itemize{
-\item \code{detect_chasing_cdf()}: CDF gap method for detecting sales chasing.
-
-\item \code{detect_chasing_dist()}: Distribution comparison method
-for detecting sales chasing.
-
-}}
-\examples{
-
-# Generate fake data with normal vs chased ratios
-normal_ratios <- c(rnorm(1000, 1, 0.15))
-chased_ratios <- c(rnorm(900, 1, 0.15), rep(1, 100))
-
-# Plot to view discontinuity
-plot(stats::ecdf(normal_ratios))
-detect_chasing(normal_ratios)
-
-plot(stats::ecdf(chased_ratios))
-detect_chasing(chased_ratios)
-}
diff --git a/man/is_outlier.Rd b/man/is_outlier.Rd
index 8f21e2e..6db38ec 100644
--- a/man/is_outlier.Rd
+++ b/man/is_outlier.Rd
@@ -6,21 +6,19 @@
 \alias{iqr_outlier}
 \title{Detect outlier values in a vector using IQR/quantile method}
 \usage{
-is_outlier(x, method = "iqr", ...)
+is_outlier(x, method = "iqr", probs = c(0.05, 0.95), mult = 3)
 
-quantile_outlier(x, probs = c(0.05, 0.95), ...)
+quantile_outlier(x, probs = c(0.05, 0.95))
 
-iqr_outlier(x, mult = 3, ...)
+iqr_outlier(x, mult = 3)
 }
 \arguments{
-\item{x}{A numeric vector. Must be longer than 2 and not contain
-\code{Inf} or \code{NaN}.}
+\item{x}{A numeric vector, typically sales ratios. Must be longer than 2 and
+cannot contain \code{Inf} or \code{NaN}.}
 
-\item{method}{Default "iqr". String indicating outlier detection method.
+\item{method}{Default \code{iqr}. String indicating outlier detection method.
 Options are \code{iqr} or \code{quantile}.}
 
-\item{...}{Named arguments passed on to methods.}
-
 \item{probs}{Upper and lower percentiles denoting outlier boundaries.}
 
 \item{mult}{Multiplier for IQR to determine outlier boundaries.}
@@ -36,7 +34,7 @@ Detect outliers in a numeric vector using standard methods.
   outliers. As such, it is often necessary to remove outliers before
   performing a sales ratio study.
 
-  Standard method is to remove outliers that are 3 * IQR. Warnings are thrown
+  The IAAO standard method is to remove outliers that are 3 * IQR. Warnings are thrown
   when sample size is extremely small or when the IQR is extremely narrow. See
   \href{https://www.iaao.org/media/standards/Standard_on_Ratio_Studies.pdf}{IAAO Standard on Ratio Studies}
   Appendix B. Outlier Trimming Guidelines for more information.
diff --git a/man/is_sales_chased.Rd b/man/is_sales_chased.Rd
new file mode 100644
index 0000000..6019728
--- /dev/null
+++ b/man/is_sales_chased.Rd
@@ -0,0 +1,74 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sales_chasing.R
+\name{is_sales_chased}
+\alias{is_sales_chased}
+\title{Detect sales chasing in a vector of sales ratios}
+\usage{
+is_sales_chased(
+  x,
+  method = "both",
+  bounds = c(0.98, 1.02),
+  gap = 0.03,
+  na.rm = FALSE
+)
+}
+\arguments{
+\item{x}{A numeric vector. Must be longer than 2 and cannot contain
+\code{inf} or \code{NA} values.}
+
+\item{method}{Default \code{both}. String indicating sales chasing detection
+method. Options are \code{cdf}, \code{dist}, or \code{both}.}
+
+\item{bounds}{Default \code{(0.98, 1.02)}. Lower and upper bounds of the
+range of ratios to consider when detecting sales chasing. Setting this to
+a narrow band at the center of the ratio distribution prevents detecting
+false positives at the tails.}
+
+\item{gap}{Default \code{0.05}. Float tuning factor. For the CDF method, it
+sets the maximum percentage difference between two adjacent ratios. For the
+distribution method, it sets the maximum percentage point difference
+between the percentage of the data between the \code{bounds} in the real
+distribution compared to the ideal distribution.}
+
+\item{na.rm}{Default FALSE. A boolean value indicating whether or not to
+remove NA values. If missing values are present but not removed the
+function will output NA for those values.}
+}
+\value{
+A logical value indicating whether or not the input values may
+  have been sales chased.
+}
+\description{
+Sales chasing is when a property is selectively reappraised to
+  shift its assessed value toward its recent sale price. Sales chasing is
+  difficult to detect. This function is NOT a statistical test and does
+  not provide the probability of the given result. Rather, it combines two
+  heuristic methods to roughly estimate if sales chasing has occurred.
+
+  The first method (cdf) detects discontinuities in the cumulative
+  distribution function (CDF) of the input vector. Ratios that are not sales
+  chased should have a fairly smooth CDF. Discontinuous jumps in the CDF,
+  particularly around 1, may indicate sales chasing. This can usually be seen
+  visually as a "flat spot" on the CDF.
+
+  The second method (dist) uses the technique outlined in the
+  \href{https://www.iaao.org/media/standards/Standard_on_Ratio_Studies.pdf}{IAAO Standard on Ratio Studies}
+  Appendix E, Section 4. It compares the percentage of real data within +-2%
+  of the mean ratio to the percentage of data within the same bounds given a
+  constructed normal distribution with the same mean and standard deviation.
+  The intuition here is that ratios that are sales chased may be more
+  "bunched up" in the center of the distribution.
+}
+\examples{
+
+# Generate fake data with normal vs chased ratios
+normal_ratios <- c(rnorm(1000, 1, 0.15))
+chased_ratios <- c(rnorm(900, 1, 0.15), rep(1, 100))
+
+# Plot to view discontinuity
+plot(stats::ecdf(normal_ratios))
+is_sales_chased(normal_ratios)
+
+plot(stats::ecdf(chased_ratios))
+is_sales_chased(chased_ratios)
+}
diff --git a/man/mki_ki.Rd b/man/mki_ki.Rd
index e83f3d1..cf1318e 100644
--- a/man/mki_ki.Rd
+++ b/man/mki_ki.Rd
@@ -6,18 +6,18 @@
 \alias{mki_met}
 \title{Calculate Kakwani and Modified Kakwani Index}
 \usage{
-mki(assessed, sale_price, na.rm = FALSE)
+mki(estimate, sale_price, na.rm = FALSE)
 
