CRAN 0.8.3 (#558)

* version bump

* .eval_if_requireNamespace

* Update from_test_statistics.Rmd

* Update cran-comments.md

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: effectsize
 Title: Indices of Effect Size
-Version: 0.8.2.10
+Version: 0.8.3
 Authors@R: 
  c(person(given = "Mattan S.",
  family = "Ben-Shachar",
@@ -67,15 +67,15 @@ Depends:
  R (>= 3.6)
 Imports:
  bayestestR (>= 0.13.0),
- insight (>= 0.18.4),
- parameters (>= 0.19.0),
- performance (>= 0.10.0),
- datawizard (>= 0.6.2),
+ insight (>= 0.18.8),
+ parameters (>= 0.20.2),
+ performance (>= 0.10.2),
+ datawizard (>= 0.6.5),
  stats,
  utils
 Suggests:
- correlation (>= 0.8.2),
- see (>= 0.7.3),
+ correlation (>= 0.8.3),
+ see (>= 0.7.4),
  afex,
  BayesFactor,
  boot,

NEWS.md

@@ -1,4 +1,4 @@
-# effectsize 0.8.2.xxx
+# effectsize 0.8.3
 
 ## Changes
 

cran-comments.md

@@ -1,13 +1,16 @@
-All URL issues have been resolved.
-DOI issues are a false positive.
+## Known issues
+
+- Failed handshake with *shinyapps.io* is a false positive.
+- Unavailable DOI link are false positives.
+- Spelling mistakes are false positives.
 
 ## Test environments
 
-* local installation: R 4.2.1 on Windows
+* local installation: R 4.2.2 on Windows
 * GitHub Actions
- - Windows: devel, release, oldrel
- - macOS: devel, release, oldrel
- - ubuntu-18.04: devel, release, oldrel, 4.0, 3.6
+ - Windows: release
+ - macOS: release
+ - ubuntu-18.04: release, oldrel, 4.0, 3.6
 * win-builder: release
 
 
@@ -16,16 +19,9 @@ DOI issues are a false positive.
 0 errors | 0 warnings | 0 notes
 
 
-### Known issues
-
-- Failed handshake with *shinyapps.io* is a false positive.
-- Unavailable DOI link are false positives.
-- Spelling mistakes are false positives.
-
-
 ## revdepcheck results
 
-We checked 18 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
+We checked 19 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
 
  * We saw 0 new problems
  * We failed to check 0 packages

vignettes/anovaES.Rmd

@@ -23,8 +23,14 @@ options(digits = 2)
 knitr::opts_chunk$set(comment = ">", warning = FALSE)
 
