Update README with ver info; remove docs/ from .gitignore

Author: friendly

.gitignore

@@ -5,8 +5,6 @@
 /*.Rproj
 Rplots.pdf
 revdep/
-# pkgdown now build by github actions
-/docs/
 /doc/
 /Meta/
 

README.Rmd

@@ -15,7 +15,10 @@ knitr::opts_chunk$set(
 #  out.width = "100%"
 )
 
+# get package versions
 library(vcdExtra)
+cran_version <- available.packages(repos = "https://cloud.r-project.org")["vcdExtra", "Version"]
+dev_version <- getNamespaceVersion("vcdExtra")
 ```
 
 <!-- badges: start -->
@@ -53,16 +56,16 @@ It is also used in my graduate course, [Psy 6136: Categorical Data Analysis](htt
 
 ## Installation
 
-Get the released version from CRAN:
+Get the released version (`r cran_version`) from CRAN:
 
      install.packages("vcdExtra")
 
-The current development version can be installed from [R-universe](https://friendly.r-universe.dev/vcdExtra) or
+The current development version (`r dev_version`) can be installed from [R-universe](https://friendly.r-universe.dev/vcdExtra) or
 directly from the [GitHub repo](https://github.com/friendly/vcdExtra) via:
 
      if (!require(remotes)) install.packages("remotes")
      
-     install.packages("mvinfluence", repos = c('https://friendly.r-universe.dev')
+     install.packages("vcdExtra", repos = c('https://friendly.r-universe.dev')
      # or
      remotes::install_github("friendly/vcdExtra", build_vignettes = TRUE)
 
@@ -137,21 +140,33 @@ vcdExtra::datasets("vcdExtra")[,1]
 * a [collection of tutorial vignettes](https://cran.r-project.org/web/packages/vcdExtra/vignettes/). In the installed package, they can be viewed using `browseVignettes(package = "vcdExtra")`;
 
 ```{r vignettes}
-tools::getVignetteInfo("vcdExtra")[,c("File", "Title")] |> knitr::kable()
+vigns <- as.data.frame(tools::getVignetteInfo("vcdExtra")[,c("File", "Title")])
+vigns$Title <- paste0("[", vigns$Title, "](https://friendly.github.io/vcdExtra/articles/",
+                      tools::file_path_sans_ext(vigns$File), ".html)")
+vigns |> knitr::kable()
 ```
 
 * a few useful utility functions for manipulating categorical data sets and working with models for
-categorical data. 
+categorical data: `joint()`, `conditional()`, `mutual()`, `saturated()`. 
+
+* A re-implementation of `vcd::woolf_test()` extends the analysis of homogeneity of odds ratios
+in 2 x 2 x R x C tables to provide tests for differences among the R strata rows and C strata columns.
+
+
 
 
 ## Examples
 
-These `README` examples simply provide illustrations of using some of the package functions in the
+These `README` examples provide simple illustrations of using some of the package functions in the
 context of loglinear models for frequency tables fit using `glm()`, including
 models for _structured associations_ taking ordinality into account.
 
-The dataset `Mental` is a data frame frequency table representing the cross-classification of mental health status (`mental`) of 1660 young New York residents by their parents' socioeconomic status (`ses`).
-Both are _ordered_ factors.
+### `Mental` dataset
+The dataset `vcdExtra::Mental` is a data frame frequency table representing the cross-classification of mental health status (`mental`) of 1660 young New York residents by their parents' socioeconomic status (`ses`).
+Both are _ordered_ factors. The questions are: 
+* Is `mental` health associated with parents `ses`? 
+* If so, what is the pattern/nature of the association?
+* How can I take the ordinal nature of the factors into account?
 
 ```{r ex-mental1}
 data(Mental)
@@ -212,6 +227,11 @@ These models typically use equally spaced _integer_ scores.
 The test for association here is analogous to a test of the correlation
 between the frequency-weighted scores, carried out using `CMHtest()`.
 
+```{r}
+CMHtest(Mental.tab)
+```
+
+
 In the data, `ses` and `mental` were declared to be ordered factors,
 so using `as.numeric(Mental$ses)` is sufficient to create a new `Cscore`
 variable. Similarly for the numeric version of `mental`, giving `Rscore`.

README.md

@@ -46,17 +46,17 @@ Data Analysis](https://friendly.github.io/psy6136/).
 
