bootstrap type in parameters() (#1021)

* bootstrap type in parameters()
Fixes #536

* typos, wordlist

R/bootstrap_parameters.R

@@ -9,6 +9,8 @@
 #' or `"all"` to compute all tests. For each "test", the corresponding
 #' **bayestestR** function is called (e.g. [bayestestR::rope()] or
 #' [bayestestR::p_direction()]) and its results included in the summary output.
+#' @param ... Arguments passed to other methods, like [`bootstrap_model()`] or
+#' [`bayestestR::describe_posterior()`].
 #' @inheritParams bootstrap_model
 #' @inheritParams bayestestR::describe_posterior
 #'
@@ -40,6 +42,11 @@
 #' b <- bootstrap_parameters(model)
 #' print(b)
 #'
+#' # different type of bootstrapping
+#' set.seed(2)
+#' b <- bootstrap_parameters(model, type = "balanced")
+#' print(b)
+#'
 #' est <- emmeans::emmeans(b, trt.vs.ctrl ~ Species)
 #' print(model_parameters(est))
 #' }

R/p_function.R

@@ -139,8 +139,8 @@
 #' and Hjort confidence distribution school. However, if you view interval
 #' estimates as being intervals of values being consistent with the data,
 #' this comes close to the idea of (epistemic) probability. We do not believe that
-#' these two pardigms contradict or exclude each other. Rather, the aim is to
-#' emphasise one point of view or the other, i.e. to place the linguistic
+#' these two paradigms contradict or exclude each other. Rather, the aim is to
+#' emphasize one point of view or the other, i.e. to place the linguistic
 #' nuances either on 'compatibility' or 'probability'.
 #'
 #' The main take-away is *not* to interpret p-values as dichotomous decisions

inst/WORDLIST

@@ -23,13 +23,15 @@ Bundock
 CFA
 CMD
 CNG
+CRC
 Cattell
 Cattell's
 CrossValidated
 Curently
 D'Agostino
 DAS
 DBSCAN
+DG
 DOI
 DRR
 Davison
@@ -41,6 +43,7 @@ DoFs
 Dom
 Dorie
 Dupont
+DV
 EFA
 EGAnet
 ESS
@@ -80,8 +83,11 @@ Hornik
 Isager
 ICA
 IRR
+JB
+JM
 JRM
 Jurs
+KJ
 KMO
 Kenward
 Korner
@@ -136,6 +142,10 @@ Rocklin
 Rosseel
 Rousseeuw
 Routledge
+Rothman
+Scand
+Senn
+Statist
 SBC
 SDs
 SEM