easystats · DominiqueMakowski · Jun 18, 2019 · Jun 17, 2019 · Jun 18, 2019 · Jun 18, 2019
diff --git a/R/bayesfactor_inclusion.R b/R/bayesfactor_inclusion.R
@@ -35,7 +35,6 @@
 #'
 #' BFmodels <- bayesfactor_models(mo1, mo2, mo3, denominator = mo0)
 #' bayesfactor_inclusion(BFmodels)
-#'
 #' \dontrun{
 #' # BayesFactor
 #' # -------------------------------
@@ -71,7 +70,7 @@ bayesfactor_inclusion.bayesfactor_models <- function(models, match_models = FALS
 
   # Build Interaction Matrix #
   if (isTRUE(match_models)) {
-    effects.matrix <- as.matrix(df.model[,-c(1:3)])
+    effects.matrix <- as.matrix(df.model[, -c(1:3)])
 
     df.interaction <- data.frame(effnames, stringsAsFactors = FALSE)
 
@@ -80,7 +79,6 @@ bayesfactor_inclusion.bayesfactor_models <- function(models, match_models = FALS
     }
     rownames(df.interaction) <- effnames
     df.interaction <- as.matrix(df.interaction[, -1])
-
   }
 
   # Build Effect Table #
@@ -105,7 +103,7 @@ bayesfactor_inclusion.bayesfactor_models <- function(models, match_models = FALS
         has_not_high_order_interactions &
         !effects.matrix[, eff]
 
-      df.model_temp <- df.model[ind_include | ind_exclude, ,drop = FALSE]
+      df.model_temp <- df.model[ind_include | ind_exclude, , drop = FALSE]
     } else {
       df.model_temp <- df.model
     }
@@ -236,4 +234,4 @@ bayesfactor_inclusion.BFBayesFactor <- function(models, match_models = FALSE, pr
   }
 
   temp
-}
+}
diff --git a/R/bayesfactor_savagedickey.R b/R/bayesfactor_savagedickey.R
@@ -2,7 +2,8 @@
 #'
 #' This method computes the ratio between the density of a single value (typically the null)
 #' of two distributions. When the compared distributions are the posterior and the prior distributions,
-#' this results in an approximation of a Bayes factor against the (point) null model.
+#' this results is an approximation of a Bayes factor comparing the model against a model in which
+#' the parameter of choice is restricted to the point null.
 #' \cr \cr
 #' See also \href{https://easystats.github.io/bayestestR/articles/bayes_factors.html}{the Bayes factors vignette}.
 #'
@@ -58,7 +59,7 @@
 #' # emmGrid objects
 #' # ---------------
 #' library(emmeans)
-#' group_diff <- pairs(emmeans(stan_model, ~ group))
+#' group_diff <- pairs(emmeans(stan_model, ~group))
 #' bayesfactor_savagedickey(group_diff, prior = stan_model)
 #'
 #' # brms models
@@ -243,7 +244,7 @@ bayesfactor_savagedickey.data.frame <- function(posterior, prior = NULL,
 #' @keywords internal
 #' @importFrom insight print_color
 .bayesfactor_savagedickey <- function(posterior, prior, direction = 0, hypothesis = 0) {
-  if (isTRUE(all.equal(posterior,prior))) {
+  if (isTRUE(all.equal(posterior, prior))) {
     return(1)
   }
 
@@ -294,7 +295,7 @@ bayesfactor_savagedickey.data.frame <- function(posterior, prior = NULL,
   }
 
   String <- c("left", "right", "one-sided", "onesided", "two-sided", "twosided", "<", ">", "=", "-1", "0", "1", "+1")
-  Value <- c(-1, 1, 1, 1,  0, 0, -1, 1, 0, -1, 0, 1, 1)
+  Value <- c(-1, 1, 1, 1, 0, 0, -1, 1, 0, -1, 0, 1, 1)
 
   ind <- String == direction
   if (length(ind) == 0) {
@@ -329,8 +330,10 @@ bayesfactor_savagedickey.data.frame <- function(posterior, prior = NULL,
 
       x_range <- range(x)
       x_rangex <- stats::median(x) + 7 * stats::mad(x) * c(-1, 1)
-      x_range <- c(max(c(x_range[1],x_rangex[1])),
-                   min(c(x_range[2],x_rangex[2])))
+      x_range <- c(
+        max(c(x_range[1], x_rangex[1])),
+        min(c(x_range[2], x_rangex[2]))
+      )
 
       extension_scale <- diff(x_range) * extend_scale
       x_range[1] <- x_range[1] - extension_scale
@@ -396,7 +399,7 @@ bayesfactor_savagedickey.data.frame <- function(posterior, prior = NULL,
 
