Merge pull request #214 from easystats/dev

CRAN submission

Author: DominiqueMakowski

R/bayesfactor_inclusion.R

@@ -212,7 +212,7 @@ bayesfactor_inclusion.BFBayesFactor <- function(models, match_models = FALSE, pr
     tmp_terms <- make_terms(df.model$Modelnames[m])
     if (length(tmp_terms) > 0) {
       missing_terms <- !tmp_terms %in% colnames(df.model) # For R < 3.6.0
-      if (any(missing_terms)) df.model[,tmp_terms[missing_terms]] <- NA # For R < 3.6.0
+      if (any(missing_terms)) df.model[, tmp_terms[missing_terms]] <- NA # For R < 3.6.0
       df.model[m, tmp_terms] <- TRUE
     }
   }
@@ -240,4 +240,4 @@ bayesfactor_inclusion.BFBayesFactor <- function(models, match_models = FALSE, pr
   }
 
   temp
-}
+}

R/bayesfactor_parameters.R

@@ -115,7 +115,7 @@ bayesfactor_parameters <- function(posterior, prior = NULL, direction = "two-sid
 
 #' @rdname bayesfactor_parameters
 #' @export
-bayesfactor_savagedickey <- function(posterior, prior = NULL, direction = "two-sided", null = 0, verbose = TRUE, hypothesis = NULL,...) {
+bayesfactor_savagedickey <- function(posterior, prior = NULL, direction = "two-sided", null = 0, verbose = TRUE, hypothesis = NULL, ...) {
   .Deprecated("bayesfactor_parameters")
 
   dots <- list(...)

R/ci.R

@@ -32,14 +32,9 @@
 #' ci(df, method = "HDI", ci = c(.80, .89, .95))
 #'
 #' library(rstanarm)
-#' model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200)
+#' model <- stan_glm(mpg ~ wt, data = mtcars, chains = 2, iter = 200)
 #' ci(model, method = "ETI", ci = c(.80, .89))
 #' ci(model, method = "HDI", ci = c(.80, .89))
-#'
-#' library(emmeans)
-#' model <- emtrends(model, ~1, "wt")
-#' ci(model, method = "ETI")
-#' ci(model, method = "HDI")
 #' \dontrun{
 #' library(brms)
 #' model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
@@ -50,6 +45,11 @@
 #' bf <- ttestBF(x = rnorm(100, 1, 1))
 #' ci(bf, method = "ETI")
 #' ci(bf, method = "HDI")
+#'
+#' library(emmeans)
+#' model <- emtrends(model, ~1, "wt")
+#' ci(model, method = "ETI")
+#' ci(model, method = "HDI")
 #' }
 #'
 #' @export

R/estimate_density.R

@@ -215,4 +215,3 @@ density_at <- function(posterior, x, precision = 2^10, method = "kernel", ...) {
   density <- estimate_density(posterior, precision = precision, method = method, ...)
   stats::approx(density$x, density$y, xout = x)$y
 }
-

R/p_map.R

@@ -34,11 +34,13 @@
 #' # Robustness to density estimation method
 #' set.seed(333)
 #' data <- data.frame()
-#' for(iteration in 1:250){
+#' for (iteration in 1:250) {
 #'   x <- rnorm(1000, 1, 1)
-#'   result <- data.frame("Kernel" = p_map(x, method = "kernel"),
-#'                        "KernSmooth" = p_map(x, method = "KernSmooth"),
-#'                        "logspline" = p_map(x, method = "logspline"))
+#'   result <- data.frame(
+#'     "Kernel" = p_map(x, method = "kernel"),
+#'     "KernSmooth" = p_map(x, method = "KernSmooth"),
+#'     "logspline" = p_map(x, method = "logspline")
+#'   )
 #'   data <- rbind(data, result)
 #' }
 #' data$KernSmooth <- data$Kernel - data$KernSmooth

R/reshape_ci.R

@@ -18,17 +18,15 @@
 #' reshape_ci(reshape_ci(x))
 #' @importFrom stats reshape
 #' @export
-reshape_ci <- function(x){
+reshape_ci <- function(x) {
 
 
   # Long to wide ----------------
   if ("CI_low" %in% names(x) & "CI_high" %in% names(x) & "CI" %in% names(x)) {
-
     ci_position <- which(names(x) == "CI")
 
     # Reshape
     if (length(unique(x$CI)) > 1) {
-
       if (!"Parameter" %in% names(x)) {
         x$Parameter <- x$CI
         remove_parameter <- TRUE
@@ -56,8 +54,7 @@ reshape_ci <- function(x){
 
 
     # Wide to long --------------
-  } else{
-
+  } else {
     if (!"Parameter" %in% names(x)) {
       x$Parameter <- 1:nrow(x)
       remove_parameter <- TRUE

R/utils.R

@@ -83,16 +83,15 @@
 #' Used in describe_posterior
 #' @keywords internal
 .reoder_rows <- function(x, out, ci = NULL) {
-
-  if(!is.data.frame(out) || nrow(out) == 1){
+  if (!is.data.frame(out) || nrow(out) == 1) {
     return(out)
   }
 
-  if(is.null(ci)){
+  if (is.null(ci)) {
     refdata <- point_estimate(x, centrality = "median", dispersion = FALSE)
     order <- refdata$Parameter
     out <- out[match(order, out$Parameter), ]
-  } else{
+  } else {
     uncertainty <- ci(x, ci = ci)
     order <- paste0(uncertainty$Parameter, uncertainty$CI)
     out <- out[match(order, paste0(out$Parameter, out$CI)), ]

R/utils_hdi_ci.R

@@ -178,9 +178,3 @@
   class(dat) <- unique(c(paste0("bayestestR_", fun), paste0("see_", fun), class(dat)))
   dat
 }
-
-
-
-
-
-