Prepare CRAN submission

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: bayestestR
 Title: Understand and Describe Bayesian Models and Posterior Distributions
-Version: 0.14.0.11
+Version: 0.15.0
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",
@@ -66,8 +66,8 @@ Description: Provides utilities to describe posterior
 Depends:
     R (>= 3.6)
 Imports:
-    insight (>= 0.20.4),
-    datawizard (>= 0.12.3),
+    insight (>= 0.20.5),
+    datawizard (>= 0.13.0),
     graphics,
     methods,
     stats,
@@ -81,14 +81,14 @@ Suggests:
     blavaan,
     bridgesampling,
     brms,
+    collapse,
     curl,
     effectsize,
     emmeans,
     gamm4,
     ggdist,
     ggplot2,
     glmmTMB,
-    httr,
     httr2,
     KernSmooth,
     knitr,
@@ -112,7 +112,8 @@ Suggests:
     rstanarm,
     see (>= 0.8.5),
     testthat,
-    tweedie
+    tweedie,
+    withr
 License: GPL-3
 URL: https://easystats.github.io/bayestestR/
 BugReports: https://github.com/easystats/bayestestR/issues
@@ -127,4 +128,3 @@ Config/testthat/parallel: true
 Config/rcmdcheck/ignore-inconsequential-notes: true
 Config/Needs/website: easystats/easystatstemplate
 Config/Needs/check: stan-dev/cmdstanr
-Remotes: easystats/insight, easystats/datawizard

NAMESPACE

@@ -112,7 +112,6 @@ S3method(ci,sim.merMod)
 S3method(ci,slopes)
 S3method(ci,stanfit)
 S3method(ci,stanreg)
-S3method(cwi,data.frame)
 S3method(describe_posterior,BFBayesFactor)
 S3method(describe_posterior,BGGM)
 S3method(describe_posterior,MCMCglmm)
@@ -642,7 +641,6 @@ export(contr.orthonorm)
 export(convert_bayesian_as_frequentist)
 export(convert_p_to_pd)
 export(convert_pd_to_p)
-export(cwi)
 export(density_at)
 export(describe_posterior)
 export(describe_prior)

NEWS.md

@@ -1,4 +1,4 @@
-# bayestestR (development version)
+# bayestestR 0.15.0
 
 ## Changes
 

R/bayesfactor.R

@@ -2,13 +2,13 @@
 #'
 #' This function compte the Bayes factors (BFs) that are appropriate to the
 #' input. For vectors or single models, it will compute [`BFs for single
-#' parameters()`][bayesfactor_parameters], or is `hypothesis` is specified,
-#' [`BFs for restricted models()`][bayesfactor_restricted]. For multiple models,
+#' parameters`][bayesfactor_parameters], or is `hypothesis` is specified,
+#' [`BFs for restricted models`][bayesfactor_restricted]. For multiple models,
 #' it will return the BF corresponding to [`comparison between
-#' models()`][bayesfactor_models] and if a model comparison is passed, it will
-#' compute the [`inclusion BF()`][bayesfactor_inclusion].
+#' models`][bayesfactor_models] and if a model comparison is passed, it will
+#' compute the [`inclusion BF`][bayesfactor_inclusion].
 #' \cr\cr
 #' For a complete overview of these functions, read the [Bayes factor vignette](https://easystats.github.io/bayestestR/articles/bayes_factors.html).
 #'
 #' @param ... A numeric vector, model object(s), or the output from
 #'   `bayesfactor_models`.
@@ -17,19 +17,20 @@
 #' @inheritParams bayesfactor_models
 #' @inheritParams bayesfactor_inclusion
 #'
-#' @return Some type of Bayes factor, depending on the input. See [bayesfactor_parameters()], [bayesfactor_models()] or [bayesfactor_inclusion()]
+#' @return Some type of Bayes factor, depending on the input. See
+#' [`bayesfactor_parameters()`], [`bayesfactor_models()`] or [`bayesfactor_inclusion()`].
 #'
 #' @note There is also a [`plot()`-method](https://easystats.github.io/see/articles/bayestestR.html) implemented in the \href{https://easystats.github.io/see/}{\pkg{see}-package}.
 #'
 #' @examplesIf require("rstanarm") && require("logspline")
+#' \dontrun{
 #' library(bayestestR)
 #'
 #' prior <- distribution_normal(1000, mean = 0, sd = 1)
-#' posterior <- distribution_normal(1000, mean = .5, sd = .3)
+#' posterior <- distribution_normal(1000, mean = 0.5, sd = 0.3)
 #'
 #' bayesfactor(posterior, prior = prior, verbose = FALSE)
 #'
-#' \donttest{
 #' # rstanarm models
 #' # ---------------
 #' model <- suppressWarnings(rstanarm::stan_lmer(extra ~ group + (1 | ID), data = sleep))