 
 #' @keywords internal
-.update_to_priors <- function(model, verbose = TRUE){
+.update_to_priors <- function(model, verbose = TRUE) {
   UseMethod(".update_to_priors")
 }
 
@@ -405,7 +408,7 @@ bayesfactor_savagedickey.data.frame <- function(posterior, prior = NULL,
 #' @keywords internal
 #' @importFrom stats update
 #' @importFrom utils capture.output
-.update_to_priors.stanreg <- function(model, verbose = TRUE){
+.update_to_priors.stanreg <- function(model, verbose = TRUE) {
   if (!requireNamespace("rstanarm")) {
     stop("Package \"rstanarm\" needed for this function to work. Please install it.")
   }
@@ -429,7 +432,7 @@ bayesfactor_savagedickey.data.frame <- function(posterior, prior = NULL,
 #' @importFrom stats update
 #' @importFrom utils capture.output
 #' @importFrom methods is
-.update_to_priors.brmsfit <- function(model, verbose = TRUE){
+.update_to_priors.brmsfit <- function(model, verbose = TRUE) {
   if (!requireNamespace("brms")) {
     stop("Package \"brms\" needed for this function to work. Please install it.")
   }
@@ -447,12 +450,13 @@ bayesfactor_savagedickey.data.frame <- function(posterior, prior = NULL,
   if (is(model_prior, "try-error")) {
     if (grepl("proper priors", model_prior)) {
       stop("Cannot compute BF for 'brmsfit' models fit with default priors.\n",
-           "See '?bayesfactor_savagedickey'",
-           call. = FALSE)
+        "See '?bayesfactor_savagedickey'",
+        call. = FALSE
+      )
     } else {
       stop(model_prior)
     }
   }
 
   model_prior
-}
+}
diff --git a/R/ci.R b/R/ci.R
@@ -58,8 +58,7 @@
 #' ci(model, ci = c(.80, .89, .95))
 #'
 #' library(emmeans)
-#' ci(emtrends(model, ~ 1, "wt"))
-#'
+#' ci(emtrends(model, ~1, "wt"))
 #' \dontrun{
 #' library(brms)
 #' model <- brms::brm(mpg ~ wt + cyl, data = mtcars)

diff --git a/R/convert_pd_to_p.R b/R/convert_pd_to_p.R
@@ -10,12 +10,11 @@
 #' @examples
 #' pd_to_p(pd = 95)
 #' pd_to_p(pd = 95, direction = "one-sided")
-#'
 #' @export
 pd_to_p <- function(pd, direction = "two-sided", ...) {
   direction <- .get_direction(direction)
-  p <- (1 - pd/100)
-  if(direction == 0){
+  p <- (1 - pd / 100)
+  if (direction == 0) {
     p <- 2 * p
   }
   p
@@ -26,10 +25,10 @@ pd_to_p <- function(pd, direction = "two-sided", ...) {
 #' @export
 p_to_pd <- function(p, direction = "two-sided", ...) {
   direction <- .get_direction(direction)
-  if(direction == 0){
+  if (direction == 0) {
     p <- p / 2
   }
-  (1-p)*100
+  (1 - p) * 100
 }
 
 