-ki(assessed, sale_price, na.rm = FALSE)
+ki(estimate, sale_price, na.rm = FALSE)
 
 mki_met(x)
 }
 \arguments{
-\item{assessed}{A numeric vector of assessed values. Must be the same
+\item{estimate}{A numeric vector of assessed values. Must be the same
 length as \code{sale_price}.}
 
 \item{sale_price}{A numeric vector of sale prices. Must be the same length
-as \code{assessed}.}
+as \code{estimate}.}
 
 \item{na.rm}{Default FALSE. A boolean value indicating whether or not to
 remove NA values. If missing values are present but not removed the
@@ -65,10 +65,10 @@ input vectors.
 }}
 \examples{
 # Calculate MKI
-mki(ratios_sample$assessed, ratios_sample$sale_price)
+mki(ratios_sample$estimate, ratios_sample$sale_price)
 
 # Calculate KI
-ki(ratios_sample$assessed, ratios_sample$sale_price)
+ki(ratios_sample$estimate, ratios_sample$sale_price)
 }
 \references{
 Quintos, C. (2020). A Gini measure for vertical equity in property
diff --git a/man/prb.Rd b/man/prb.Rd
index 1dbe5f7..6b8e785 100644
--- a/man/prb.Rd
+++ b/man/prb.Rd
@@ -6,18 +6,18 @@
 \alias{prb_met}
 \title{Calculate Coefficient of Price-Related Bias (PRB)}
 \usage{
-prb(assessed, sale_price, na.rm = FALSE)
+prb(estimate, sale_price, na.rm = FALSE)
 
-prb_ci(assessed, sale_price, alpha = 0.05, na.rm = FALSE)
+prb_ci(estimate, sale_price, alpha = 0.05, na.rm = FALSE)
 
 prb_met(x)
 }
 \arguments{
-\item{assessed}{A numeric vector of assessed values. Must be the same
+\item{estimate}{A numeric vector of assessed values. Must be the same
 length as \code{sale_price}.}
 
 \item{sale_price}{A numeric vector of sale prices. Must be the same length
-as \code{assessed}.}
+as \code{estimate}.}
 
 \item{na.rm}{Default FALSE. A boolean value indicating whether or not to
 remove NA values. If missing values are present but not removed the
@@ -31,7 +31,7 @@ against IAAO/Quintos standards.}
 }
 \description{
 PRB is an index of vertical equity that quantifies the
-  relationship between ratios and assessed values as a percentage. In
+  relationship betweem ratios and assessed values as a percentage. In
   concrete terms, a PRB of 0.02 indicates that, on average, ratios increase
   by 2\% whenever assessed values increase by 100 percent.
 
@@ -56,10 +56,10 @@ input vectors.
 }}
 \examples{
 # Calculate PRB
-prb(ratios_sample$assessed, ratios_sample$sale_price)
+prb(ratios_sample$estimate, ratios_sample$sale_price)
 
 # Calculate PRD confidence interval
-prb_ci(ratios_sample$assessed, ratios_sample$sale_price)
+prb_ci(ratios_sample$estimate, ratios_sample$sale_price)
 }
 \seealso{
 Other formulas: 
diff --git a/man/prd.Rd b/man/prd.Rd
index 4e2d2f4..23ae09f 100644
--- a/man/prd.Rd
+++ b/man/prd.Rd
@@ -6,24 +6,24 @@
 \alias{prd_met}
 \title{Calculate Price-Related Differential (PRD)}
 \usage{
-prd(assessed, sale_price, na.rm = FALSE)
+prd(estimate, sale_price, na.rm = FALSE)
 
-prd_ci(assessed, sale_price, nboot = 100, alpha = 0.05, na.rm = FALSE)
+prd_ci(estimate, sale_price, nboot = 1000, alpha = 0.05, na.rm = FALSE)
 
 prd_met(x)
 }
 \arguments{
-\item{assessed}{A numeric vector of assessed values. Must be the same
+\item{estimate}{A numeric vector of assessed values. Must be the same
 length as \code{sale_price}.}
 
 \item{sale_price}{A numeric vector of sale prices. Must be the same length
-as \code{assessed}.}
+as \code{estimate}.}
 