R/bic_to_bf.R

@@ -27,8 +27,8 @@ bic_to_bf <- function(bic, denominator, log = FALSE) {
   delta <- (denominator - bic) / 2
 
   if (log) {
-    return(delta)
+    delta
   } else {
-    return(exp(delta))
+    exp(delta)
   }
 }

R/describe_posterior.R

@@ -6,18 +6,16 @@
 #'   **bayestestR** supports a wide range of models (see `methods("describe_posterior")`)
 #'   and not all of those are documented in the 'Usage' section, because methods
 #'   for other classes mostly resemble the arguments of the `.numeric` method.
-#' @param ci_method The type of index used for Credible Interval. Can be
-#'   `"ETI"` (default, see [bayestestR::eti()]), `"HDI"`
-#'   (see [bayestestR::hdi()]), `"BCI"` (see
-#'   [bayestestR::bci()]), `"SPI"` (see [bayestestR::spi()]), or
-#'   `"SI"` (see [bayestestR::si()]).
+#' @param ci_method The type of index used for Credible Interval. Can be `"ETI"`
+#'   (default, see [`eti()`]), `"HDI"` (see [`hdi()`]), `"BCI"` (see [`bci()`]),
+#'   `"SPI"` (see [`spi()`]), or `"SI"` (see [`si()`]).
 #' @param test The indices of effect existence to compute. Character (vector) or
 #'   list with one or more of these options: `"p_direction"` (or `"pd"`),
 #'   `"rope"`, `"p_map"`, `"equivalence_test"` (or `"equitest"`),
-#'   `"bayesfactor"` (or `"bf"`) or `"all"` to compute all tests.
-#'   For each "test", the corresponding \pkg{bayestestR} function is called
-#'   (e.g. [bayestestR::rope()] or [bayestestR::p_direction()]) and its results
-#'   included in the summary output.
+#'   `"bayesfactor"` (or `"bf"`) or `"all"` to compute all tests. For each
+#'   "test", the corresponding \pkg{bayestestR} function is called (e.g.
+#'   [`rope()`] or [`p_direction()`]) and its results included in the summary
+#'   output.
 #' @param rope_range ROPE's lower and higher bounds. Should be a vector of two
 #'   values (e.g., `c(-0.1, 0.1)`), `"default"` or a list of numeric vectors of
 #'   the same length as numbers of parameters. If `"default"`, the bounds are
@@ -27,7 +25,7 @@
 #' @param keep_iterations If `TRUE`, will keep all iterations (draws) of
 #'   bootstrapped or Bayesian models. They will be added as additional columns
 #'   named `iter_1, iter_2, ...`. You can reshape them to a long format by
-#'   running [bayestestR::reshape_iterations()].
+#'   running [`reshape_iterations()`].
 #' @param bf_prior Distribution representing a prior for the computation of
 #'   Bayes factors / SI. Used if the input is a posterior, otherwise (in the
 #'   case of models) ignored.
@@ -48,7 +46,7 @@
 #' - 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 2019;10:2767. \doi{10.3389/fpsyg.2019.02767}
 #' - [Region of Practical Equivalence (ROPE)](https://easystats.github.io/bayestestR/articles/region_of_practical_equivalence.html)
 #' - [Bayes factors](https://easystats.github.io/bayestestR/articles/bayes_factors.html)
 #'
 #' @examples
@@ -122,7 +120,7 @@
 
 
 #' @keywords internal
 .describe_posterior <- function(x,
                                 centrality = "median",
                                 dispersion = FALSE,
                                 ci = 0.95,