diff --git a/R/describe_posterior.R b/R/describe_posterior.R
@@ -58,7 +58,6 @@
 #' # -----------------------------------------------
 #' library(emmeans)
 #' describe_posterior(emtrends(model, ~1, "wt"))
-#'
 #' \dontrun{
 #' # brms models
 #' # -----------------------------------------------
@@ -396,4 +395,4 @@ describe_posterior.BFBayesFactor <- function(posteriors, centrality = "median",
     "rope", "equivalence", "equivalence_test", "equitest",
     "bf", "bayesfactor", "bayes_factor", "p_map", "all"
   ), several.ok = TRUE)
-}
+}
diff --git a/R/diagnostic_posterior.R b/R/diagnostic_posterior.R
@@ -6,7 +6,7 @@
 #' @param diagnostic Diagnostic metrics to compute.  Character (vector) or list with one or more of these options: \code{"ESS"}, \code{"Rhat"}, \code{"MCSE"} or \code{"all"}.
 #'
 #' @details
-#'   \strong{Effective Sample (ESS)} should be as large as possible, altough for most applications, an effective sample size greater than 1,000 is sufficient for stable estimates (Bürkner, 2017). The ESS corresponds to the number of independent samples with the same estimation power as the N autocorrelated samples. It is is a measure of \dQuote{how much independent information there is in autocorrelated chains} (\emph{Kruschke 2015, p182-3}).
+#'   \strong{Effective Sample (ESS)} should be as large as possible, altough for most applications, an effective sample size greater than 1000 is sufficient for stable estimates (Bürkner, 2017). The ESS corresponds to the number of independent samples with the same estimation power as the N autocorrelated samples. It is is a measure of \dQuote{how much independent information there is in autocorrelated chains} (\emph{Kruschke 2015, p182-3}).
 #'   \cr \cr
 #'   \strong{Rhat} should be the closest to 1. It should not be larger than 1.1 (Gelman and Rubin, 1992) or 1.01 (Vehtari et al., 2019). The split R-hat statistic quantifies the consistency of an ensemble of Markov chains.
 #'   \cr \cr

diff --git a/R/distribution.R b/R/distribution.R
@@ -144,13 +144,13 @@ distribution_custom <- function(n, type = "norm", ..., random = FALSE) {
 distribution_mixture_normal <- function(n, mean = c(-3, 3), sd = 1, random = FALSE, ...) {
   n <- round(n / length(mean))
   sd <- c(sd)
-  if(length(sd) != length(mean)){
+  if (length(sd) != length(mean)) {
     sd <- rep(sd, length.out = length(mean))
   }
 
 
   x <- c()
-  for(i in 1:length(mean)){
+  for (i in 1:length(mean)) {
     x <- c(x, distribution_normal(n = n, mean = mean[i], sd = sd[i], random = random))
   }
   x

diff --git a/R/estimate_density.R b/R/estimate_density.R
@@ -152,9 +152,11 @@ estimate_density.emmGrid <- function(x, method = "kernel", precision = 2^10, ext
   }
   x <- as.data.frame(as.matrix(emmeans::as.mcmc.emmGrid(x, names = FALSE)))
 
-  estimate_density(x, method = method, precision = precision,
-                              extend = extend, extend_scale = extend_scale,
-                              bw = bw, ...)
+  estimate_density(x,
+    method = method, precision = precision,
+    extend = extend, extend_scale = extend_scale,
+    bw = bw, ...
+  )
 }
 
 

diff --git a/R/hdi.R b/R/hdi.R
@@ -110,7 +110,7 @@ hdi.emmGrid <- function(x, ci = .89, verbose = TRUE, ...) {
     stop("Package \"emmeans\" needed for this function to work. Please install it.")
   }
   xdf <- as.data.frame(as.matrix(emmeans::as.mcmc.emmGrid(x, names = FALSE)))
-  out <- hdi(xdf , ci = ci, verbose = verbose, ...)
+  out <- hdi(xdf, ci = ci, verbose = verbose, ...)
   attr(out, "object_name") <- deparse(substitute(x), width.cutoff = 500)
   out
 }

diff --git a/R/p_rope.R b/R/p_rope.R
@@ -129,7 +129,7 @@ p_rope.emmGrid <- function(x, range = "default", precision = .1, ...) {
   }
   xdf <- as.data.frame(as.matrix(emmeans::as.mcmc.emmGrid(x, names = FALSE)))
 
-  out <- p_rope(xdf , range = range, precision = precision, ...)
+  out <- p_rope(xdf, range = range, precision = precision, ...)
   attr(out, "object_name") <- deparse(substitute(x), width.cutoff = 500)
   out
 }

diff --git a/R/print.bayesfactor_inclusion.R b/R/print.bayesfactor_inclusion.R
@@ -9,7 +9,7 @@ print.bayesfactor_inclusion <- function(x, digits = 2, log = FALSE, ...) {
 
   xBF <- BFE$BF
   BFE$BF <- as.character(round(xBF, digits = digits))
-  big_ind <- abs(xBF) >= 1000 | abs(xBF) < 1 / (10 ^ digits)
+  big_ind <- abs(xBF) >= 1000 | abs(xBF) < 1 / (10^digits)
   big_ind <- sapply(big_ind, isTRUE)
   if (isTRUE(any(big_ind))) {
     BFE$BF[big_ind] <- formatC(xBF, format = "e", digits = digits)[big_ind]