 \item{na.rm}{Default FALSE. A boolean value indicating whether or not to
 remove NA values. If missing values are present but not removed the
 function will output NA.}
 
-\item{nboot}{Default 100. Number of iterations to use to estimate
+\item{nboot}{Default 1000. Number of iterations to use to estimate
 the output statistic confidence interval.}
 
 \item{alpha}{Default 0.05. Numeric value indicating the confidence
@@ -60,10 +60,10 @@ input vectors.
 }}
 \examples{
 # Calculate PRD
-prd(ratios_sample$assessed, ratios_sample$sale_price)
+prd(ratios_sample$estimate, ratios_sample$sale_price)
 
 # Calculate PRD confidence interval
-prd_ci(ratios_sample$assessed, ratios_sample$sale_price)
+prd_ci(ratios_sample$estimate, ratios_sample$sale_price)
 }
 \seealso{
 Other formulas: 
diff --git a/man/quintos_sample.Rd b/man/quintos_sample.Rd
new file mode 100644
index 0000000..c089060
--- /dev/null
+++ b/man/quintos_sample.Rd
@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{quintos_sample}
+\alias{quintos_sample}
+\title{Sample of sales and estimated market values provided by Quintos in the
+   following MKI papers:}
+\format{
+A data frame with 30 observation and 2 variables:
+\describe{
+  \item{estimate}{Assessed fair market value}
+  \item{sale_price}{Recorded sale price of this property}
+}
+}
+\usage{
+quintos_sample
+}
+\description{
+Sample of sales and estimated market values provided by Quintos in the
+   following MKI papers:
+}
+\references{
+Quintos, C. (2020). A Gini measure for vertical equity in property
+assessments. <https://researchexchange.iaao.org/jptaa/vol17/iss2/2>
+
+Quintos, C. (2021). A Gini decomposition of the sources of inequality in
+property assessments. <https://researchexchange.iaao.org/jptaa/vol18/iss2/6>
+}
+\keyword{datasets}
diff --git a/man/ratios_sample.Rd b/man/ratios_sample.Rd
index 9cacd96..ea2e185 100644
--- a/man/ratios_sample.Rd
+++ b/man/ratios_sample.Rd
@@ -5,12 +5,11 @@
 \alias{ratios_sample}
 \title{Sample of ratio and sales data pulled from CCAO records}
 \format{
-A data frame with 979 observation and 4 variables:
+A data frame with 979 observation and 3 variables:
 \describe{
-  \item{assessed}{The fair market assessed value predicted by CCAO assessment
+  \item{estimate}{The fair market assessed value predicted by CCAO assessment
     models, including any successful appeals}
   \item{sale_price}{The recorded sale price of this property}
-  \item{ratio}{Sales ratio representing fair market value / sale price}
   \item{town}{Township name the property is in}
 }
 }
diff --git a/tests/testthat/data/mki_ki_data.csv b/tests/testthat/data/mki_ki_data.csv
deleted file mode 100644
index dc99e5e..0000000
--- a/tests/testthat/data/mki_ki_data.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-"","Sale_Price","Assessed"
-"1",32900,37299
-"2",36000,40166
-"3",54000,56317
-"4",64500,66184
-"5",68000,69487
-"6",70000,71515
-"7",74000,75338
-"8",80000,81036
-"9",84900,85673
-"10",89000,85021
-"11",94250,90046
-"12",99000,94089
-"13",105900,100227
-"14",109000,103157
-"15",115000,108290
-"16",124500,117099
-"17",129900,115347
-"18",135000,119678
-"19",149000,131631
-"20",155800,137321
-"21",163500,143974
-"22",175000,153572
-"23",179000,148457
-"24",185600,153488
-"25",199900,165040
-"26",215000,176940
-"27",235000,192959
-"28",250000,180046
-"29",279000,200240
-"30",295000,211445
diff --git a/tests/testthat/test-ci.R b/tests/testthat/test-ci.R
index eecceae..4244ee4 100644
--- a/tests/testthat/test-ci.R
+++ b/tests/testthat/test-ci.R
@@ -4,18 +4,17 @@ context("load testing data")
 data("ratios_sample")
 
 # Extract the components of the dataframe as vectors
-ratio <- ratios_sample$ratio
 sale_price <- ratios_sample$sale_price
-assessed <- ratios_sample$assessed
+estimate <- ratios_sample$estimate
 
 
 
 ##### TEST COD CI #####
 context("test cod_ci function")
 
-# Calculate PRB CI
-cod_ci_out_95 <- cod_ci(ratio, nboot = 1000)
-cod_ci_out_80 <- cod_ci(ratio, nboot = 1000, alpha = 0.2)
+# Calculate COD CI
+cod_ci_out_95 <- cod_ci(estimate, sale_price, nboot = 1000)
+cod_ci_out_80 <- cod_ci(estimate, sale_price, nboot = 1000, alpha = 0.2)
 
 test_that("returns expected type", {
   expect_type(cod_ci_out_95, "double")
@@ -30,21 +29,22 @@ test_that("output equal to expected", {
 