 ## Installation
 
-Get the released version from CRAN:
+Get the released version (0.8.7) from CRAN:
 
      install.packages("vcdExtra")
 
-The current development version can be installed from
+The current development version (0.9.0) can be installed from
 [R-universe](https://friendly.r-universe.dev/vcdExtra) or directly from
 the [GitHub repo](https://github.com/friendly/vcdExtra) via:
 
      if (!require(remotes)) install.packages("remotes")
      
-     install.packages("mvinfluence", repos = c('https://friendly.r-universe.dev')
+     install.packages("vcdExtra", repos = c('https://friendly.r-universe.dev')
      # or
      remotes::install_github("friendly/vcdExtra", build_vignettes = TRUE)
 
@@ -76,11 +76,11 @@ packages.
 
 ##### See also:
 
-<a href="https://www.routledge.com/Discrete-Data-Analysis-with-R-Visualization-and-Modeling-Techniques-for/Friendly-Meyer/p/book/9781498725835"><img src="man/figures/ddar-cover.png" style="height:70px;"/></a> 
+<a href="https://www.routledge.com/Discrete-Data-Analysis-with-R-Visualization-and-Modeling-Techniques-for/Friendly-Meyer/p/book/9781498725835"><img src="man/figures/ddar-cover.png" style="height:80px;"/></a> 
 
-<a href="https://friendly.github.io/psy6136/"><img src="https://friendly.github.io/psy6136/icons/psy6136-highres.png" style="height:70px;" /></a> 
+<a href="https://friendly.github.io/psy6136/"><img src="https://friendly.github.io/psy6136/icons/psy6136-highres.png" style="height:80px;" /></a> 
 
-<a href="https://friendly.github.io/nestedLogit/"><img src="https://friendly.github.io/nestedLogit/logo.png" style="height:70px;" /></a>
+<a href="https://friendly.github.io/nestedLogit/"><img src="https://friendly.github.io/nestedLogit/logo.png" style="height:80px;" /></a>
 
 - My book, [*Discrete Data Analysis with R: Visualization and Modeling
   Techniques for Categorical and Count
@@ -157,26 +157,48 @@ vcdExtra::datasets("vcdExtra")[,1]
   `browseVignettes(package = "vcdExtra")`;
 
 ``` r
-tools::getVignetteInfo("vcdExtra")[,c("File", "Title")] |> knitr::kable()
+vigns <- as.data.frame(tools::getVignetteInfo("vcdExtra")[,c("File", "Title")])
+vigns$Title <- paste0("[", vigns$Title, "](https://friendly.github.io/vcdExtra/articles/",
+                      tools::file_path_sans_ext(vigns$File), ".html)")
+vigns |> knitr::kable()
 ```
 
 | File | Title |
-|:-----|:------|
+|:---|:---|
+| a1-creating.Rmd | [1. Creating and manipulating frequency tables](https://friendly.github.io/vcdExtra/articles/a1-creating.html) |
+| a2-tests.Rmd | [2. Tests of Independence](https://friendly.github.io/vcdExtra/articles/a2-tests.html) |
+| a3-loglinear.Rmd | [3. Loglinear Models](https://friendly.github.io/vcdExtra/articles/a3-loglinear.html) |
+| a4-mosaics.Rmd | [4. Mosaic plots](https://friendly.github.io/vcdExtra/articles/a4-mosaics.html) |
+| a5-demo-housing.Rmd | [5. Demo - Housing Data](https://friendly.github.io/vcdExtra/articles/a5-demo-housing.html) |
+| a6-mobility.Rmd | [6. Mobility tables](https://friendly.github.io/vcdExtra/articles/a6-mobility.html) |
+| a7-continuous.Rmd | [7. Continuous predictors](https://friendly.github.io/vcdExtra/articles/a7-continuous.html) |
+| datasets.Rmd | [Datasets for categorical data analysis](https://friendly.github.io/vcdExtra/articles/datasets.html) |
+| tidyCats.Rmd | [tidyCat: Tidy Methods For Categorical Data Analysis](https://friendly.github.io/vcdExtra/articles/tidyCats.html) |
 
 - a few useful utility functions for manipulating categorical data sets
-  and working with models for categorical data.
+  and working with models for categorical data: `joint()`,
+  `conditional()`, `mutual()`, `saturated()`.
+
+- A re-implementation of `vcd::woolf_test()` extends the analysis of
+  homogeneity of odds ratios in 2 x 2 x R x C tables to provide tests
+  for differences among the R strata rows and C strata columns.
 
