fix merge

Author: friendly

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: vcdExtra
 Type: Package
 Title: 'vcd' Extensions and Additions
-Version: 0.8-7
-Date: 2025-11-18
+Version: 0.8.7
+Date: 2025-12-10
 Language: en-US
 Authors@R: c(
             person(given = "Michael", 
@@ -29,7 +29,7 @@ Authors@R: c(
             person(given = "Daniel", family = "Sabanes Bove", role="ctb")
             )
 Maintainer: Michael Friendly <[email protected]>
-Depends: R (>= 3.5.0), vcd, gnm (>= 1.0-3), grid
+Depends: R (>= 3.5.0), vcd, gnm (>= 1.0-3)
 Suggests: 
     gmodels,
     Fahrmeir,
@@ -58,11 +58,12 @@ Suggests:
     readxl, 
     stringr, 
     tidyr (>= 1.3.0)
-Imports: 
-    MASS, 
-    grDevices, 
-    stats, 
-    utils, 
+Imports:
+    MASS,
+    grDevices,
+    grid,
+    stats,
+    utils,
     ca,
     rgl
 Description: Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data
@@ -80,3 +81,4 @@ LazyData: yes
 Encoding: UTF-8
 Config/testthat/edition: 3
 RoxygenNote: 7.3.3
+Roxygen: list(markdown = TRUE)

NEWS.md

@@ -1,13 +1,15 @@
-## Version 0.8.7 (2025-11-18)
+## Version 0.8.7 (2025-12-10)
 
 This is a major release of the package, fixing bugs and revising documentation
 
 o Added tests for CMHtest() PR #13 [Thx: Daniel Sabanes Bove] 
 o Automatically omit strata with a single observation in CMHtest() because they do not contribute to the test statistics
 o Use the generalized Moore-Penrose inverse from MASS in CMHtest() such that it can work when the variance
   matrix is singular.
-o Converted the package to use roxygen documentation.
+o Converted the package to use roxygen documentation via {rd2roxygen}. UGH!
 o Added `CrabSatellites` data from {countreg} b/c that's still not available on CRAN
+o Moved `grid` from `Depends:` to `Imports:`
+o Now use markdown in package documentation for easier maintenance, via {roxygen2md}
 
 ## Version 0.8-6 (2025-07-23)
 

R/CMHtest.R

@@ -17,13 +17,11 @@
 # DONE: With strata, calculate overall CMH tests controlling for strata
 # FIXED: rmeans and cmeans tests were labeled incorrectly
 