 test_that("bad input data stops execution", {
   expect_error(cod_ci(numeric(0)))
-  expect_error(cod_ci(numeric(10)))
-  expect_error(cod_ci(c(ratio, Inf)))
-  expect_error(cod_ci(data.frame(ratio)))
-  expect_error(cod_ci(c(ratio, NaN)))
-  expect_error(cod_ci(c(ratio, "2")))
-  expect_error(cod_ci(ratio, na.rm = "yes"))
+  expect_error(cod_ci(numeric(10), numeric(10)))
+  expect_error(cod_ci(c(estimate, Inf), c(sale_price, 0)))
+  expect_error(cod_ci(estimate, c(sale_price, 10e5)))
+  expect_error(cod_ci(data.frame(estimate), sale_price))
+  expect_error(cod_ci(c(estimate, NaN), c(sale_price, 1)))
+  expect_error(cod_ci(c(estimate, "2"), c(sale_price, 1)))
+  expect_error(cod_ci(estimate, sale_price, na.rm = "yes"))
 })
 
 test_that("incomplete data returns NAs unless removed", {
   expect_equal(
-    cod_ci(c(ratio, NA)),
+    cod_ci(c(estimate, NA), c(sale_price, 10e5)),
     NA_real_
   )
   expect_equivalent(
-    cod_ci(c(ratio, NA), nboot = 1000, na.rm = TRUE),
+    cod_ci(c(estimate, NA), c(sale_price, 10e5), na.rm = TRUE),
     c(16.49595, 18.84529),
     tolerance = 0.04
   )
@@ -54,9 +54,9 @@ test_that("incomplete data returns NAs unless removed", {
 ##### TEST PRD CI #####
 context("test prb_ci function")
 
-# Calculate PRB CI
-prd_ci_out_95 <- prd_ci(assessed, sale_price, nboot = 1000)
-prd_ci_out_80 <- prd_ci(assessed, sale_price, nboot = 1000, alpha = 0.2)
+# Calculate PRD CI
+prd_ci_out_95 <- prd_ci(estimate, sale_price, nboot = 1000)
+prd_ci_out_80 <- prd_ci(estimate, sale_price, nboot = 1000, alpha = 0.2)
 
 test_that("returns expected type", {
   expect_type(prd_ci_out_95, "double")
@@ -72,21 +72,21 @@ test_that("output equal to expected", {
 test_that("bad input data stops execution", {
   expect_error(prd_ci(numeric(0)))
   expect_error(prd_ci(numeric(10), numeric(10)))
-  expect_error(prd_ci(c(assessed, Inf), c(sale_price, 0)))
-  expect_error(prd_ci(assessed, c(sale_price, 10e5)))
-  expect_error(prd_ci(data.frame(assessed), sale_price))
-  expect_error(prd_ci(c(assessed, NaN), c(sale_price, 1)))
-  expect_error(prd_ci(c(assessed, "2"), c(sale_price, 1)))
-  expect_error(prd_ci(assessed, sale_price, na.rm = "yes"))
+  expect_error(prd_ci(c(estimate, Inf), c(sale_price, 0)))
+  expect_error(prd_ci(estimate, c(sale_price, 10e5)))
+  expect_error(prd_ci(data.frame(estimate), sale_price))
+  expect_error(prd_ci(c(estimate, NaN), c(sale_price, 1)))
+  expect_error(prd_ci(c(estimate, "2"), c(sale_price, 1)))
+  expect_error(prd_ci(estimate, sale_price, na.rm = "yes"))
 })
 
 test_that("incomplete data returns NAs unless removed", {
   expect_equal(
-    prd_ci(c(assessed, NA), c(sale_price, 10e5)),
+    prd_ci(c(estimate, NA), c(sale_price, 10e5)),
     NA_real_
   )
   expect_equivalent(
-    prd_ci(c(assessed, NA), c(sale_price, 10e5), na.rm = TRUE),
+    prd_ci(c(estimate, NA), c(sale_price, 10e5), na.rm = TRUE),
     c(1.034447, 1.062625),
     tolerance = 0.04
   )
@@ -98,8 +98,8 @@ test_that("incomplete data returns NAs unless removed", {
 context("test prb_ci function")
 
 # Calculate PRB CI
-prb_ci_out_95 <- prb_ci(assessed, sale_price)
-prb_ci_out_80 <- prb_ci(assessed, sale_price, alpha = 0.2)
+prb_ci_out_95 <- prb_ci(estimate, sale_price)
+prb_ci_out_80 <- prb_ci(estimate, sale_price, alpha = 0.2)
 
 test_that("returns expected type", {
   expect_type(prb_ci_out_95, "double")
@@ -115,21 +115,21 @@ test_that("output equal to expected", {
 test_that("bad input data stops execution", {
   expect_error(prb_ci(numeric(0)))
   expect_error(prb_ci(numeric(10), numeric(10)))
-  expect_error(prb_ci(c(assessed, Inf), c(sale_price, 0)))
-  expect_error(prb_ci(assessed, c(sale_price, 10e5)))
-  expect_error(prb_ci(data.frame(assessed), sale_price))
-  expect_error(prb_ci(c(assessed, NaN), c(sale_price, 1)))
-  expect_error(prb_ci(c(assessed, "2"), c(sale_price, 1)))
-  expect_error(prb_ci(assessed, sale_price, na.rm = "yes"))
+  expect_error(prb_ci(c(estimate, Inf), c(sale_price, 0)))
+  expect_error(prb_ci(estimate, c(sale_price, 10e5)))
+  expect_error(prb_ci(data.frame(estimate), sale_price))
+  expect_error(prb_ci(c(estimate, NaN), c(sale_price, 1)))
+  expect_error(prb_ci(c(estimate, "2"), c(sale_price, 1)))
+  expect_error(prb_ci(estimate, sale_price, na.rm = "yes"))
 })
 
