Reduce dev dependencies

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: sps
 Title: Sequential Poisson Sampling
-Version: 0.6.3.9001
+Version: 0.6.3.9003
 Authors@R: c(
     person("Steve", "Martin",
             role = c("aut", "cre", "cph"),
@@ -21,13 +21,13 @@ Depends: R (>= 4.2)
 Imports: stats
 Suggests: 
     kit (>= 0.0.10),
-    knitr,
-    quarto,
+    litedown,
+    rbibutils,
     tinytest
 License: MIT + file LICENSE
 Encoding: UTF-8
 URL: https://marberts.github.io/sps/, https://github.com/marberts/sps
 BugReports: https://github.com/marberts/sps/issues
-VignetteBuilder: quarto
+VignetteBuilder: litedown
 Roxygen: list(markdown = TRUE)
 Config/roxygen2/version: 8.0.0

NEWS.md

@@ -2,7 +2,7 @@
 
 - Small improvements to the documentation.
 
-- Now using `{tinytest}`.
+- Now using `{tinytest}` and `{litedown}`.
 
 ## sps 0.6.3
 

inst/CITATION

@@ -1,3 +1,5 @@
+citHeader("To cite sps in publications use:")
+
 bibentry(
   bibtype  = "Manual",
   title    = "{sps}: Sequential Poisson Sampling",

vignettes/ref.bib

@@ -0,0 +1,27 @@
+@article{ohlsson1998,
+  title = {Sequential {P}oisson sampling},
+  author = {Ohlsson, E.},
+  year = {1998},
+  journal = {Journal of Official Statistics},
+  number = {2},
+  volume = {14},
+  pages = {149--162}
+}
+
+@book{tille2020,
+  title = {Sampling and Estimation from Finite Populations},
+  author = {Tillé, Y},
+  year = {2020},
+  publisher = {Wiley},
+  doi = {10.1002/9781119071259}
+}
+
+@article{tille2023,
+  title = {Remarks on some misconceptions about unequal probability sampling without replacement},
+  author = {Tillé, Y},
+  year = {2023},
+  journal = {Computer Science Review},
+  volume = {47},
+  pages = {100533},
+  doi = {10.1016/j.cosrev.2022.100533}
+}

vignettes/sps.qmd → vignettes/sps.Rmd

@@ -1,16 +1,13 @@
 ---
 title: "Drawing a Sequential Poisson Sample"
+bibliography: ref.bib
 vignette: >
   %\VignetteIndexEntry{Drawing a Sequential Poisson Sample}
-  %\VignetteEngine{quarto::html}
+  %\VignetteEngine{litedown::vignette}
   %\VignetteEncoding{UTF-8}
-knitr:
-  opts_chunk: 
-    collapse: true
-    comment: '#>'
 ---
 
-Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units. It's a fast, simple, and flexible method for sampling units proportional to their size, and is often used for drawing a sample of businesses. The purpose of this vignette is to give an example of how the functions in this package can be used to easily draw a sample using the sequential Poisson method. More details can be found on the help pages for the functions used in this vignette.
+Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units. It's a fast, simple, and flexible method for sampling units proportional to their size, and is often used for drawing a sample of businesses. The purpose of this vignette is to give an example of how the functions in this package can be used to easily draw a sample using the sequential Poisson method. More details, including references to the literature, can be found on the help pages for the functions used in this vignette. @tille2020 (Chapter 5) gives a textbook treatment of this method and how it compares with other probability-proportional-to-size sampling methods.
 
 ## Drawing a sample of businesses
 
@@ -211,7 +208,7 @@ summary(sampling_distribution / sum(sales) - 1)
 ```
 
 More generally, the distribution of inclusion probabilities
-is usually close to what is expected if sequential Poisson sampling was exactly proportional to size.^[See Tillé, Y. (2023). Remarks on some misconceptions about unequal probability sampling without replacement. *Computer Science Review*, 47, 100533.]
+is usually close to what is expected if sequential Poisson sampling was exactly proportional to size [@tille2023].
 
 ```{r tille, fig.width=8, fig.height=5.33}
 #| fig.alt: >
@@ -241,4 +238,3 @@ plot(
 lines(seq(-4, 4, 0.1), dnorm(seq(-4, 4, 0.1)), lty = "dashed")
 legend("topright", c("empirical", "theoretical"), lty = c("solid", "dashed"))
 ```
-

vignettes/take-all.qmd → vignettes/take-all.Rmd

@@ -3,14 +3,11 @@ title: "Take-all units"
 format: 
   html:
     html-math-method: mathjax
+bibliography: ref.bib
 vignette: >
   %\VignetteIndexEntry{Take-all units}
-  %\VignetteEngine{quarto::html}
+  %\VignetteEngine{litedown::vignette}
   %\VignetteEncoding{UTF-8}
-knitr:
-  opts_chunk: 
-    collapse: true
-    comment: '#>'
 ---
 
 When drawing a sample of size $n$ from a population of size $N$,
@@ -27,16 +24,20 @@ probability greater than 1, especially when $n$ is large. The usual procedure to
 deal with these units is to
 put them in a special take-all stratum so that they are always included in the
 sample, essentially fixing their inclusion probabilities at 1, with the
-remaining units (the take-some units) drawn at random. The
+remaining units (the take-some units) drawn at random; see, e.g., @ohlsson1998
+or @tille2020 (Chapter 5). The
 usual algorithm used by, say, `sampling::inclusionprobabilities()`
 repeatedly moves units into the take-all stratum
 and recalculates the inclusion probabilities for the remaining units
-until all inclusion probabilities are less than 1.^[To see why this needs
+until all inclusion probabilities are less than 1.[^1]
+
+[^1]: To see why this needs
 to be done repeatedly, consider a population with three units with sizes 1, 2, and 3
 and find which units have an inclusion probability greater than 1 when the sample
-size is 3.]
+size is 3.
 
-Sequential poisson sampling is a bit more complicated because it can be useful
+Sequential poisson sampling is a bit more complicated because, as noted by
+@ohlsson1998, it can be useful
 to place units with an inclusion probability greater than $1 - \alpha$, for some 
 small $\alpha$, into the take-all stratum. Unless $\alpha = 0$, the usual algorithm
 for finding take-all units can put units into the take-all stratum that have
@@ -172,3 +173,13 @@ 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"))
 symbols(4.5, 0.85, circles = 1, inches = FALSE, add = TRUE, lty = 2)
 ```
+
+This can be calculated for each unit in the population.
+
+```{r}
+sps::becomes_ta(x, alpha = alpha)
+```
+
+Knowing when a unit enters the take-all stratum also makes it possible to draw
+units one at a time with the sequential Poisson method, as with the `sps_iterator()`
+function.