-
-
 #' Generalized Cochran-Mantel-Haenszel Tests
 #'
 #' Provides generalized Cochran-Mantel-Haenszel tests of association of two
 #' possibly ordered factors, optionally stratified other factor(s).  With
-#' strata, \code{CMHtest} calculates these tests for each level of the
+#' strata, `CMHtest` calculates these tests for each level of the
 #' stratifying variables and also provides overall tests controlling for the
 #' strata.
 #'
@@ -41,29 +39,29 @@
 #' The CMH analysis for a two-way table produces generalized
 #' Cochran-Mantel-Haenszel statistics (Landis etal., 1978).
 #'
-#' These include the CMH \bold{correlation} statistic (\code{"cor"}), treating
+#' These include the CMH **correlation** statistic (`"cor"`), treating
 #' both factors as ordered. For a given statum, with equally spaced row and
 #' column scores, this CMH statistic reduces to \eqn{(n-1) r^2}, where \eqn{r}
-#' is the Pearson correlation between X and Y. With \code{"midrank"} scores,
+#' is the Pearson correlation between X and Y. With `"midrank"` scores,
 #' this CMH statistic is analogous to \eqn{(n-1) r_S^2}, using the Spearman
 #' rank correlation.
 #'
-#' The \bold{ANOVA} (row mean scores and column mean scores) statistics, treat
+#' The **ANOVA** (row mean scores and column mean scores) statistics, treat
 #' the columns and rows respectively as ordinal, and are sensitive to mean
 #' shifts over columns or rows. These are transforms of the \eqn{F} statistics
 #' from one-way ANOVAs with equally spaced scores and to Kruskal-Wallis tests
-#' with \code{"midrank"} scores.
+#' with `"midrank"` scores.
 #'
-#' The CMH \bold{general} association statistic treat both factors as
+#' The CMH **general** association statistic treat both factors as
 #' unordered, and give a test closely related to the Pearson \eqn{\chi^2} test.
 #' When there is more than one stratum, the overall general CMH statistic gives
 #' a stratum-adjusted Pearson \eqn{\chi^2}, equivalent to what is calculated by
 #' \code{\link[stats]{mantelhaen.test}}.
 #'
 #' For a 3+ way table, one table of CMH tests is produced for each combination
-#' of the factors identified as \code{strata}. If \code{overall=TRUE}, an
+#' of the factors identified as `strata`. If `overall=TRUE`, an
 #' additional table is calculated for the same two primary variables,
-#' controlling for (pooling over) the \code{strata} variables.
+#' controlling for (pooling over) the `strata` variables.
 #'
 #' These overall tests implicitly assume no interactions between the primary
 #' variables and the strata and they will have low power in the presence of
@@ -73,41 +71,42 @@
 #' observations) are automatically omitted from the analysis.
 #'
 #' @aliases CMHtest CMHtest.formula CMHtest.default Cochran Mantel Haenszel test print.CMHtest
-#' @param x A 2+ way contingency table in array form, or a class \code{"table"}
+#' @param x A 2+ way contingency table in array form, or a class `"table"`
 #'          object with optional category labels specified in the dimnames(x) attribute.
 #' @param formula a formula specifying the variables used to create a
-#'           contingency table from \code{data}.  This should be a one-sided formula when
-#'           \code{data} is in array form, and a two-sided formula with a response
-#'           \code{Freq} if \code{data} is a data frame with a cell frequency variable.
+#'           contingency table from `data`.  This should be a one-sided formula when
+#'           `data` is in array form, and a two-sided formula with a response
+#'           `Freq` if `data` is a data frame with a cell frequency variable.
 #'            For convenience, conditioning formulas can be specified indicating strata.
-#' @param data either a data frame, or an object of class \code{"table"} or \code{"ftable"}.
+#' @param data either a data frame, or an object of class `"table"` or `"ftable"`.
 #' @param subset an optional vector specifying a subset of observations to be used.
-#' @param na.action a function which indicates what should happen when the data contain \code{NA}s.  Ignored if \code{data} is a contingency table
+#' @param na.action a function which indicates what should happen when the data contain `NA`s.
+#'           Ignored if `data` is a contingency table.
 #' @param strata For a 3- or higher-way table, the names or numbers of the
 #'           factors to be treated as strata.  By default, the first 2 factors are
 #'           treated as the main table variables, and all others considered stratifying factors.
 #' @param rscores Row scores.  Either a set of numbers (typically integers,
-#'            \code{1:R}) or the string \code{"midrank"} for standardized midrank scores,
-#' or \code{NULL} to exclude tests that depend on row scores.
+#'            `1:R`) or the string `"midrank"` for standardized midrank scores,
+#' or `NULL` to exclude tests that depend on row scores.
 #' @param cscores Column scores. Same as for row scores.
-#' @param types Types of CMH tests to compute: Any one or more of \code{c("cor", "cmeans", "rmeans", "general")}, or
-#'           \code{"ALL"} for all of these.
+#' @param types Types of CMH tests to compute: Any one or more of `c("cor", "cmeans", "rmeans", "general")`, or
+#'           `"ALL"` for all of these.
 #' @param overall logical. Whether to calculate overall tests, controlling for the stratifying factors.
 #' @param details logical.  Whether to include computational details in the result
 #' @param \dots Other arguments passed to default method.
 #' @param digits Digits to print.
 #'
-#' @return An object of class \code{"CMHtest"} , a list with the following 4 components:
+#' @return An object of class `"CMHtest"` , a list with the following 4 components:
 #'
-#' \item{table}{A matrix containing the test statistics, with columns \code{Chisq}, \code{Df} and \code{Prob} }
+#' \item{table}{A matrix containing the test statistics, with columns `Chisq`, `Df` and `Prob` }
 #' \item{names}{The names of the table row and column variables}
 #' \item{rscore}{Row scores}
 #' \item{cscore}{Column scores}
 #'
-#' If \code{details==TRUE}, additional components are included.
+#' If `details==TRUE`, additional components are included.
 #'
-#' If there are strata, the result is a list of \code{"CMHtest"} objects. If
-#' \code{overall=TRUE} another component, labeled \code{ALL} is appended to the
+#' If there are strata, the result is a list of `"CMHtest"` objects. If
+#' `overall=TRUE` another component, labeled `ALL` is appended to the
 #' list.
 #'
 #' @author Michael Friendly
@@ -120,25 +119,25 @@
 #' Cochran-Mantel-Haenszel chi-squared test of the null that two nominal
 #' variables are conditionally independent in each stratum, assuming that there
 #' is no three-way interaction
+#' @family association tests
 #'
-#' @references Stokes, M. E. & Davis, C. S.  & Koch, G., (2000).
-#' \emph{Categorical Data Analysis using the SAS System}, 2nd Ed., Cary, NC:
+#' @references
+#' Stokes, M. E. & Davis, C. S.  & Koch, G., (2000).
+#' *Categorical Data Analysis using the SAS System*, 2nd Ed., Cary, NC:
 #' SAS Institute, pp 74-75, 92-101, 124-129.  Details of the computation are
-#' given at:
-#' \url{http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_freq_a0000000648.htm
-#' }
+#' given at: <http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_freq_a0000000648.htm>
 #'
 #' Cochran, W. G. (1954), Some Methods for Strengthening the Common
-#' \eqn{\chi^2} Tests, \emph{Biometrics}, 10, 417-451.
+#' \eqn{\chi^2} Tests, *Biometrics*, 10, 417-451.
 #'
 #' Landis, R. J., Heyman, E. R., and Koch, G. G. (1978).  Average Partial
 #' Association in Three-way Contingency Tables: A Review and Discussion of
-#' Alternative Tests, \emph{International Statistical Review}, \bold{46},
+#' Alternative Tests, *International Statistical Review*, **46**,
 #' 237-254.
 #'
 #' Mantel, N. (1963), Chi-square Tests with One Degree of Freedom: Extensions
-#' of the Mantel-Haenszel Procedure," \emph{Journal of the American Statistical
-#' Association}, 58, 690-700.
+#' of the Mantel-Haenszel Procedure," *Journal of the American Statistical
+#' Association*, 58, 690-700.
 #' @keywords htest
 #' @export
 #' @examples

