Ready for CRAN

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: gpindex
 Title: Generalized Price and Quantity Indexes
-Version: 0.5.0.9009
+Version: 0.6.0
 Authors@R: c(
     person("Steve", "Martin", role = c("aut", "cre", "cph"),
     email = "[email protected]",

NEWS.md

@@ -11,7 +11,7 @@ control whether products in the first period match to themselves or return `NA`.
 
 - Updated documentation.
 
-- Added a vignette.
+- Added a brief vignette.
 
 ## Version 0.5.0
 

R/offset_prices.R

@@ -49,7 +49,7 @@ offset_period <- function(f) {
 #' gives the location of the corresponding product in the previous period.
 #' With `base_period()`, the resulting vector gives the location of the
 #' corresponding product in the first period. The locations are unchanged for 
-#' he first time period if `match_first = TRUE`, `NA` otherwise.
+#' the first time period if `match_first = TRUE`, `NA` otherwise.
 #' 
 #' @note
 #' By definition, there must be at most one transaction for each product

R/outliers.R

@@ -1,4 +1,4 @@
-#' Outlier methods for price relatives
+#' Outlier detection for price relatives
 #'
 #' Standard cutoff-based methods for detecting outliers with price relatives.
 #'
@@ -89,13 +89,15 @@
 #' @export
 quartile_method <- function(x, cu = 2.5, cl = cu, a = 0, type = 7) {
   x <- as.numeric(x)
+  cu <- as.numeric(cu)
+  cl <- as.numeric(cl)
   q <- stats::quantile(
     x, c(0.25, 0.5, 0.75),
     names = FALSE, na.rm = TRUE, type = type
   )
   x <- x - q[2L]
-  u <- cu * pmax(q[3L] - q[2L], abs(a * q[2L]))
-  l <- -cl * pmax(q[2L] - q[1L], abs(a * q[2L]))
+  u <- cu * pmax.int(q[3L] - q[2L], abs(a * q[2L]))
+  l <- -cl * pmax.int(q[2L] - q[1L], abs(a * q[2L]))
   x > u | x < l
 }
 
@@ -104,11 +106,13 @@ quartile_method <- function(x, cu = 2.5, cl = cu, a = 0, type = 7) {
 #' @export
 resistant_fences <- function(x, cu = 2.5, cl = cu, a = 0, type = 7) {
   x <- as.numeric(x)
+  cu <- as.numeric(cu)
+  cl <- as.numeric(cl)
   q <- stats::quantile(
     x, c(0.25, 0.5, 0.75),
     names = FALSE, na.rm = TRUE, type = type
   )
-  iqr <- pmax(q[3L] - q[1L], abs(a * q[2L]))
+  iqr <- pmax.int(q[3L] - q[1L], abs(a * q[2L]))
   u <- q[3L] + cu * iqr
   l <- q[1L] - cl * iqr
   x > u | x < l
@@ -119,6 +123,8 @@ resistant_fences <- function(x, cu = 2.5, cl = cu, a = 0, type = 7) {
 #' @export
 robust_z <- function(x, cu = 2.5, cl = cu) {
   x <- as.numeric(x)
+  cu <- as.numeric(cu)
+  cl <- as.numeric(cl)
   med <- stats::median(x, na.rm = TRUE)
   s <- stats::mad(x, na.rm = TRUE)
   x <- x - med
@@ -132,6 +138,8 @@ robust_z <- function(x, cu = 2.5, cl = cu) {
 #' @export
 fixed_cutoff <- function(x, cu = 2.5, cl = 1 / cu) {
   x <- as.numeric(x)
+  cu <- as.numeric(cu)
+  cl <- as.numeric(cl)
   x > cu | x < cl
 }
 
@@ -140,6 +148,8 @@ fixed_cutoff <- function(x, cu = 2.5, cl = 1 / cu) {
 #' @export
 tukey_algorithm <- function(x, cu = 2.5, cl = cu, type = 7) {
   x <- as.numeric(x)
+  cu <- as.numeric(cu)
+  cl <- as.numeric(cl)
   q <- stats::quantile(
     x, c(0.05, 0.95),
     names = FALSE, na.rm = TRUE, type = type

R/weights.R

@@ -13,10 +13,8 @@
 #' `nested_transmute(r1, r2, t, s)` and `nested_transmute2(r1, r2, t, s)` do
 #' the same for nested generalized means, so that
 #'
-#' \preformatted{
-#' nested_mean(r1, r2, t)(x, w1, w2) ==
-#'   generalized_mean(s)(x, v(x, w1, w2))
-#' }
+#' \preformatted{nested_mean(r1, r2, t)(x, w1, w2) ==
+#'   generalized_mean(s)(x, v(x, w1, w2))}
 #'
 #' This generalizes the result for turning a geometric mean into an arithmetic
 #' mean (and vice versa) in section 4.2 of Balk (2008), and a Fisher mean into

tests/Examples/gpindex-Ex.Rout.save

@@ -1031,7 +1031,7 @@ Warning in back_period(period) :
 > flush(stderr()); flush(stdout())
 > 
 > ### Name: outliers
-> ### Title: Outlier methods for price relatives
+> ### Title: Outlier detection for price relatives
 > ### Aliases: outliers quartile_method resistant_fences robust_z
 > ###   fixed_cutoff tukey_algorithm hb_transform
 >