Merge branch 'main' into support_coxme_ranef

Author: strengejacke

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

@@ -8,4 +8,4 @@ name: R-CMD-check
 
 jobs:
   R-CMD-check:
-    uses: easystats/workflows/.github/workflows/R-CMD-check.yaml@main
+    uses: easystats/workflows/.github/workflows/R-CMD-check-no-oldrel.yaml@main

.github/workflows/test-coverage.yaml

@@ -1,6 +1,8 @@
 # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
+  push:
+    branches: [main, master]
   pull_request:
     branches: [main, master]
 

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.26.0.2
+Version: 0.27.0.3
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -78,9 +78,9 @@ BugReports: https://github.com/easystats/parameters/issues
 Depends:
     R (>= 3.6)
 Imports:
-    bayestestR (>= 0.16.0),
-    datawizard (>= 1.1.0),
-    insight (>= 1.3.0),
+    bayestestR (>= 0.16.1),
+    datawizard (>= 1.2.0),
+    insight (>= 1.3.1),
     graphics,
     methods,
     stats,
@@ -110,11 +110,13 @@ Suggests:
     cluster,
     cobalt,
     coda,
+    correlation (>= 0.8.8),
     coxme,
     cplm,
     curl,
     dbscan,
     did,
+    discovr,
     distributional,
     domir (>= 0.2.0),
     drc,
@@ -173,6 +175,7 @@ Suggests:
     mmrm,
     multcomp,
     MuMIn,
+    mvtnorm,
     NbClust,
     nFactors,
     nestedLogit,
@@ -199,6 +202,7 @@ Suggests:
     rms,
     rstan,
     rstanarm,
+    sampleSelection,
     sandwich,
     see (>= 0.8.1),
     serp,
@@ -227,4 +231,4 @@ Config/testthat/parallel: true
 Config/Needs/website: easystats/easystatstemplate
 Config/Needs/check: stan-dev/cmdstanr
 Config/rcmdcheck/ignore-inconsequential-notes: true
-Remotes: easystats/insight, easystats/bayestestR
+Remotes: easystats/insight

NAMESPACE

@@ -126,10 +126,14 @@ S3method(display,parameters_brms_meta)
 S3method(display,parameters_efa)
 S3method(display,parameters_efa_summary)
 S3method(display,parameters_model)
+S3method(display,parameters_omega)
+S3method(display,parameters_omega_summary)
+S3method(display,parameters_p_function)
 S3method(display,parameters_pca)
 S3method(display,parameters_pca_summary)
 S3method(display,parameters_sem)
 S3method(display,parameters_simulate)
+S3method(display,parameters_standardized)
 S3method(dof_satterthwaite,lmerMod)
 S3method(equivalence_test,MixMod)
 S3method(equivalence_test,feis)
@@ -149,6 +153,11 @@ S3method(equivalence_test,rma)
 S3method(equivalence_test,wbm)
 S3method(equivalence_test,zeroinfl)
 S3method(factor_analysis,data.frame)
+S3method(factor_analysis,matrix)
+S3method(factor_scores,fa)
+S3method(factor_scores,omega)
+S3method(factor_scores,parameters_efa)
+S3method(factor_scores,parameters_omega)
 S3method(format,compare_parameters)
 S3method(format,equivalence_test_lm)
 S3method(format,p_calibrate)
@@ -250,6 +259,7 @@ S3method(model_parameters,hkmeans)
 S3method(model_parameters,htest)
 S3method(model_parameters,hurdle)
 S3method(model_parameters,hypotheses)
+S3method(model_parameters,item_omega)
 S3method(model_parameters,ivFixed)
 S3method(model_parameters,ivprobit)
 S3method(model_parameters,kmeans)
@@ -603,9 +613,14 @@ S3method(print,parameters_sem)
 S3method(print,parameters_simulate)
 S3method(print,parameters_standardized)
 S3method(print_html,compare_parameters)
+S3method(print_html,equivalence_test_lm)
 S3method(print_html,parameters_brms_meta)
+S3method(print_html,parameters_efa)
+S3method(print_html,parameters_efa_summary)
 S3method(print_html,parameters_model)
 S3method(print_html,parameters_p_function)
+S3method(print_html,parameters_pca)
+S3method(print_html,parameters_pca_summary)
 S3method(print_html,parameters_sem)
 S3method(print_html,parameters_simulate)
 S3method(print_html,parameters_standardized)
@@ -615,6 +630,8 @@ S3method(print_md,parameters_brms_meta)
 S3method(print_md,parameters_efa)
 S3method(print_md,parameters_efa_summary)
 S3method(print_md,parameters_model)
+S3method(print_md,parameters_omega)
+S3method(print_md,parameters_omega_summary)
 S3method(print_md,parameters_p_function)
 S3method(print_md,parameters_pca)
 S3method(print_md,parameters_pca_summary)
@@ -935,6 +952,7 @@ export(dominance_analysis)
 export(efa_to_cfa)
 export(equivalence_test)
 export(factor_analysis)
+export(factor_scores)
 export(format_df_adjust)
 export(format_order)
 export(format_p_adjust)
@@ -966,7 +984,6 @@ export(pool_parameters)
 export(principal_components)
 export(print_html)
 export(print_md)
-export(print_table)
 export(random_parameters)
 export(reduce_data)
 export(reduce_parameters)