 test_that("incomplete data returns NAs unless removed", {
   expect_equal(
-    prb_ci(c(assessed, NA), c(sale_price, 10e5)),
+    prb_ci(c(estimate, NA), c(sale_price, 10e5)),
     NA_real_
   )
   expect_equivalent(
-    prb_ci(c(assessed, NA), c(sale_price, 10e5), na.rm = TRUE),
+    prb_ci(c(estimate, NA), c(sale_price, 10e5), na.rm = TRUE),
     c(-0.01404379, 0.01899536),
     tolerance = 0.04
   )
diff --git a/tests/testthat/test-formulas.R b/tests/testthat/test-formulas.R
index 99b1b06..80400df 100644
--- a/tests/testthat/test-formulas.R
+++ b/tests/testthat/test-formulas.R
@@ -1,20 +1,16 @@
 context("load testing data")
 
-# Load the ratios sample dataset for testing
+# Load the sample datasets for testing
 data("ratios_sample")
+data("quintos_sample")
 
 # Extract the components of the dataframe as vectors
-ratio <- ratios_sample$ratio
 sale_price <- ratios_sample$sale_price
-assessed <- ratios_sample$assessed
+estimate <- ratios_sample$estimate
 
 # Load example data from Quintos article
-mki_ki_data <- read.csv(
-  rprojroot::find_testthat_root_file("data/mki_ki_data.csv")
-)
-
-mki_ki_assessed <- mki_ki_data$Assessed
-mki_ki_sale_price <- mki_ki_data$Sale_Price
+mki_ki_estimate <- quintos_sample$estimate
+mki_ki_sale_price <- quintos_sample$sale_price
 
 
 
@@ -22,7 +18,7 @@ mki_ki_sale_price <- mki_ki_data$Sale_Price
 context("test cod function")
 
 # Calculate COD
-cod_out <- cod(ratio)
+cod_out <- cod(estimate, sale_price)
 
 test_that("returns numeric vector", {
   expect_type(cod_out, "double")
@@ -35,17 +31,22 @@ test_that("output equal to expected", {
 
 test_that("bad input data stops execution", {
   expect_error(cod(numeric(0)))
-  expect_error(cod(numeric(10)))
-  expect_error(cod(c(cod_out, Inf)))
-  expect_error(cod(data.frame(ratio)))
-  expect_error(cod(c(ratio, NaN)))
-  expect_error(cod(c(ratio, "2")))
-  expect_error(cod(ratio, na.rm = "yes"))
+  expect_error(cod(numeric(10), numeric(10)))
+  expect_error(cod(c(cod_out, Inf), c(prb_out, 0)))
+  expect_error(cod(estimate, c(sale_price, 10e5)))
+  expect_error(cod(data.frame(estimate), sale_price))
+  expect_error(cod(c(estimate, NaN), c(sale_price, 1)))
+  expect_error(cod(c(estimate, "2"), c(sale_price, 1)))
+  expect_error(cod(estimate, sale_price, na.rm = "yes"))
 })
 
 test_that("incomplete data returns NAs unless removed", {
-  expect_equal(cod(c(ratio, NA)), NA_real_)
-  expect_equal(cod(c(ratio, NA), na.rm = TRUE), 17.81457, tolerance = 0.02)
+  expect_equal(cod(c(estimate, NA), c(sale_price, 10e5)), NA_real_)
+  expect_equal(
+    cod(c(estimate, NA), c(sale_price, 10e5), na.rm = TRUE),
+    17.81457,
+    tolerance = 0.02
+  )
 })
 
 test_that("standard met function", {
@@ -58,7 +59,7 @@ test_that("standard met function", {
 context("test prd function")
 
 # Calculate PRD
-prd_out <- prd(assessed, sale_price)
+prd_out <- prd(estimate, sale_price)
 
 test_that("returns numeric vector", {
   expect_type(prd_out, "double")
@@ -73,20 +74,20 @@ test_that("bad input data stops execution", {
   expect_error(prd(numeric(0)))
   expect_error(prd(numeric(10), numeric(10)))
   expect_error(prd(c(prd_out, Inf), c(prb_out, 0)))
-  expect_error(prd(assessed, c(sale_price, 10e5)))
-  expect_error(prd(data.frame(assessed), sale_price))
-  expect_error(prd(c(assessed, NaN), c(sale_price, 1)))
-  expect_error(prd(c(assessed, "2"), c(sale_price, 1)))
-  expect_error(prd(assessed, sale_price, na.rm = "yes"))
+  expect_error(prd(estimate, c(sale_price, 10e5)))
+  expect_error(prd(data.frame(estimate), sale_price))
+  expect_error(prd(c(estimate, NaN), c(sale_price, 1)))
+  expect_error(prd(c(estimate, "2"), c(sale_price, 1)))
+  expect_error(prd(estimate, sale_price, na.rm = "yes"))
 })
 
 test_that("incomplete data returns NAs unless removed", {
   expect_equal(
-    prd(c(assessed, NA), c(sale_price, 10e5)),
+    prd(c(estimate, NA), c(sale_price, 10e5)),
     NA_real_
   )
   expect_equal(
-    prd(c(assessed, NA), c(sale_price, 10e5), na.rm = TRUE),
+    prd(c(estimate, NA), c(sale_price, 10e5), na.rm = TRUE),
     1.048419,
     tolerance = 0.02
   )
@@ -102,7 +103,7 @@ test_that("standard met function", {
 context("test prb function")
 