R/CrabSatellites.R

@@ -29,7 +29,7 @@
 #' color, spine condition, weight, and carapace width. Color and spine condition are ordered factors but are treated as numeric in some analyses.
 
 #' @source
-#' Table 4.3 in Agresti (2002). This dataset was taken from the \pkg{countreg}, which is not on CRAN
+#' Table 4.3 in Agresti (2002). This dataset was taken from the \pkg{countreg} package, which is not on CRAN
 #'
 #' @references
 #' Agresti A (2002). Categorical Data Analysis, 2nd ed., John Wiley & Sons, Hoboken.
@@ -39,9 +39,7 @@
 #' Brockmann HJ (1996). “Satellite Male Groups in Horseshoe Crabs, Limulus polyphemus”, Ethology, 102(1), 1–21.
 #'
 #' @examples
-#' # example code
-#' ## load data, use ordered factors as numeric, and
-#' ## grouped factor version of width
+#' ## load data, use ordered factors as numeric, and grouped factor version of width
 #' data("CrabSatellites", package = "vcdExtra")
 #' CrabSatellites <- transform(CrabSatellites,
 #'   color = as.numeric(color),

R/Crossings.R

@@ -1,14 +1,14 @@
 #' Crossings Interaction of Factors
 #'
 #' Given two ordered factors in a square, n x n frequency table,
-#' \code{Crossings} creates an n-1 column matrix corresponding to different
+#' `Crossings` creates an n-1 column matrix corresponding to different
 #' degrees of difficulty in crossing from one level to the next, as described
 #' by Goodman (1972).
 #'
 #'
 #' @param \dots Two factors
-#' @return For two factors of \code{n} levels, returns a binary indicator
-#' matrix of \code{n*n} rows and \code{n-1} columns.
+#' @return For two factors of `n` levels, returns a binary indicator
+#' matrix of `n*n` rows and `n-1` columns.
 #' @author Michael Friendly and Heather Turner
 #' @seealso
 #' \code{\link[stats]{glm}}, \code{\link[gnm]{gnm}} for model fitting
@@ -18,8 +18,8 @@
 #'
 #' @references
 #' Goodman, L. (1972).  Some multiplicative models for the analysis
-#' of cross-classified data. In: \emph{Proceedings of the Sixth Berkeley
-#' Symposium on Mathematical Statistics and Probability}, Berkeley, CA:
+#' of cross-classified data. In: *Proceedings of the Sixth Berkeley
+#' Symposium on Mathematical Statistics and Probability*, Berkeley, CA:
 #' University of California Press, pp. 649-696.
 #' @keywords models manip
 #' @export