NEWS.md

@@ -1,3 +1,66 @@
+# parameters (devel)
+
+## Breaking Changes
+
+* The experimental `print_table()` function was removed. The aim of this function
+  was to test the implementation of the `tinytable` backend for printing. Now,
+  `tinytable` is fully supported by `insight::export_table()` and thereby also
+  by the various `print()` resp. `display()` methods for model parameters.
+
+## Changes
+
+* All `print_html()` methods get an `engine` argument, to either use the `gt`
+  package or the `tinytable` package for printing HTML tables. Since `tinytable`
+  not only produces HTML tables, but rather different formats depending on the
+  environment, `print_html()` may also generate a markdown table. Thus, the
+  generic `display()` method can be used, too, which has a `format` argument that
+  also supports `"tt"` for `tinytable`.
+
+## Bug fixes
+
+* Fixed issue with models of class `selection` with multiple outcomes.
+
+# parameters 0.27.0
+
+## Breaking Changes
+
+* The `standardize` argument in `factor_analysis()` now defaults to `FALSE`.
+
+* The `rotation` argument in `factor_analysis()` now defaults to `"oblimin"`,
+  because the former default of `"none"` rarely makes sense in the context of
+  factor analysis. If you want to use no rotation, please set `rotation =
+  "none"`.
+
+* The `cor` argument in `n_factors()` was renamed into `correlation_matrix`. In
+  `factor_analysis()`, the `cor` argument was completely removed to avoid naming
+  collision with the `cor` argument of `psych::fa()`, which now users can pass
+  the `cor` argument to `psych::fa()` when using `factor_analysis()`.
+
+## Changes
+
+* `factor_analysis()` gets a `.matrix` method, including a new argument `n_obs`
+  (which can be a single value or a matrix of pairwise counts), to compute
+  factor analysis for a correlation matrix or covariance matrix.
+
+* New function `factor_scores()` to extract factor scores from EFA (`psych::fa()`
+  or `factor_analysis()`).
+
+* Added and/or improved print-methods for all functions around PCA, FA and Omega.
+
+* Improved efficiency in `model_parameters()` for models from packages *brms*
+  and *rstanarm*.
+
+* `p_adjust` for `model_parameters()` gets a new options, `"sup-t"`, to calculate
+  simultaneous confidence intervals.
+
+## Bug fixes
+
+* `bootstrap_model()` did not work for intercept-only models. This has been fixed.
+
+* Fixed issue with printing labels as pretty names for models from package
+  *pscl*, i.e. `print(model_parameters(model), pretty_names = "labels")` now
+  works as expected.
+
 # parameters 0.26.0
 
 ## Changes

R/1_model_parameters.R

@@ -358,12 +358,16 @@
 #'
 #'   - Lakens, D., Scheel, A. M., and Isager, P. M. (2018). Equivalence Testing
 #'     for Psychological Research: A Tutorial. Advances in Methods and Practices
-#'     in Psychological Science, 1(2), 259–269. \doi{10.1177/2515245918770963}
+#'     in Psychological Science, 1(2), 259–269.
 #'
 #'   - Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., and Lüdecke, D. (2019).
 #'     Indices of Effect Existence and Significance in the Bayesian Framework.
 #'     Frontiers in Psychology, 10, 2767. \doi{10.3389/fpsyg.2019.02767}
 #'
+#'   - Montiel Olea, J. L., and Plagborg-Møller, M. (2019). Simultaneous
+#'     confidence bands: Theory, implementation, and an application to SVARs.
+#'     Journal of Applied Econometrics, 34(1), 1–17. \doi{10.1002/jae.2656}
+#'
 #'   - Neter, J., Wasserman, W., and Kutner, M. H. (1989). Applied linear
 #'     regression models.
 #'
@@ -463,11 +467,12 @@ parameters <- model_parameters
 #'   coefficient. The transformed confidence interval more clearly captures this
 #'   uncertainty. For `compare_parameters()`, `exponentiate = "nongaussian"`
 #'   will only exponentiate coefficients from non-Gaussian families.
-#' @param p_adjust Character vector, if not `NULL`, indicates the method to
-#'   adjust p-values. See [`stats::p.adjust()`] for details. Further
-#'   possible adjustment methods are `"tukey"`, `"scheffe"`,
-#'   `"sidak"` and `"none"` to explicitly disable adjustment for
-#'   `emmGrid` objects (from **emmeans**).
+#' @param p_adjust String value, if not `NULL`, indicates the method to adjust
+#'   p-values. See [`stats::p.adjust()`] for details. Further possible
+#'   adjustment methods are `"tukey"`, `"scheffe"`, `"sidak"`, `"sup-t"`, and
+#'   `"none"` to explicitly disable adjustment for `emmGrid` objects (from
+#'   **emmeans**). `"sup-t"` computes simultaneous confidence bands, also called
+#'   sup-t confidence band (Montiel Olea & Plagborg-Møller, 2019).
 #' @param ci_method Method for computing degrees of freedom for
 #'   confidence intervals (CI) and the related p-values. Allowed are following
 #'   options (which vary depending on the model class): `"residual"`,

R/bootstrap_model.R

@@ -82,7 +82,7 @@ bootstrap_model.default <- function(model,
   model_response <- insight::find_response(model)
 
   boot_function <- function(model, data, indices) {
-    d <- data[indices, ] # allows boot to select sample
+    d <- data[indices, , drop = FALSE] # allows boot to select sample
 
     if (inherits(model, "biglm")) {
       fit <- suppressMessages(stats::update(model, moredata = d))
@@ -237,7 +237,7 @@ bootstrap_model.nestedLogit <- function(model,
   model_response <- insight::find_response(model)
 
   boot_function <- function(model, data, indices) {
-    d <- data[indices, ] # allows boot to select sample
+    d <- data[indices, , drop = FALSE] # allows boot to select sample
 
     if (verbose) {
       fit <- stats::update(model, data = d)