 # Calculate PRB
-prb_out <- prb(assessed, sale_price)
+prb_out <- prb(estimate, sale_price)
 
 test_that("returns expected type", {
   expect_type(prb_out, "double")
@@ -117,20 +118,20 @@ test_that("bad input data stops execution", {
   expect_error(prb(numeric(0)))
   expect_error(prb(numeric(10), numeric(10)))
   expect_error(prb(c(prb_out, Inf), c(prb_out, 0)))
-  expect_error(prb(assessed, c(sale_price, 10e5)))
-  expect_error(prb(data.frame(assessed), sale_price))
-  expect_error(prb(c(assessed, NaN), c(sale_price, 1)))
-  expect_error(prb(c(assessed, "2"), c(sale_price, 1)))
-  expect_error(prb(assessed, sale_price, na.rm = "yes"))
+  expect_error(prb(estimate, c(sale_price, 10e5)))
+  expect_error(prb(data.frame(estimate), sale_price))
+  expect_error(prb(c(estimate, NaN), c(sale_price, 1)))
+  expect_error(prb(c(estimate, "2"), c(sale_price, 1)))
+  expect_error(prb(estimate, sale_price, na.rm = "yes"))
 })
 
 test_that("incomplete data returns NAs unless removed", {
   expect_equal(
-    prb(c(assessed, NA), c(sale_price, 10e5)),
+    prb(c(estimate, NA), c(sale_price, 10e5)),
     NA_real_
   )
   expect_equal(
-    prb(c(assessed, NA), c(sale_price, 10e5), na.rm = TRUE),
+    prb(c(estimate, NA), c(sale_price, 10e5), na.rm = TRUE),
     0.0024757,
     tolerance = 0.02
   )
@@ -146,7 +147,7 @@ test_that("standard met function", {
 context("test mki function")
 
 # Calculate MKI
-mki_out <- mki(mki_ki_assessed, mki_ki_sale_price)
+mki_out <- mki(mki_ki_estimate, mki_ki_sale_price)
 
 test_that("returns expected type", {
   expect_type(mki_out, "double")
@@ -160,21 +161,21 @@ test_that("output equal to expected", {
 test_that("bad input data stops execution", {
   expect_error(mki(numeric(0)))
   expect_error(mki(numeric(10), numeric(10)))
-  expect_error(mki(c(mki_ki_assessed, Inf), c(mki_ki_sale_price, 0)))
-  expect_error(mki(mki_ki_assessed, c(mki_ki_sale_price, 10e5)))
-  expect_error(mki(data.frame(mki_ki_assessed), mki_ki_sale_price))
-  expect_error(mki(c(mki_ki_assessed, NaN), c(mki_ki_sale_price, 1)))
-  expect_error(mki(c(mki_ki_assessed, "2"), c(mki_ki_sale_price, 1)))
-  expect_error(mki(mki_ki_assessed, mki_ki_sale_price, na.rm = "yes"))
+  expect_error(mki(c(mki_ki_estimate, Inf), c(mki_ki_sale_price, 0)))
+  expect_error(mki(mki_ki_estimate, c(mki_ki_sale_price, 10e5)))
+  expect_error(mki(data.frame(mki_ki_estimate), mki_ki_sale_price))
+  expect_error(mki(c(mki_ki_estimate, NaN), c(mki_ki_sale_price, 1)))
+  expect_error(mki(c(mki_ki_estimate, "2"), c(mki_ki_sale_price, 1)))
+  expect_error(mki(mki_ki_estimate, mki_ki_sale_price, na.rm = "yes"))
 })
 
 test_that("incomplete data returns NAs unless removed", {
   expect_equal(
-    mki(c(mki_ki_assessed, NA), c(mki_ki_sale_price, 10e5)),
+    mki(c(mki_ki_estimate, NA), c(mki_ki_sale_price, 10e5)),
     NA_real_
   )
   expect_equal(
-    mki(c(mki_ki_assessed, NA), c(mki_ki_sale_price, 10e5), na.rm = TRUE),
+    mki(c(mki_ki_estimate, NA), c(mki_ki_sale_price, 10e5), na.rm = TRUE),
     0.79,
     tolerance = 0.01
   )
@@ -190,7 +191,7 @@ test_that("standard met function", {
 context("test ki function")
 
