Add notes on more advanced functions in betaselectr and manymome

.github/workflows/R-CMD-check.yaml

@@ -4,7 +4,7 @@ on:
   push:
     branches: [main, master]
   pull_request:
-    branches: [main, master]
+    branches: [main, master, devel]
 
 name: R-CMD-check
 

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: stdmod
 Title: Standardized Moderation Effect and Its Confidence Interval
-Version: 0.2.13
+Version: 0.2.14
 Authors@R:
     c(person(given = "Shu Fai",
            family = "Cheung",
@@ -23,7 +23,6 @@ License: GPL-3
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.3
 Suggests:
     testthat,
     knitr,
@@ -43,3 +42,4 @@ Imports:
     manymome,
     rlang
 VignetteBuilder: knitr
+Config/roxygen2/version: 8.0.0

NEWS.md

@@ -1,4 +1,13 @@
-# stdmod 0.2.13
+# stdmod 0.2.14
+
+## Miscellaneous
+
+- Updated README and other files to
+  refer users to `manymome` and `betaselectr`
+  for more advanced functions.
+  (0.2.14)
+
+# stdmod 0.2.14
 
 ## Miscellaneous
 

R/condeff.R

@@ -2,7 +2,23 @@
 #'
 #' @description Compute the conditional effects in a moderated regression model.
 #'
-#' @details [cond_effect()] uses the centering approach to find the conditional
+#' @details
+#'
+#' ## NOTE
+#'
+#' For computing conditional effects and plotting conditional
+#' effects in regression models, it is recommended to
+#' use the package [`manymome`](https://sfcheung.github.io/manymome/).
+#' It has more comprehensive support than `stdmod`. See
+#' See [this demo](https://sfcheung.github.io/manymome/articles/mo_lm.html) for
+#' a one-moderator model, and [these articles](https://sfcheung.github.io/manymome/articles/#moderated-regression)
+#' for other demonstrations. The package also supports moderation
+#' in structural equation models fitted by `lavaan`, which is not
+#' supported in `stdmod`.
+#'
+#' ## Details
+#'
+#' [cond_effect()] uses the centering approach to find the conditional
 #' effect of the focal variable. For each level of the moderator, the value for
 #' this level is subtracted from the moderator scores, and the model is
 #' fitted to the modified data.

R/plotmod.R

@@ -2,7 +2,24 @@
 #'
 #' @description Plot the moderation effect in a regression model
 #'
-#' @details This function generate a basic `ggplot2` graph
+#' @details
+#'
+#'
+#' ## NOTE
+#'
+#' For computing conditional effects and plotting conditional
+#' effects in regression models, it is recommended to
+#' use the package [`manymome`](https://sfcheung.github.io/manymome/).
+#' It has more comprehensive support than `stdmod`. See
+#' See [this demo](https://sfcheung.github.io/manymome/articles/mo_lm.html) for
+#' a one-moderator model, and [these articles](https://sfcheung.github.io/manymome/articles/#moderated-regression)
+#' for other demonstrations. The package also supports moderation
+#' in structural equation models fitted by `lavaan`, which is not
+#' supported in `stdmod`.
+#'
+#' ## Details
+#'
+#' This function generates a basic `ggplot2` graph
 #'          typically found in psychology manuscripts. It tries to
 #'          check whether one or more variables are standardized, and
 #'          report this in the plot if required.

R/std_selected.R

@@ -4,6 +4,20 @@
 #'              selected variables in a regression model and refit the model
 #'
 #' @details
+#'
+#' ## NOTE
+#'
+#' For standardizing only selected variables and for properly
+#' standardizing product terms in regression models fitted by
+#' `stats::lm()`, the function [`lm_betaselect()`](https://sfcheung.github.io/betaselectr/articles/betaselectr_lm.html)
+#' from [`betaselectr`](https://sfcheung.github.io/betaselectr/)
+#' can be used instead of `std_selected()` and `std_selected_boot()`.
+#' The package also has supports models fitted by `stats::glm()`,
+#' such as logistic regression models. See [this article](https://sfcheung.github.io/betaselectr/articles/betaselectr_glm.html)
+#' for a demonstration.
+#'
+#' ## Details
+#'
 #' [std_selected()] was originally developed to compute the standardized
 #' moderation effect and the standardized coefficients for other predictors
 #' given an [lm()] output (Cheung, Cheung, Lau, Hui, & Vong, 2022).

R/stdmod_lavaan.R

@@ -5,6 +5,19 @@
 #'              form the nonparametric bootstrap confidence interval.
 #'
 #' @details
+#'
+#' ## NOTE
+#'
+#' For standardizing only selected variables in models fitted
+#' by `lavaan`, `lavaan` natively supports this since version 0.7-2,
+#' through setting `type` to a character vector of the variables
+#' to be standardized. Alternatively, the function [`lav_betaselect()`](https://sfcheung.github.io/betaselectr/articles/betaselectr_lav.html)
+#' from [`betaselectr`](https://sfcheung.github.io/betaselectr/)
+#' can also be used. [`lav_betaselect()`](https://sfcheung.github.io/betaselectr/reference/lav_betaselect.html)
+#' also supports properly standardizing a product term. In addition
+#' to bootstrap confidence intervals, [`lav_betaselect()`](https://sfcheung.github.io/betaselectr/reference/lav_betaselect.html)
+#' also supports delta-method confidence intervals.
+#'
 #' ## Important Notes
 #'
 #' Starting from Version 0.2.7.5, of [stdmod_lavaan()] adopts an approach