R/GKgamma.R

@@ -14,7 +14,7 @@
 #' @aliases GKgamma print.GKgamma
 #' @param x A two-way frequency table, in matrix or table form.  The rows and columns are considered to be ordinal factors
 #' @param level Confidence level for a significance test of \eqn{\gamma \ne =}{gamma !=0}
-#' @return Returns an object of class \code{"GKgamma"} with 6 components, as follows
+#' @return Returns an object of class `"GKgamma"` with 6 components, as follows
 #' \describe{
 #'   \item{gamma}{The gamma statistic}
 #'   \item{C}{Total number of concordant pairs in the table}
@@ -26,16 +26,17 @@
 #'
 #' @author Michael Friendly; original version by Laura Thompson
 #' @seealso \code{\link[vcd]{assocstats}}, \link[vcd]{Kappa}
-#' @references Agresti, A. \emph{Categorical Data Analysis}. John Wiley & Sons,
+#' @family association tests
+#' @references Agresti, A. *Categorical Data Analysis*. John Wiley & Sons,
 #' 2002, pp. 57--59.
 #'
 #' Goodman, L. A., & Kruskal, W. H. (1954). Measures of association for cross
-#' classifications. \emph{Journal of the American Statistical Association}, 49,
+#' classifications. *Journal of the American Statistical Association*, 49,
 #' 732-764.
 #'
 #' Goodman, L. A., & Kruskal, W. H. (1963). Measures of association for cross
-#' classifications III: Approximate sampling theory. \emph{Journal of the
-#' American Statistical Association}, 58, 310-364.
+#' classifications III: Approximate sampling theory. *Journal of the
+#' American Statistical Association*, 58, 310-364.
 #' @keywords htest category
 #' @examples
 #'

R/HLtest.R

@@ -10,48 +10,49 @@
 
 #' Hosmer-Lemeshow Goodness of Fit Test
 #' 
-#' The \code{HLtest} function computes the classical Hosmer-Lemeshow (1980)
-#' goodness of fit test for a binomial \code{glm} object in logistic regression
+#' The `HLtest` function computes the classical Hosmer-Lemeshow (1980)
+#' goodness of fit test for a binomial `glm` object in logistic regression
 #' 
 #' The general idea is to assesses whether or not the observed event rates
 #' match expected event rates in subgroups of the model population. The
 #' Hosmer-Lemeshow test specifically identifies subgroups as the deciles of
-#' fitted event values, or other quantiles as determined by the \code{g}
-#' argument. Given these subgroups, a simple chisquare test on \code{g-2} df is
+#' fitted event values, or other quantiles as determined by the `g`
+#' argument. Given these subgroups, a simple chisquare test on `g-2` df is
 #' used.
 #' 
-#' In addition to \code{print} and \code{summary} methods, a \code{plot} method
+#' In addition to `print` and `summary` methods, a `plot` method
 #' is supplied to visualize the discrepancies between observed and fitted
 #' frequencies.
 #' 
 #' 
 #' @aliases HosmerLemeshow HLtest plot.HLtest print.HLtest rootogram.HLtest
 #' summary.HLtest
-#' @param model A \code{glm} model object in the \code{binomial} family
+#' @param model A `glm` model object in the `binomial` family
 #' @param g Number of groups used to partition the fitted values for the GOF
 #' test.
-#' @param x,object A \code{HLtest} object
+#' @param x,object A `HLtest` object
 #' @param \dots Other arguments passed down to methods
-#' @return A class \code{HLtest} object with the following components:
+#' @return A class `HLtest` object with the following components:
 #' \item{table}{A data.frame describing the results of partitioning the data
-#' into \code{g} groups with the following columns: \code{cut}, \code{total},
-#' \code{obs}, \code{exp}, \code{chi}} \item{chisq}{The chisquare statistics}
+#' into `g` groups with the following columns: `cut`, `total`,
+#' `obs`, `exp`, `chi`} \item{chisq}{The chisquare statistics}
 #' \item{df}{Degrees of freedom} \item{p.value}{p value} \item{groups}{Number
-#' of groups} \item{call}{\code{model} call} %% ...
+#' of groups} \item{call}{`model` call} %% ...
 #' @author Michael Friendly
 #' @seealso \code{\link[vcd]{rootogram}}, ~~~
+#' @family association tests
 #' @references
 #' 
 #' Hosmer, David W., Lemeshow, Stanley (1980).  A goodness-of-fit test for
-#' multiple logistic regression model. \emph{Communications in Statistics,
-#' Series A}, 9, 1043-1069.
+#' multiple logistic regression model. *Communications in Statistics,
+#' Series A*, 9, 1043-1069.
 #' 
-#' Hosmer, David W., Lemeshow, Stanley (2000).  \emph{Applied Logistic
-#' Regression}, New York: Wiley, ISBN 0-471-61553-6
+#' Hosmer, David W., Lemeshow, Stanley (2000).  *Applied Logistic
+#' Regression*, New York: Wiley, ISBN 0-471-61553-6
 #' 
 #' Lemeshow, S. and Hosmer, D.W.  (1982).  A review of goodness of fit
 #' statistics for use in the development of logistic regression models.
-#' \emph{American Journal of Epidemiology}, 115(1), 92-106.
+#' *American Journal of Epidemiology*, 115(1), 92-106.
 #' 
 #' @importFrom vcd rootogram
 #' @keywords htest