 # Calculate KI
-ki_out <- ki(mki_ki_assessed, mki_ki_sale_price)
+ki_out <- ki(mki_ki_estimate, mki_ki_sale_price)
 
 test_that("returns expected type", {
   expect_type(ki_out, "double")
@@ -204,21 +205,21 @@ test_that("output equal to expected", {
 test_that("bad input data stops execution", {
   expect_error(ki(numeric(0)))
   expect_error(ki(numeric(10), numeric(10)))
-  expect_error(ki(c(mki_ki_assessed, Inf), c(mki_ki_sale_price, 0)))
-  expect_error(ki(mki_ki_assessed, c(mki_ki_sale_price, 10e5)))
-  expect_error(ki(data.frame(mki_ki_assessed), mki_ki_sale_price))
-  expect_error(ki(c(mki_ki_assessed, NaN), c(mki_ki_sale_price, 1)))
-  expect_error(ki(c(mki_ki_assessed, "2"), c(mki_ki_sale_price, 1)))
-  expect_error(ki(mki_ki_assessed, mki_ki_sale_price, na.rm = "yes"))
+  expect_error(ki(c(mki_ki_estimate, Inf), c(mki_ki_sale_price, 0)))
+  expect_error(ki(mki_ki_estimate, c(mki_ki_sale_price, 10e5)))
+  expect_error(ki(data.frame(mki_ki_estimate), mki_ki_sale_price))
+  expect_error(ki(c(mki_ki_estimate, NaN), c(mki_ki_sale_price, 1)))
+  expect_error(ki(c(mki_ki_estimate, "2"), c(mki_ki_sale_price, 1)))
+  expect_error(ki(mki_ki_estimate, mki_ki_sale_price, na.rm = "yes"))
 })
 
 test_that("incomplete data returns NAs unless removed", {
   expect_equal(
-    ki(c(mki_ki_assessed, NA), c(mki_ki_sale_price, 10e5)),
+    ki(c(mki_ki_estimate, NA), c(mki_ki_sale_price, 10e5)),
     NA_real_
   )
   expect_equal(
-    ki(c(mki_ki_assessed, NA), c(mki_ki_sale_price, 10e5), na.rm = TRUE),
+    ki(c(mki_ki_estimate, NA), c(mki_ki_sale_price, 10e5), na.rm = TRUE),
     -0.0595,
     tolerance = 0.003
   )
diff --git a/tests/testthat/test-sale_chasing.R b/tests/testthat/test-sale_chasing.R
index 7ac15ff..ae50b32 100644
--- a/tests/testthat/test-sale_chasing.R
+++ b/tests/testthat/test-sale_chasing.R
@@ -4,19 +4,19 @@ context("load testing data")
 data("ratios_sample")
 
 # Extract the components of the dataframe as vectors
-sample_ratios <- ratios_sample$ratio
+sample_ratios <- ratios_sample$estimate / ratios_sample$sale_price
 normal_ratios <- c(rnorm(1000, 1, 0.15))
 chased_ratios <- c(rnorm(900, 1, 0.15), rep(1, 100))
 
 
 
 ##### TEST CHASING DETECTION #####
-context("test detect_chashing function")
+context("test is_sales_chased function")
 
 # Run detection
-sample_out <- detect_chasing(sample_ratios)
-normal_out <- detect_chasing(normal_ratios)
-chased_out <- detect_chasing(chased_ratios)
+sample_out <- is_sales_chased(sample_ratios)
+normal_out <- is_sales_chased(normal_ratios)
+chased_out <- is_sales_chased(chased_ratios)
 
 test_that("returns logical value", {
   expect_type(sample_out, "logical")
@@ -31,20 +31,20 @@ test_that("output equal to expected", {
 })
 
 test_that("bad input data stops execution", {
-  expect_error(detect_chasing(numeric(0)))
-  expect_error(detect_chasing(c(sample_ratios, Inf)))
-  expect_error(detect_chasing(data.frame(sample_ratios)))
-  expect_error(detect_chasing(c(sample_ratios, NaN)))
-  expect_error(detect_chasing(c(sample_ratios, "2")))
-  expect_error(detect_chasing(sample_ratios, na.rm = "yes"))
+  expect_error(is_sales_chased(numeric(0)))
+  expect_error(is_sales_chased(c(sample_ratios, Inf)))
+  expect_error(is_sales_chased(data.frame(sample_ratios)))
+  expect_error(is_sales_chased(c(sample_ratios, NaN)))
+  expect_error(is_sales_chased(c(sample_ratios, "2")))
+  expect_error(is_sales_chased(sample_ratios, na.rm = "yes"))
 })
 
 test_that("incomplete data returns NAs unless removed", {
-  expect_equal(detect_chasing(c(sample_ratios, NA)), NA)
-  expect_false(detect_chasing(c(sample_ratios, NA), na.rm = TRUE))
-  expect_true(detect_chasing(c(chased_ratios, NA), na.rm = TRUE))
+  expect_equal(is_sales_chased(c(sample_ratios, NA)), NA)
+  expect_false(is_sales_chased(c(sample_ratios, NA), na.rm = TRUE))
+  expect_true(is_sales_chased(c(chased_ratios, NA), na.rm = TRUE))
 })
 