diff --git a/R/print.bayesfactor_models.R b/R/print.bayesfactor_models.R
@@ -10,7 +10,7 @@ print.bayesfactor_models <- function(x, digits = 2, log = FALSE, ...) {
   }
   xBF <- BFE$BF
   BFE$BF <- as.character(round(xBF, digits = digits))
-  big_ind <- abs(xBF) >= 1000 | abs(xBF) < 1 / (10 ^ digits)
+  big_ind <- abs(xBF) >= 1000 | abs(xBF) < 1 / (10^digits)
   big_ind <- sapply(big_ind, isTRUE)
   if (isTRUE(any(big_ind))) {
     BFE$BF[big_ind] <- formatC(xBF, format = "e", digits = digits)[big_ind]

diff --git a/R/print.bayesfactor_savagedickey.R b/R/print.bayesfactor_savagedickey.R
@@ -10,7 +10,7 @@ print.bayesfactor_savagedickey <- function(x, digits = 2, log = FALSE, ...) {
 
   xBF <- BFE$BF
   BFE$BF <- as.character(round(xBF, digits = digits))
-  big_ind <- abs(xBF) >= 1000 | abs(xBF) < 1 / (10 ^ digits)
+  big_ind <- abs(xBF) >= 1000 | abs(xBF) < 1 / (10^digits)
   big_ind <- sapply(big_ind, isTRUE)
   if (isTRUE(any(big_ind))) {
     BFE$BF[big_ind] <- formatC(xBF, format = "e", digits = digits)[big_ind]

diff --git a/R/update.bayesfactor_models.R b/R/update.bayesfactor_models.R
@@ -18,7 +18,6 @@
 #' m
 #'
 #' update(m, reference = "bottom")
-#'
 #' @export
 update.bayesfactor_models <- function(object, subset = NULL, reference = NULL, ...) {
   if (!is.null(reference)) {

diff --git a/README.Rmd b/README.Rmd
@@ -68,7 +68,7 @@ Click on the buttons above to access the package [**documentation**](https://eas
 
 #### Articles
 
-- [Credible Intervals (CIs)](https://easystats.github.io/bayestestR/articles/credible_interval.html)
+- [Credible Intervals (CI)](https://easystats.github.io/bayestestR/articles/credible_interval.html)
 - [Probability of Direction (pd)](https://easystats.github.io/bayestestR/articles/probability_of_direction.html)
 - [Region of Practical Equivalence (ROPE)](https://easystats.github.io/bayestestR/articles/region_of_practical_equivalence.html)
 - [Bayes Factors (BF)](https://easystats.github.io/bayestestR/articles/bayes_factors.html)
@@ -227,7 +227,7 @@ posterior %>%
 
 ### Bayes Factor
 
-[**`bayesfactor_savagedickey()`**](https://easystats.github.io/bayestestR/reference/bayesfactor_savagedickey.html) computes the ratio between the density of a single value (typically the null) in two distributions, typically the posterior vs. the prior distributions. This method is used to examine if the hypothesis value is less or more likely given the observed data.
+[**`bayesfactor_savagedickey()`**](https://easystats.github.io/bayestestR/reference/bayesfactor_savagedickey.html) computes  the ratio between the density of a single value (typically the null) of two distributions. When the compared distributions are the posterior and the prior distributions, this results is an approximation of a Bayes factor comparing the model against a model in which the parameter of choice is restricted to the point null.
 
 
 ```{r message=FALSE, warning=FALSE, results='hide'}

diff --git a/_pkgdown.yml b/_pkgdown.yml
@@ -32,13 +32,13 @@ navbar:
         href: articles/example3.html
   - text: Articles
     menu:
-      - text: 'Credible Intervals'
+      - text: 'Credible Intervals (CI)'
         href: articles/credible_interval.html
-      - text: 'Region of Practical Equivalence'
+      - text: 'Region of Practical Equivalence (ROPE)'
         href: articles/region_of_practical_equivalence.html
-      - text: 'Probability of Direction'
+      - text: 'Probability of Direction (pd)'
         href: articles/probability_of_direction.html
-      - text: 'Bayes Factors'
+      - text: 'Bayes Factors (BF)'
         href: articles/bayes_factors.html
       - text: 'Comparison of Point-Estimates'
         href: articles/indicesEstimationComparison.html

diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
diff --git a/docs/articles/bayes_factors.html b/docs/articles/bayes_factors.html