 ## Examples
 
-These `README` examples simply provide illustrations of using some of
+These `README` examples provide simple illustrations of using some of
 the package functions in the context of loglinear models for frequency
 tables fit using `glm()`, including models for *structured associations*
 taking ordinality into account.
 
-The dataset `Mental` is a data frame frequency table representing the
-cross-classification of mental health status (`mental`) of 1660 young
-New York residents by their parents’ socioeconomic status (`ses`). Both
-are *ordered* factors.
+### `Mental` dataset
+
+The dataset `vcdExtra::Mental` is a data frame frequency table
+representing the cross-classification of mental health status (`mental`)
+of 1660 young New York residents by their parents’ socioeconomic status
+(`ses`). Both are *ordered* factors. The questions are: \* Is `mental`
+health associated with parents `ses`? \* If so, what is the
+pattern/nature of the association? \* How can I take the ordinal nature
+of the factors into account?
 
 ``` r
 data(Mental)
@@ -264,6 +286,17 @@ variables. These models typically use equally spaced *integer* scores.
 The test for association here is analogous to a test of the correlation
 between the frequency-weighted scores, carried out using `CMHtest()`.
 
+``` r
+CMHtest(Mental.tab)
+## Cochran-Mantel-Haenszel Statistics for ses by mental 
+## 
+##                  AltHypothesis  Chisq Df       Prob
+## cor        Nonzero correlation 37.156  1 1.0907e-09
+## rmeans  Row mean scores differ 40.297  5 1.3012e-07
+## cmeans  Col mean scores differ 40.666  3 7.6971e-09
+## general    General association 45.958 15 5.4003e-05
+```
+
 In the data, `ses` and `mental` were declared to be ordered factors, so
 using `as.numeric(Mental$ses)` is sufficient to create a new `Cscore`
 variable. Similarly for the numeric version of `mental`, giving

cran-comments.md

@@ -1,6 +1,6 @@
 ## Test environments
 * local Windows 10, R version 4.5.2 (2025-10-31 ucrt) 
-* win-builder R Under development (unstable) (2025-07-22 r88445 ucrt)
+* win-builder R Under development (unstable) (2026-01-18 r89306 ucrt)
 
 
 ## R CMD check results
@@ -17,19 +17,20 @@ This is a major release of the package, adding structure to dataset examples and
 * Generalized `vcd::woolf_test()` to handle strata better, with special handling for 2 x 2 x R x C case. 
 * Completed extension of `vcd::woolf_test()` to fully handle R x C strata, with tests for rows/cols/residuals
 * Added GH actions R-CMD-check & pkgdown
+* Now depends R (>= 4.1.0) due to use of native pipe `|>`
+* Fixed some broken URLs in `R/data.R`
 
 # reverse dependencies
 
 > devtools::revdep()
-[1] "aplore3"            "catdata"            "genridge"           "gnm"               
-[5] "heplots"            "iarm"               "jmv"                "public.ctn0094data"
-[9] "reappraised" 
+ [1] "aplore3"            "catdata"            "genridge"           "gnm"                "heplots"           
+ [6] "iarm"               "jmv"                "junco"              "public.ctn0094data" "reappraised" 
 
 > revdepcheck::revdep_check(num_workers = 4)
 
 ## revdepcheck results
 
-We checked 9 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
+We checked 10 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
 
  * We saw 0 new problems
  * We failed to check 0 packages

vignettes/tidyCats.Rmd

@@ -76,7 +76,7 @@ expand.dft(hec.df) |> head()
 ```
 
 
-* `vcd::structable()` (and `stats::ftable()`) produce a ‘flat’ representation of a high-dimensional contingency table constructed by recursive splits (similar to the construction of mosaic displays). One can be constructed from a table or from a data frame with
+* `vcd::structable()` (and `stats::ftable()`) produce a 'flat' representation of a high-dimensional contingency table constructed by recursive splits (similar to the construction of mosaic displays). One can be constructed from a table or from a data frame with
 a formula method,
 
 ```{r}