README.md

@@ -13,7 +13,46 @@
 
 # stdmod: Standardized Moderation <img src="man/figures/logo.png" align="right" height="150" />
 
-(Version 0.2.13, updated on 2026-05-04, [release history](https://sfcheung.github.io/stdmod/news/index.html))
+(Version 0.2.14, updated on 2026-07-23, [release history](https://sfcheung.github.io/stdmod/news/index.html))
+
+**IMPORTANT NOTICE**
+
+> This package will no longer be actively updated. It will
+still be maintained. However, new features will not be added.
+The package [`manymome`](https://sfcheung.github.io/manymome/)
+can do all the tasks in `stdmod` related to computing,
+testing, and printing conditional effects, and can be used
+for any number of moderators. The package
+[`betaselectr`](https://sfcheung.github.io/betaselectr/)
+can do all the tasks related to forming confidence intervals
+for properly standardized coefficients, in both regression
+models fitted by `stats::lm()` and `stats::glm()`, as well
+as structural equation models fitted by `lavaan::sem()`.
+
+> For standardizing only selected variables and for properly
+standardizing product terms in regression models fitted by
+`stats::lm()`, the function [`lm_betaselect()`](https://sfcheung.github.io/betaselectr/articles/betaselectr_lm.html)
+from [`betaselectr`](https://sfcheung.github.io/betaselectr/)
+can be used instead of `std_selected()` and `std_selected_boot()`.
+The package also has supports models fitted by `stats::glm()`,
+such as logistic regression models. See [this article](https://sfcheung.github.io/betaselectr/articles/betaselectr_glm.html)
+for a demonstration.
+
+> For standardizing only selected variables in models fitted
+by `lavaan`, `lavaan` natively supports this since version 0.7-2,
+through setting `type` to a character vector of the variables
+to be standardized. Alternatively, the function [`lav_betaselect()`](https://sfcheung.github.io/betaselectr/articles/betaselectr_lav.html)
+from [`betaselectr`](https://sfcheung.github.io/betaselectr/)
+can also be used. [`lav_betaselect()`](https://sfcheung.github.io/betaselectr/reference/lav_betaselect.html)
+also supports properly standardizing a product term. In addition
+to bootstrap confidence intervals, [`lav_betaselect()`](https://sfcheung.github.io/betaselectr/reference/lav_betaselect.html)
+also supports delta-method confidence intervals.
+
+> For computing conditional effects and plotting conditional
+effects in regression models, the package [`manymome`](https://sfcheung.github.io/manymome/)
+has more comprehensive support. See [these articles](https://sfcheung.github.io/manymome/articles/#moderated-regression)
+for some demonstration. The package also supports moderation
+in structural equation models fitted by `lavaan`.
 
 (Important changes since 0.2.0.0: Bootstrap confidence intervals and
 variance-covariance matrix of estimates are the defaults of `confint()`
@@ -54,11 +93,47 @@ https://sfcheung.github.io/stdmod/
 - [plotmod](https://sfcheung.github.io/stdmod/articles/plotmod.html): How to generate a typical plot of
   moderation effect using `plotmod()`.
 
+    - It is recommended to use [`manymome`](https://sfcheung.github.io/manymome/)
+      for plotting conditional effects. See [this article](https://sfcheung.github.io/manymome/articles/mo_lm.html)
+      for a demonstration, and [these articles](https://sfcheung.github.io/manymome/articles/#moderated-regression)
+      for more complicated models.
+
 - [cond_effect](https://sfcheung.github.io/stdmod/articles/cond_effect.html): How to compute conditional
   effects of the predictor for selected
   levels of the moderator, and form nonparametric bootstrap
   confidence intervals these effects.
 
+    - It is recommended to use [`manymome`](https://sfcheung.github.io/manymome/)
+      for computing conditional effects. See [this article](https://sfcheung.github.io/manymome/articles/mo_lm.html)
+      for a demonstration, and [these articles](https://sfcheung.github.io/manymome/articles/#moderated-regression)
+      for more complicated models.
+
+## Related Functions in `betaselectr`
+
+The function `lm_betaselect()` from the package `betaselectr`
+can be used in place
+of `std_selected()` and `std_selected_boot()`. A demonstration
+of `lm_betaselect()` can be found [here](https://sfcheung.github.io/betaselectr/articles/betaselectr_lm.html).
+This package also has `glm_betaselect()` for models, such
+as logistic regression models, fitted by `stats::glm()`
+(see a demonstration [here](https://sfcheung.github.io/betaselectr/articles/betaselectr_glm.html)).
+
+The function `lav_betaselect()` from the package `betaselectr`
+is a version of `std_selected()` but for structural equation
+models fitted by `lavaan::sem()`. A demonstration
+of `lav_betaselect()` can be found [here](https://sfcheung.github.io/betaselectr/articles/betaselectr_lav.html).
+
+## Related Functions in `manymome`
+
+Although the package `manymome` is mainly for mediation and
+moderated mediation, moderation is a special case and is also
+supported. The `plot` method in `manymome` is more powerful
+than `plotmod`, supports not only a regression model but also
+a structural equation model, and also supports any number of
+moderators. The function `manymome::cond_effects()` in `manymome`
+is also more powerful than `cond_effect` in `stdmod`, supporting
+both regression models and structural equation models.
+
 # Installation
 
 The stable CRAN version can be installed by `install.packages()`:
@@ -90,4 +165,4 @@ are also taken into account.
 # Issues
 
 If you have any suggestions and found any bugs, please feel
-feel to open a GitHub issue. Thanks.
+free to open a GitHub issue. Thanks.