R/describe_prior.R

@@ -63,7 +63,7 @@ describe_prior.brmsfit <- function(model,
 
   # If the prior scale has been adjusted, it is the actual scale that was used.
   if ("Prior_Adjusted_Scale" %in% names(priors)) {
-    priors$Prior_Scale[!is.na(priors$Prior_Adjusted_Scale)] <- priors$Prior_Adjusted_Scale[!is.na(priors$Prior_Adjusted_Scale)]
+    priors$Prior_Scale[!is.na(priors$Prior_Adjusted_Scale)] <- priors$Prior_Adjusted_Scale[!is.na(priors$Prior_Adjusted_Scale)] # nolint
     priors$Prior_Adjusted_Scale <- NULL
   }
 
@@ -85,7 +85,7 @@ describe_prior.brmsfit <- function(model,
     colnames(priors)[1] <- "Cleaned_Parameter"
     out <- merge(cp, priors, by = "Cleaned_Parameter", all = TRUE)
     out <- out[!duplicated(out$Parameter), ]
-    priors <- out[intersect(colnames(out), c("Parameter", "Prior_Distribution", "Prior_df", "Prior_Location", "Prior_Scale", "Response"))]
+    priors <- out[intersect(colnames(out), c("Parameter", "Prior_Distribution", "Prior_df", "Prior_Location", "Prior_Scale", "Response"))] # nolint
   }
 
   priors

R/diagnostic_draws.R

@@ -1,6 +1,6 @@
 #' Diagnostic values for each iteration
 #'
 #' Returns the accumulated log-posterior, the average Metropolis acceptance rate, divergent transitions, treedepth rather than terminated its evolution normally.
 #' @inheritParams diagnostic_posterior
 #'
 #' @examples
@@ -27,17 +27,26 @@
 diagnostic_draws.brmsfit <- function(posterior, ...) {
   insight::check_if_installed("brms")
 
-  data <- brms::nuts_params(posterior)
-  data$idvar <- paste0(data$Chain, "_", data$Iteration)
+  nuts_parameters <- brms::nuts_params(posterior)
+  nuts_parameters$idvar <- paste0(
+    nuts_parameters$Chain,
+    "_",
+    nuts_parameters$Iteration
+  )
   out <- stats::reshape(
-    data,
+    nuts_parameters,
     v.names = "Value",
     idvar = "idvar",
     timevar = "Parameter",
     direction = "wide"
   )
   out$idvar <- NULL
-  out <- merge(out, brms::log_posterior(posterior), by = c("Chain", "Iteration"), sort = FALSE)
+  out <- merge(
+    out,
+    brms::log_posterior(posterior),
+    by = c("Chain", "Iteration"),
+    sort = FALSE
+  )
 
   # Rename
   names(out)[names(out) == "Value.accept_stat__"] <- "Acceptance_Rate"

R/distribution.R

@@ -33,21 +33,21 @@
   )
 
   switch(match.arg(arg = type, choices = basr_r_distributions),
-    "beta" = distribution_beta(...),
-    "binom" = ,
-    "binomial" = distribution_binomial(...),
-    "cauchy" = distribution_cauchy(...),
-    "chisq" = ,
-    "chisquared" = distribution_chisquared(...),
-    "gamma" = distribution_gamma(...),
-    "gaussian" = ,
-    "normal" = distribution_normal(...),
-    "nbinom" = distribution_nbinom(...),
-    "poisson" = distribution_poisson(...),
-    "t" = ,
-    "student" = ,
-    "student_t" = distribution_student(...),
-    "uniform" = distribution_uniform(...),
+    beta = distribution_beta(...),
+    binom = ,
+    binomial = distribution_binomial(...),
+    cauchy = distribution_cauchy(...),
+    chisq = ,
+    chisquared = distribution_chisquared(...),
+    gamma = distribution_gamma(...),
+    gaussian = ,
+    normal = distribution_normal(...),
+    nbinom = distribution_nbinom(...),
+    poisson = distribution_poisson(...),
+    t = ,
+    student = ,
+    student_t = distribution_student(...),
+    uniform = distribution_uniform(...),
     distribution_custom(type = type, ...)
   )
 }
@@ -148,7 +148,7 @@
   n <- round(n / length(mean))
   sd <- sd
   if (length(sd) != length(mean)) {
-    sd <- rep(sd, length.out = length(mean))
+    sd <- rep_len(sd, length(mean))
   }
 
