minor

R/bayesfactor_models.R

@@ -220,11 +220,11 @@ bayesfactor_models.default <- function(..., denominator = 1, verbose = TRUE) {
       any(vapply(mods, insight::is_mixed_model, TRUE)) &&
       !isTRUE(attr(objects, "same_fixef")) &&
       verbose) {
-      insight::format_warning(
+      insight::format_warning(paste0(
         "Information criteria (like BIC) based on REML fits (i.e. `estimator=\"REML\"`)",
         "are not recommended for comparison between models with different fixed effects.",
         "Concider setting `estimator=\"ML\"`."
-      )
+      ))
     }
   } else if (verbose) {
     insight::format_alert("Unable to validate that all models were fit with the same data.")

R/rope.R

@@ -27,7 +27,7 @@
 #' underlining ROPE is to let the user define an area around the null value
 #' enclosing values that are *equivalent to the null* value for practical
 #' purposes (_Kruschke 2010, 2011, 2014_).
-#' 
+#'
 #' Kruschke (2018) suggests that such null value could be set, by default,
 #' to the -0.1 to 0.1 range of a standardized parameter (negligible effect
 #' size according to Cohen, 1988). This could be generalized: For instance,
@@ -39,7 +39,7 @@
 #' (or `89%`, considered more stable) [HDI][hdi] that falls within the
 #' ROPE as an index for "null-hypothesis" testing (as understood under the
 #' Bayesian framework, see [`equivalence_test()`][equivalence_test]).
-#' 
+#'
 #' @section Sensitivity to parameter's scale:
 #' It is important to consider the unit (i.e., the scale) of the predictors
 #' when using an index based on the ROPE, as the correct interpretation of the

R/simulate_priors.R

@@ -68,7 +68,7 @@ simulate_prior.brmsfit <- function(model,
     effects = effects,
     component = component,
     parameters = parameters,
-    verbose = verbose,
+    verbose = verbose
   )
 
   .simulate_prior(priors, n = n, verbose = verbose)

R/unupdate.R

@@ -117,8 +117,7 @@ unupdate.brmsfit_multiple <- function(model,
   if (methods::is(model_prior, "try-error")) {
     if (grepl("proper priors", model_prior, fixed = TRUE)) {
       insight::format_error(
-        "Cannot sample from flat priors (such as the default ",
-        "priors for fixed-effects in a 'brmsfit' model).",
+        "Cannot sample from flat priors (such as the default priors for fixed-effects in a 'brmsfit' model)."
       )
     } else {
       insight::format_error(model_prior)