prep for CRAN 0.8.7

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: effectsize
 Title: Indices of Effect Size
-Version: 0.8.6.6
+Version: 0.8.7
 Authors@R: 
     c(person(given = "Mattan S.",
              family = "Ben-Shachar",
@@ -72,9 +72,9 @@ Depends:
     R (>= 3.6)
 Imports:
     bayestestR (>= 0.13.2),
-    insight (>= 0.19.8),
-    parameters (>= 0.21.5),
-    performance (>= 0.10.9),
+    insight (>= 0.19.9),
+    parameters (>= 0.21.6),
+    performance (>= 0.11.0),
     datawizard (>= 0.9.1),
     stats,
     utils
@@ -105,7 +105,7 @@ VignetteBuilder:
 Encoding: UTF-8
 Language: en-US
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3.9000
+RoxygenNote: 7.3.1
 Config/testthat/edition: 3
 Config/testthat/parallel: true
 Config/Needs/website:

NEWS.md

@@ -1,25 +1,19 @@
-# effectsize 0.8.7.xxx
+# effectsize 0.8.7
 
 - This release changes the licensing model of `{effectsize}` to an MIT license.
 
 ## New features
 
-- `cohens_d()` and `glass_delta()` gain an `adjust` argument for applying Hedges' small-sample bias correction (`hedges_g()` is now an alias for `cohens_d(adjust = TRUE)`).
-
+- `cohens_d()` and `glass_delta()` gain an `adjust` argument for applying Hedges' small-sample bias correction (`hedges_g()` is now an alias for `cohens_d(adjust = TRUE)`).  
 - `repeated_measures_d()` to compute standardized mean differences (SMD) for repeated measures data.
-  - Also supported in `effectsize(<t.test(paired = TRUE)>)`
+  - Also supported in `effectsize(<t.test(paired = TRUE)>)`  
+- New function: `interpret_fei()`
 
 ## Bug fixes
 
 - Minor stability fix to ncp-based CI methods ( #628 )
 - `nnt()` now properly accepts the `y` argument.
 
-# effectsize 0.8.7
-
-## New features
-
-- New function: `interpret_fei()`
-
 # effectsize 0.8.6
 
 This is a minor update to bring `effectsize` in-line with the formula methods 

R/utils_validate_input_data.R

@@ -12,7 +12,7 @@
     if (length(x) != 3L) {
       insight::format_error(
         "Formula must have one of the following forms:",
-        "          y ~ group   (independant samples)",
+        "          y ~ group   (independent samples)",
         "          y ~ 1       (one sample)",
         "  Pair(x,y) ~ 1       (paired samples)"
       )
@@ -102,7 +102,7 @@
 }
 
 #' @keywords internal
 .get_data_paired <- function(x, y = NULL, data = NULL, method,
                              verbose = TRUE, ...) {
   if (inherits(x, "formula")) {
     formula_error <-
@@ -323,7 +323,7 @@
                                    verbose = TRUE, ...) {
   if (inherits(x, "formula")) {
     if (length(x) != 3L || length(x[[3]]) != 1L) {
       insight::format_error("Formula must have the form of 'DV1 + ... + DVk ~ group', with exactly one term on the RHS.")
     }
 
     data <- .resolve_formula(stats::reformulate(as.character(x)[3:2]), data, ...)

cran-comments.md

@@ -6,7 +6,7 @@
 
 ## Test environments
 
-* local installation: R 4.3.1 on Windows
+* local installation: R 4.3.2 on Windows
 * GitHub Actions
     - Windows:        release
     - macOS:          release

inst/WORDLIST

@@ -49,7 +49,6 @@ Falk
 Faraggi
 Fei
 Fo
-Fum
 Funder
 GFI
 Gelman
@@ -82,8 +81,6 @@ Kiesser
 Koo
 Kruschke
 LMM
-Labelled
-labelled
 Lajeunesse
 Lakens
 Landis
@@ -126,6 +123,7 @@ Penfield
 PloS
 Psychometrika
 Psychonomic
+Pustejovsky
 RFI
 RMR
 RMSEA
@@ -137,17 +135,21 @@ Ringle
 Rosenthal
 Rothstein
 Rouder
+Rouder's
 Routledge
 Ruscio
 SDs
 SEM
+SMD
 SRMR
 Sarstedt
 Sawilowsky
 Schumacker
 Schweder
 Shachar
+Shadish
 Shmekels
+Speckman
 Steiger
 Szodorai
 Szumilas
@@ -172,6 +174,7 @@ anova
 arXiv
 arcsin
 biserial
+blogspot
 bmwiernik
 brglm
 brms
@@ -189,21 +192,24 @@ doi
 dynamicfit
 easystats
 effectSize
+effectsize’
 et
 fixest
 frac
 friedman
 ggstatsplot
-github
 glmmadmb
 gtsummary
 homoscedasticity
+http
 https
+independant
 infty
 intraclass
-io
+jeffrouder
 joss
 kruskal
+labelled
 lifecycle
 lm
 mattansb
@@ -233,7 +239,6 @@ rescale
 rmANOVA
 sd
 semipartial
-shinyapps
 statswiki
 strengejacke
 th

vignettes/statistical_power.Rmd

@@ -79,7 +79,7 @@
 The following section walks through a simple applied example to ensure 1) the concepts surrounding and involved in power are clear and digestible, and 2) that the role and value of the `effectsize` package are likewise clear and digestible.
 Understanding both of these realities will allow for more complex extensions and applications to a wide array of research problems and questions. 
 
-# Example: Comparing Means of Independant Samples
+# Example: Comparing Means of Independent Samples
 
 In addition to relying on the _easystats_ `effectsize` package for effect size calculation, we will also leverage the simple, but excellent `pwr` package for the following implementation of power analysis [@champley2017]. 
 
@@ -164,7 +164,7 @@
 There are many conversions available in the package. 
 Take a look [here](https://easystats.github.io/effectsize/reference/index.html#effect-size-conversion).
 
-This can also be dne directly by the user using the `t_to_d()` function:
+This can also be done directly by the user using the `t_to_d()` function:
 
 ```{r}
 t_to_d(
@@ -180,8 +180,8 @@
 For the present application, the effect size obtained from `cohens_d()` (or any of the three approaches previously described) can be passed to the `d` argument.
 
 ```{r}
 (result <- cohens_d(mpg ~ am, data = mtcars))
 (Ns <- table(mtcars$am))
 
 pwr.t2n.test(
   n1 = Ns[1], n2 = Ns[2],