Version 0.6.2

CITATION.cff

@@ -8,7 +8,7 @@ message: 'To cite package "sps" in publications use:'
 type: software
 license: MIT
 title: 'sps: Sequential Poisson Sampling'
-version: 0.6.1
+version: 0.6.2
 doi: 10.5281/zenodo.10109857
 identifiers:
 - type: doi
@@ -36,7 +36,7 @@ preferred-citation:
   year: '2025'
   doi: 10.5281/zenodo.10109857
   url: https://cran.r-project.org/package=sps
-  notes: R package version 0.6.1
+  notes: R package version 0.6.2
 repository: https://CRAN.R-project.org/package=sps
 repository-code: https://github.com/marberts/sps
 url: https://marberts.github.io/sps/
@@ -103,49 +103,22 @@ references:
   year: '2025'
   doi: 10.32614/CRAN.package.knitr
 - type: software
-  title: rmarkdown
-  abstract: 'rmarkdown: Dynamic Documents for R'
+  title: quarto
+  abstract: 'quarto: R Interface to ''Quarto'' Markdown Publishing System'
   notes: Suggests
-  url: https://pkgs.rstudio.com/rmarkdown/
-  repository: https://CRAN.R-project.org/package=rmarkdown
+  url: https://quarto-dev.github.io/quarto-r/
+  repository: https://CRAN.R-project.org/package=quarto
   authors:
   - family-names: Allaire
     given-names: JJ
     email: jj@posit.co
-  - family-names: Xie
-    given-names: Yihui
-    email: xie@yihui.name
-    orcid: https://orcid.org/0000-0003-0645-5666
+    orcid: https://orcid.org/0000-0003-0174-9868
   - family-names: Dervieux
     given-names: Christophe
     email: cderv@posit.co
     orcid: https://orcid.org/0000-0003-4474-2498
-  - family-names: McPherson
-    given-names: Jonathan
-    email: jonathan@posit.co
-  - family-names: Luraschi
-    given-names: Javier
-  - family-names: Ushey
-    given-names: Kevin
-    email: kevin@posit.co
-  - family-names: Atkins
-    given-names: Aron
-    email: aron@posit.co
-  - family-names: Wickham
-    given-names: Hadley
-    email: hadley@posit.co
-  - family-names: Cheng
-    given-names: Joe
-    email: joe@posit.co
-  - family-names: Chang
-    given-names: Winston
-    email: winston@posit.co
-  - family-names: Iannone
-    given-names: Richard
-    email: rich@posit.co
-    orcid: https://orcid.org/0000-0003-3925-190X
   year: '2025'
-  doi: 10.32614/CRAN.package.rmarkdown
+  doi: 10.32614/CRAN.package.quarto
 - type: software
   title: testthat
   abstract: 'testthat: Unit Testing for R'

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: sps
 Title: Sequential Poisson Sampling
-Version: 0.6.1.9007
+Version: 0.6.2
 Authors@R: c(
     person("Steve", "Martin",
             role = c("aut", "cre", "cph"),
@@ -14,7 +14,7 @@ Description: Sequential Poisson sampling is a variation of Poisson sampling for
     functions to draw stratified sequential Poisson samples according to the
     method by Ohlsson (1998, ISSN:0282-423X), as well as other order sample
     designs by Rosén (1997, <doi:10.1016/S0378-3758(96)00186-3>), and generate
-    appropriate bootstrap replicate weights according to the generalized
+    approximate bootstrap replicate weights according to the generalized
     bootstrap method by Beaumont and Patak
     (2012, <doi:10.1111/j.1751-5823.2011.00166.x>).
 Depends: R (>= 4.2)

NEWS.md

@@ -1,11 +1,11 @@
-## sps (development version)
+## sps 0.6.2
 
 - Added `divisor_method()` to more easily generate divisor functions for
 `prop_allocation()` by name.
 
 - `prop_allocation()` is now faster.
 
-- Added `sps_iterator()` to make an iterator that draws an SPS sample one unit at a time.
+- Added `sps_iterator()` to make an iterator that draws a SPS one unit at a time.
 
 ## sps 0.6.1
 

R/sps_iterator.R

@@ -4,11 +4,13 @@
 #' sequential Poisson method without replacing previously sampled units.
 #'
 #' @inheritParams sps
-#' @param ... Additional arugments to [`becomes_ta()`].
+#' @param ... Additional arguments to [`becomes_ta()`].
 #' @param n A positive integer giving the initial sample size for the iterator.
 #'
 #' @returns
-#' A function that returns the next unit in the sample.
+#' A function that returns the next unit in the sample. It take a single
+#' argument giving the sentinel value to indicate that there are no units
+#' left to sample (default `NULL`).
 #'
 #' @examples
 #' prn <- runif(5)
@@ -43,9 +45,9 @@ sps_iterator <- function(x, ..., n = 0L, prn = NULL) {
   } else {
     sampled <- integer(0L)
   }
-  function() {
+  function(done = NULL) {
     if (length(s) == 0L) {
-      return(NULL)
+      return(done)
     }
     tas <- bta[[n + 1L]][!bta[[n + 1L]] %in% sampled]
     if (length(tas) > 0L) {

README.Rmd

@@ -24,7 +24,7 @@ knitr::opts_chunk$set(
 [![Mentioned in Awesome Official Statistics ](https://awesome.re/mentioned-badge.svg)](https://github.com/SNStatComp/awesome-official-statistics-software)
 <!-- badges: end -->
 
-Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998), as well as other order sample designs by Rosén (1997), and generate appropriate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012).
+Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998), as well as other order sample designs by Rosén (1997), and generate approximate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012).
 