 
@@ -229,8 +229,8 @@
 #' @export
 distribution_tweedie <- function(n,
                                  xi = NULL,
                                  mu,
                                  phi,
                                  power = NULL,
                                  random = FALSE,
                                  ...) {

R/effective_sample.R

@@ -16,8 +16,10 @@
 #'   information there is in autocorrelated chains} (*Kruschke 2015, p182-3*).
 #'
 #' @references
-#'   - Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
-#'   - Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1-28
+#' - Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS,
+#'   and Stan. Academic Press.
+#' - Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models
+#'   using Stan. Journal of Statistical Software, 80(1), 1-28
 #'
 #' @examplesIf require("rstanarm")
 #' \donttest{
@@ -82,7 +84,7 @@ effective_sample.brmsfit <- function(model,
 #' @export
 effective_sample.stanreg <- function(model,
                                      effects = c("fixed", "random", "all"),
-                                     component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"),
+                                     component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"), # nolint
                                      parameters = NULL,
                                      ...) {
   # check arguments
@@ -112,7 +114,7 @@ effective_sample.stanreg <- function(model,
 #' @export
 effective_sample.stanmvreg <- function(model,
                                        effects = c("fixed", "random", "all"),
-                                       component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"),
+                                       component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"), # nolint
                                        parameters = NULL,
                                        ...) {
   # check arguments

R/equivalence_test.R

@@ -31,8 +31,8 @@
 #'   to zero the better).
 #'   \cr \cr
 #'   Some attention is required for finding suitable values for the ROPE limits
-#'   (argument `range`). See 'Details' in [`rope_range()`][rope_range]
-#'   for further information.
+#'   (argument `range`). See 'Details' in [`rope_range()`] for further
+#'   information.
 #'   \cr \cr
 #'   **Multicollinearity: Non-independent covariates**
 #'   \cr \cr
@@ -53,9 +53,9 @@
 #'
 #'
 #' @references
 #' - Kruschke, J. K. (2018). Rejecting or accepting parameter values in Bayesian estimation. Advances in Methods and Practices in Psychological Science, 1(2), 270-280. \doi{10.1177/2515245918771304}
 #' - Kruschke, J. K. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press
 #' - Piironen, J., & Vehtari, A. (2017). Comparison of Bayesian predictive methods for model selection. Statistics and Computing, 27(3), 711–735. \doi{10.1007/s11222-016-9649-y}
 #'
 #' @return A data frame with following columns:
 #'
@@ -172,7 +172,7 @@
     # check if list of values contains only valid values
     range <- .check_list_range(range, x)
     # apply thresholds to each column
     l <- insight::compact_list(mapply(
       function(p, r) {
         equivalence_test(
           p,

R/hdi.R

@@ -82,7 +82,8 @@
 #' @inherit ci return
 #'
 #' @family ci
-#' @seealso Other interval functions, such as [hdi()], [eti()], [bci()], [spi()], [si()], [cwi()].
+#' @seealso Other interval functions, such as [`hdi()`], [`eti()`], [`bci()`],
+#' [`spi()`], [`si()`].
 #'
 #' @examplesIf require("rstanarm") && require("brms") && require("emmeans") && require("BayesFactor")
 #' library(bayestestR)

R/model_to_priors.R

@@ -35,12 +35,12 @@ model_to_priors.brmsfit <- function(model, scale_multiply = 3, ...) {
 
   for (p in priors_params$Parameter) {
     if (p %in% params$Parameter) {
-      subset <- params[params$Parameter == p, ]
+      param_subset <- params[params$Parameter == p, ]
       priors$prior[priors_params$Parameter == p] <- paste0(
         "normal(",
-        insight::format_value(subset$Mean),
+        insight::format_value(param_subset$Mean),
         ", ",
-        insight::format_value(subset$SD * scale_multiply),
+        insight::format_value(param_subset$SD * scale_multiply),
         ")"
       )
     }