 test_that("warnings thrown when expected", {
-  expect_warning(detect_chasing(rnorm(29)))
+  expect_warning(is_sales_chased(rnorm(29)))
 })
diff --git a/vignettes/example-ratio-study.Rmd b/vignettes/example-ratio-study.Rmd
index 7e2a670..4b8aeff 100644
--- a/vignettes/example-ratio-study.Rmd
+++ b/vignettes/example-ratio-study.Rmd
@@ -163,10 +163,10 @@ combined <- combined %>%
   pivot_longer(
     mailed_tot:board_tot,
     names_to = "stage",
-    values_to = "assessed"
+    values_to = "estimate"
   ) %>%
-  mutate_at(vars(sale_price, assessed), as.numeric) %>%
-  mutate(ratio = (assessed * 10) / sale_price)
+  mutate_at(vars(sale_price, estimate), as.numeric) %>%
+  mutate(ratio = (estimate * 10) / sale_price)
 ```
 
 ### Sales ratio statistics by township
@@ -177,19 +177,19 @@ Cook County has jurisdictions called townships that are important units for asse
 # For each town and stage, calculate COD, PRD, and PRB, and their respective
 # confidence intervals then arrange by town name and stage of assessment
 combined %>%
-  filter(assessed > 0) %>%
+  filter(estimate > 0) %>%
   group_by(township_name, stage) %>%
   summarise(
     n = n(),
-    cod = cod(ratio, na.rm = TRUE),
+    cod = cod(estimate, sale_price, na.rm = TRUE),
     cod_ci = paste(
-      round(cod_ci(ratio, nboot = 1000, na.rm = TRUE), 3),
+      round(cod_ci(estimate, sale_price, nboot = 1000, na.rm = TRUE), 3),
       collapse = ", "
     ),
     cod_met = cod_met(cod),
-    prb = prb(assessed, sale_price, na.rm = TRUE),
+    prb = prb(estimate, sale_price, na.rm = TRUE),
     prb_ci = paste(
-      round(prb_ci(assessed, sale_price, na.rm = TRUE), 3),
+      round(prb_ci(estimate, sale_price, na.rm = TRUE), 3),
       collapse = ", "
     ),
     prb_met = prb_met(prb)
@@ -276,20 +276,20 @@ Using the ordered data, you can plot the classic [Lorenz curve](https://en.wikip
 
 ```{r}
 gini_data <- combined %>%
-  select(sale_price, assessed) %>%
+  select(sale_price, estimate) %>%
   arrange(sale_price)
 
 sale_price <- gini_data$sale_price
-assessed <- gini_data$assessed
+estimate <- gini_data$estimate
 
 lorenz_data_price <- data.frame(
   pct = c(0, cumsum(sale_price) / sum(sale_price)),
   cum_pct = c(0, seq_along(sale_price)) / length(sale_price)
 )
 
-lorenz_data_assessed <- data.frame(
-  pct = c(0, cumsum(assessed) / sum(assessed)),
-  cum_pct = c(0, seq_along(assessed)) / length(assessed)
+lorenz_data_estimate <- data.frame(
+  pct = c(0, cumsum(estimate) / sum(estimate)),
+  cum_pct = c(0, seq_along(estimate)) / length(estimate)
 )
 
 ggplot() +
@@ -298,7 +298,7 @@ ggplot() +
     aes(x = cum_pct, y = pct), color = "blue"
   ) +
   geom_line(
-    data = lorenz_data_assessed,
+    data = lorenz_data_estimate,
     aes(x = cum_pct, y = pct), color = "red"
   ) +
   geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "green") +
@@ -337,27 +337,27 @@ To translate these curves to a single metric, the Kakwani Index (KI) and Modifie
 - **Modified Kakwani Index:** `Assessed Gini / Sale Price Gini`
 
 ```{r}
-# Calculate the sum of the n elements of the assessed vector
-n <- length(assessed)
-g_assessed <- sum(assessed * seq_len(n))
+# Calculate the sum of the n elements of the estimate vector
+n <- length(estimate)
+g_estimate <- sum(estimate * seq_len(n))
 
 # Compute the Gini coefficient based on the previously calculated sum
 # and the increasing sum of all elements in the assessed vector
-g_assessed <- 2 * g_assessed / sum(assessed) - (n + 1L)
+g_estimate <- 2 * g_estimate / sum(estimate) - (n + 1L)
 
 # Normalize the Gini coefficient by dividing it by n.
-gini_assessed <- g_assessed / n
+gini_estimate <- g_estimate / n
 
 # Follow the same process for the sale_price vector
 g_sale <- sum(sale_price * seq_len(n))
 g_sale <- 2 * g_sale / sum(sale_price) - (n + 1L)
 gini_sale <- g_sale / n
 
-MKI <- round(gini_assessed / gini_sale, 4)
-KI <- round(gini_assessed - gini_sale, 4)
+MKI <- round(gini_estimate / gini_sale, 4)
+KI <- round(gini_estimate - gini_sale, 4)
 ```
 
-The output for the Modified Kakwani Index is `r MKI`, and the Kakwani Index is `r KI`. According to the following table, this means that the assessments are slightly regressive. 
+The output for the Modified Kakwani Index is `r MKI`, and the Kakwani Index is `r KI`. According to the following table, this means that the assessments are slightly regressive.
 
 | KI Range           | MKI Range          | Interpretation     |
 |:-------------------|:-------------------|:-------------------|
@@ -388,8 +388,8 @@ ggplot() +
 ```{r}
 # Detect chasing for each vector
 tibble(
-  "Blue Chased?" = detect_chasing(normal_ratios),
-  "Red Chased?" = detect_chasing(chased_ratios)
+  "Blue Chased?" = is_sales_chased(normal_ratios),
+  "Red Chased?" = is_sales_chased(chased_ratios)
 ) %>%
   kable(format = "markdown", digits = 3)
 ```