Merge branch 'code_style' of https://github.com/easystats/bayestestR …

…into code_style

R/sexit_thresholds.R

@@ -12,16 +12,18 @@
 #' sexit_thresholds(rnorm(1000))
 #' \dontrun{
 #' if (require("rstanarm")) {
-#'   model <- stan_glm(
+#'   model <- suppressWarnings(stan_glm(
 #'     mpg ~ wt + gear,
 #'     data = mtcars,
 #'     chains = 2,
 #'     iter = 200,
 #'     refresh = 0
-#'   )
+#'   ))
 #'   sexit_thresholds(model)
 #'
-#'   model <- stan_glm(vs ~ mpg, data = mtcars, family = "binomial", refresh = 0)
+#'   model <- suppressWarnings(
+#'     stan_glm(vs ~ mpg, data = mtcars, family = "binomial", refresh = 0)
+#'   )
 #'   sexit_thresholds(model)
 #' }
 #'

R/si.R

@@ -5,9 +5,9 @@
 #' updating factor greater or equal than *k*. From the perspective of the Savage-Dickey Bayes factor, testing
 #' against a point null hypothesis for any value within the support interval will yield a Bayes factor smaller
 #' than *1/k*.
-#' \cr \cr
-#' \strong{For more info, in particular on specifying correct priors for factors with more than 2 levels,
-#' see [the Bayes factors vignette](https://easystats.github.io/bayestestR/articles/bayes_factors.html).}
+#'
+#' **For more info, in particular on specifying correct priors for factors with more than 2 levels,
+#' see [the Bayes factors vignette](https://easystats.github.io/bayestestR/articles/bayes_factors.html).**
 #'
 #' @param BF The amount of support required to be included in the support interval.
 #' @inheritParams bayesfactor_parameters
@@ -20,48 +20,49 @@
 #' they should be *not flat*, and it is preferable that they be *informative* - note
 #' that by default, `brms::brm()` uses flat priors for fixed-effects; see example below).
 #'
-#' \subsection{Choosing a value of `BF`}{
-#' The choice of `BF` (the level of support) depends on what we want our interval to represent:
-#' \itemize{
-#'   \item A `BF` = 1 contains values whose credibility is not decreased by observing the data.
-#'   \item A `BF` > 1 contains values who received more impressive support from the data.
-#'   \item A `BF` < 1 contains values whose credibility has *not* been impressively decreased by observing the data.
-#'   Testing against values outside this interval will produce a Bayes factor larger than 1/`BF` in support of
-#'   the alternative. E.g., if an SI (BF = 1/3) excludes 0, the Bayes factor against the point-null will be larger than 3.
-#' }
-#' }
+#' @section Choosing a value of `BF`:
+#' The choice of `BF` (the level of support) depends on what we want our interval
+#' to represent:
+#'
+#' - A `BF` = 1 contains values whose credibility is not decreased by observing the data.
+#' - A `BF` > 1 contains values who received more impressive support from the data.
+#' - A `BF` < 1 contains values whose credibility has *not* been impressively
+#'   decreased by observing the data. Testing against values outside this interval
+#'   will produce a Bayes factor larger than 1/`BF` in support of the alternative.
+#'   E.g., if an SI (BF = 1/3) excludes 0, the Bayes factor against the point-null
+#'   will be larger than 3.
 #'
 #' @inheritSection bayesfactor_parameters Setting the correct `prior`
 #'
 #' @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}.
 #'
 #' @return
 #' A data frame containing the lower and upper bounds of the SI.
-#' \cr
+#'
 #' Note that if the level of requested support is higher than observed in the data, the
 #' interval will be `[NA,NA]`.
 #'
 #' @examplesIf requireNamespace("logspline", quietly = TRUE)
 #' 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)
 #'
-#' si(posterior, prior)
+#' si(posterior, prior, verbose = FALSE)
 #' \dontrun{
 #' # rstanarm models
 #' # ---------------
 #' library(rstanarm)
 #' contrasts(sleep$group) <- contr.equalprior_pairs # see vignette
 #' stan_model <- stan_lmer(extra ~ group + (1 | ID), data = sleep)
-#' si(stan_model)
-#' si(stan_model, BF = 3)
+#' si(stan_model, verbose = FALSE)
+#' si(stan_model, BF = 3, verbose = FALSE)
 #'
 #' # emmGrid objects
 #' # ---------------
 #' library(emmeans)
 #' group_diff <- pairs(emmeans(stan_model, ~group))
-#' si(group_diff, prior = stan_model)
+#' si(group_diff, prior = stan_model, verbose = FALSE)
 #'
 #' # brms models
 #' # -----------
@@ -76,7 +77,7 @@
 #'   prior = my_custom_priors,
 #'   refresh = 0
 #' ))
-#' si(brms_model)
+#' si(brms_model, verbose = FALSE)
 #' }
 #' @references
 #' Wagenmakers, E., Gronau, Q. F., Dablander, F., & Etz, A. (2018, November 22).