 ## Installation
 

README.md

@@ -23,7 +23,7 @@ drawing probability-proportional-to-size samples with a given number of
 units, and is commonly used for price-index surveys. This package gives
 functions to draw stratified sequential Poisson samples according to the
 method by Ohlsson (1998), as well as other order sample designs by Rosén
-(1997), and generate appropriate bootstrap replicate weights according
+(1997), and generate approximate bootstrap replicate weights according
 to the generalized bootstrap method by Beaumont and Patak (2012).
 
 ## Installation
@@ -64,11 +64,11 @@ revenue <- c(1:10, 100, 150)
 
 # Draw a sample of 6 businesses
 (samp <- sps(revenue, 6))
-#> [1]  4  5  9 10 11 12
+#> [1]  6  7  9 10 11 12
 
 # Design weights and sampling strata are stored with the sample
 weights(samp)
-#> [1] 3.437500 2.750000 1.527778 1.375000 1.000000 1.000000
+#> [1] 2.291667 1.964286 1.527778 1.375000 1.000000 1.000000
 levels(samp)
 #> [1] "TS" "TS" "TS" "TS" "TA" "TA"
 ```
@@ -88,10 +88,10 @@ stratum <- rep(c("a", "b"), c(9, 3))
 
 # Draw a stratified sample
 (samp <- sps(revenue, allocation, stratum))
-#> [1]  1  5  7 10 11 12
+#> [1]  4  6  8 10 11 12
 
 weights(samp)
-#> [1] 15.000000  3.000000  2.142857  1.000000  1.000000  1.000000
+#> [1] 3.750 2.500 1.875 1.000 1.000 1.000
 levels(samp)
 #> [1] "TS" "TS" "TS" "TA" "TA" "TA"
 ```
@@ -101,15 +101,15 @@ replicate weights with the `sps_repweights()` function.
 
 ``` r
 sps_repweights(weights(samp), 5)
-#>            [,1]      [,2]    [,3]   [,4]      [,5]
-#> [1,] 29.9985000 0.0015000 15.0000 0.0015 15.000000
-#> [2,]  3.0000000 3.0000000  0.0003 3.0000  5.999700
-#> [3,]  0.1430571 0.1430571  2.2857 2.2857  4.428343
-#> [4,]  1.0000000 1.0000000  1.0000 1.0000  1.000000
-#> [5,]  1.0000000 1.0000000  1.0000 1.0000  1.000000
-#> [6,]  1.0000000 1.0000000  1.0000 1.0000  1.000000
+#>           [,1]     [,2]      [,3]      [,4]      [,5]
+#> [1,] 0.0003750 3.515648 3.5156484 7.9683281 3.5156484
+#> [2,] 0.6251875 6.718328 5.3122187 0.6251875 2.0312969
+#> [3,] 1.7578242 1.757824 0.0001875 0.0001875 0.0001875
+#> [4,] 1.0000000 1.000000 1.0000000 1.0000000 1.0000000
+#> [5,] 1.0000000 1.000000 1.0000000 1.0000000 1.0000000
+#> [6,] 1.0000000 1.000000 1.0000000 1.0000000 1.0000000
 #> attr(,"tau")
-#> [1] 1.0001
+#> [1] 1.066773
 ```
 
 The vignette gives more detail about how to use these functions to draw

vignettes/sps.qmd

@@ -172,7 +172,7 @@ The solution to this problem is to simply increase the size of the sample until
 sample %in% sps(x, 6, prn = u)
 ```
 
-It's possible to go one step further and make an interator that draws the next unit in the sample without replacing the old ones, and gives the same result as directly drawing that many units.
+It's possible to go one step further and make an iterator that draws the next unit in the sample without replacing the old ones, and gives the same result as directly drawing that many units.
 
 ```{r}
 s <- sps_iterator(x, prn = u)

vignettes/take-all.qmd

@@ -63,7 +63,7 @@ which(pi(x[1:10], 5) >= 1 - alpha)
 any(pi(x[1:8], 3) >= 1 - alpha)
 ```
 
-Although all inclusion probabilities are less than $1 - \alpha$ after two round
+Although all inclusion probabilities are less than $1 - \alpha$ after two rounds
 of placing units into a take-all stratum, the inclusion
 probability for unit 9 would also be less than $1 - \alpha$ despite it being in the take-all stratum.
 
@@ -147,3 +147,24 @@ abline(1 - alpha, 0, lty = 2)
 legend(1, 2, c("take-some", "take-all"), pch = c(20, 19))
 ```
 
+As each element $p_{i}$ in the sequence is a strictly increasing function of $i$, we can also identify the unique value for $n$ when a unit enters the take-all stratum.
+
+```{r, fig.width=8, fig.height=5.33}
+#| fig.alt: >
+#|   Diagram showing when units first enter the take-all stratum.
+plot(
+  2:5,
+  p(x, 8)[1:4],
+  xlab = "",
+  ylab = "p",
+  xlim = c(1, 9),
+  ylim = c(0, 2.5),
+  pch = 20
+)
+points(6:9, p(x, 8)[5:8], pch = 19)
+points(1:3, p(x, 9)[1:3], pch = 20, col = "red")
+points(4:9, p(x, 9)[4:9], pch = 19, col = "red")
+abline(1 - alpha, 0, lty = 2)
+legend(1, 2.5, c("take-some", "take-all"), pch = c(20, 19))
+legend(1, 2, c("n = 8", "n = 9"), pch = 20, col = c("black", "red"))
+```