R/simulate_priors.R

@@ -5,14 +5,16 @@
 #' @inheritParams effective_sample
 #' @param n Size of the simulated prior distributions.
 #'
-#' @seealso [unupdate()] for directly sampling from the prior
+#' @seealso [`unupdate()`] for directly sampling from the prior
 #'   distribution (useful for complex priors and designs).
 #'
 #' @examples
 #' \dontrun{
 #' library(bayestestR)
 #' if (require("rstanarm")) {
-#'   model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
+#'   model <- suppressWarnings(
+#'     stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
+#'   )
 #'   simulate_prior(model)
 #' }
 #' }

R/simulate_simpson.R

@@ -4,10 +4,8 @@
 #' and statistics, in which a trend appears in several different groups of data
 #' but disappears or reverses when these groups are combined.
 #'
-#' @param n The number of observations for each group to be generated (minimum
-#'   4).
-#' @param groups Number of groups (groups can be participants, clusters,
-#'   anything).
+#' @param n The number of observations for each group to be generated (minimum 4).
+#' @param groups Number of groups (groups can be participants, clusters, anything).
 #' @param difference Difference between groups.
 #' @param group_prefix The prefix of the group name (e.g., "G_1", "G_2", "G_3", ...).
 #' @inheritParams simulate_correlation

R/spi.R

@@ -35,7 +35,9 @@
 #' spi(df, ci = c(0.80, 0.89, 0.95))
 #' \dontrun{
 #' library(rstanarm)
-#' model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
+#' model <- suppressWarnings(
+#'   stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
+#' )
 #' spi(model)
 #' }
 #'

R/unupdate.R

@@ -4,7 +4,7 @@
 #' the goal of this function is to un-update the posteriors to obtain models
 #' representing the priors. These models can then be used to examine the prior
 #' predictive distribution, or to compare priors with posteriors.
-#' \cr\cr
+#'
 #' This function in used internally to compute Bayes factors.
 #'
 #' @param model A fitted Bayesian model.

R/weighted_posteriors.R

@@ -22,12 +22,12 @@
 #' subtracting for continuous variables, and effects coding via `contr.sum` or
 #' orthonormal coding via [`contr.equalprior_pairs`] for factors) can reduce this
 #' issue. In any case you should be mindful of this issue.
-#' \cr\cr
+#'
 #' See [bayesfactor_models()] details for more info on passed models.
-#' \cr\cr
+#'
 #' Note that for `BayesFactor` models, posterior samples cannot be generated
 #' from intercept only models.
-#' \cr\cr
+#'
 #' This function is similar in function to `brms::posterior_average`.
 #'
 #' @note For `BayesFactor < 0.9.12-4.3`, in some instances there might be
@@ -36,24 +36,24 @@
 #'
 #' @return A data frame with posterior distributions (weighted across models) .
 #'
-#' @seealso [bayesfactor_inclusion()] for Bayesian model averaging.
+#' @seealso [`bayesfactor_inclusion()`] for Bayesian model averaging.
 #'
 #' @examples
 #' \donttest{
 #' if (require("rstanarm") && require("see")) {
-#'   stan_m0 <- stan_glm(extra ~ 1,
+#'   stan_m0 <- suppressWarnings(stan_glm(extra ~ 1,
 #'     data = sleep,
 #'     family = gaussian(),
 #'     refresh = 0,
 #'     diagnostic_file = file.path(tempdir(), "df0.csv")
-#'   )
+#'   ))
 #'
-#'   stan_m1 <- stan_glm(extra ~ group,
+#'   stan_m1 <- suppressWarnings(stan_glm(extra ~ group,
 #'     data = sleep,
 #'     family = gaussian(),
 #'     refresh = 0,
 #'     diagnostic_file = file.path(tempdir(), "df1.csv")
-#'   )
+#'   ))
 #'
 #'   res <- weighted_posteriors(stan_m0, stan_m1, verbose = FALSE)
 #'
@@ -73,21 +73,21 @@
 #'
 #' ## weighted prediction distributions via data.frames
 #' if (require("rstanarm")) {
-#'   m0 <- stan_glm(
+#'   m0 <- suppressWarnings(stan_glm(
 #'     mpg ~ 1,
 #'     data = mtcars,
 #'     family = gaussian(),
 #'     diagnostic_file = file.path(tempdir(), "df0.csv"),
 #'     refresh = 0
-#'   )
+#'   ))
 #'
-#'   m1 <- stan_glm(
+#'   m1 <- suppressWarnings(stan_glm(
 #'     mpg ~ carb,
 #'     data = mtcars,
 #'     family = gaussian(),
 #'     diagnostic_file = file.path(tempdir(), "df1.csv"),
 #'     refresh = 0
-#'   )
+#'   ))
 #'
 #'   # Predictions:
 #'   pred_m0 <- data.frame(posterior_predict(m0))