 set.seed(1)
-pkgs <- c("effectsize", "afex")
-knitr::opts_chunk$set(eval = all(sapply(pkgs, requireNamespace, quietly = TRUE)))
+
+
+.eval_if_requireNamespace <- function(...) {
+ pkgs <- c(...)
+ knitr::opts_chunk$get("eval") && all(sapply(pkgs, requireNamespace, quietly = TRUE))
+}
+
+knitr::opts_chunk$set(eval = .eval_if_requireNamespace("effectsize", "afex"))
 ```
 
 ## Eta<sup>2</sup>
@@ -112,7 +118,7 @@ is common to use "simultaneous" sums of squares (*type-II* or *type-III* tests),
 where each *SS* is computed controlling for all other predictors, regardless of
 order. This can be done with `car::Anova(type = ...)`:
 
-```{r, eval=requireNamespace("car", quietly = TRUE)}
+```{r, eval=.eval_if_requireNamespace("car")}
 eta_squared(car::Anova(m, type = 2), partial = FALSE)
 
 eta_squared(car::Anova(m, type = 3)) # partial = TRUE by default
@@ -138,7 +144,7 @@ for factors this can be done by using orthogonal coding (such as `contr.sum` for
 This unfortunately makes parameter interpretation harder, but *only* when this is does do the *SS*s associated with each lower-order term (or lower-order interaction) represent the ***SS*** of the **main effect** (with treatment coding they represent the *SS* of the simple effects).
 
 
-```{r, eval=requireNamespace("car", quietly = TRUE)}
+```{r, eval=.eval_if_requireNamespace("car")}
 # compare
 m_interaction1 <- lm(value ~ treatment * gender, data = obk.long)
 
@@ -233,12 +239,7 @@ always the case.
 
 For example, in linear mixed models (LMM/HLM/MLM), the estimation of all required *SS*s is not straightforward. However, we can still *approximate* these effect sizes (only their partial versions) based on the **test-statistic approximation method** (learn more in the [*Effect Size from Test Statistics* vignette](https://easystats.github.io/effectsize/articles/from_test_statistics.html)).
 
-```{r, echo = FALSE, eval=TRUE}
-lmm_pkgs <- c("lmerTest", "lme4")
-eval_lmm <- all(sapply(lmm_pkgs, requireNamespace, quietly = TRUE))
-```
-
-```{r, eval=eval_lmm}
+```{r, eval=.eval_if_requireNamespace("lmerTest", "lme4")}
 library(lmerTest)
 
 fit_lmm <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
@@ -251,7 +252,7 @@ F_to_eta2(45.8, df = 1, df_error = 17)
 Or directly with `eta_squared() and co.:
 
 
-```{r, eval=eval_lmm}
+```{r, eval=.eval_if_requireNamespace("lmerTest", "lme4")}
 eta_squared(fit_lmm)
 epsilon_squared(fit_lmm)
 omega_squared(fit_lmm)
@@ -277,14 +278,9 @@ model!** So it is vital to [validate the
 PPD](https://mc-stan.org/docs/2_23/stan-users-guide/meta-models-part.html#meta-models.part/)
 before using it to estimate explained variance measures.
 
-```{r, echo = FALSE, eval=TRUE}
-bayes_pkgs <- c("rstanarm", "bayestestR", "car")
-eval_bayes <- all(sapply(bayes_pkgs, requireNamespace, quietly = TRUE))
-```
-
 Let's fit our model:
 
-```{r, eval = eval_bayes}
+```{r, eval = .eval_if_requireNamespace("rstanarm", "bayestestR", "car")}
 library(rstanarm)
 
 m_bayes <- stan_glm(value ~ gender + phase + treatment,
@@ -297,7 +293,7 @@ We can use `eta_squared_posterior()` to get the posterior distribution of
 $eta^2$ or $eta^2_p$ for each effect. Like an ANOVA table, we must make sure to
 use the right effects-coding and *SS*-type:
 
-```{r, eval = eval_bayes}
+```{r, eval = .eval_if_requireNamespace("rstanarm", "bayestestR", "car")}
 pes_posterior <- eta_squared_posterior(m_bayes,
  draws = 500, # how many samples from the PPD?
  partial = TRUE, # partial eta squared
@@ -315,7 +311,7 @@ bayestestR::describe_posterior(pes_posterior,
 
 Compare to:
 
-```{r, eval = eval_bayes}
+```{r, eval = .eval_if_requireNamespace("rstanarm", "bayestestR", "car")}
 m_ML <- lm(value ~ gender + phase + treatment, data = obk.long)
 
 eta_squared(car::Anova(m_ML, type = 3))

vignettes/convert_r_d_OR.Rmd

@@ -20,6 +20,11 @@ library(knitr)
 options(knitr.kable.NA = "")
 knitr::opts_chunk$set(comment = ">")
 options(digits = 3)
+
+.eval_if_requireNamespace <- function(...) {
+ pkgs <- c(...)
+ knitr::opts_chunk$get("eval") && all(sapply(pkgs, requireNamespace, quietly = TRUE))
+}
 ```
 
 The `effectsize` package contains function to convert among indices of effect
@@ -48,7 +53,7 @@ cohens_d(salary ~ is_senior, data = hardlyworking)
 
 But we can also compute a point-biserial correlation, which is Pearson's *r* when treating the 2-level `is_senior` variable as a numeric binary variable:
 
-```{r, warning=FALSE, eval=requireNamespace("correlation", quietly = TRUE)}
+```{r, warning=FALSE, eval=.eval_if_requireNamespace("correlation")}
 correlation::cor_test(hardlyworking, "salary", "is_senior")
 ```
 
@@ -91,7 +96,7 @@ t_to_d(5.31, df_error = 497)[[1]]
 We can convert these semi-*d* values to *r* values, but in this case these
 represent the *partial* correlation:
 
-```{r, eval=requireNamespace("correlation", quietly = TRUE)}
+```{r, eval=.eval_if_requireNamespace("correlation")}
 t_to_r(5.31, df_error = 497)
 
 correlation::correlation(hardlyworking[, c("salary", "xtra_hours", "is_senior")],

vignettes/from_test_statistics.Rmd

@@ -24,6 +24,11 @@ options(digits = 2)
 options(knitr.kable.NA = "")
 
 set.seed(747)
+
+.eval_if_requireNamespace <- function(...) {
+ pkgs <- c(...)
+ knitr::opts_chunk$get("eval") && all(sapply(pkgs, requireNamespace, quietly = TRUE))
+}
 ```
 
 # Introduction
@@ -63,7 +68,7 @@ The conversion of the $F$- or $t$-statistic is based on
 
 Let's look at an example:
 
-```{r, eval = requireNamespace("afex", quietly = TRUE), message=FALSE}
+```{r, eval = .eval_if_requireNamespace("afex"), message=FALSE}
 library(afex)
 
 data(md_12.1)
@@ -105,7 +110,7 @@ For example:
 
 ### In Simple Effect and Contrast Analysis
 
-```{r, eval = requireNamespace("afex", quietly = TRUE) && requireNamespace("emmeans", quietly = TRUE)}
+```{r, eval = .eval_if_requireNamespace("afex", "emmeans")}
 library(emmeans)
 
 joint_tests(aov_fit, by = "noise")
@@ -119,7 +124,7 @@ F_to_eta2(
 
 We can also use `t_to_eta2()` for contrast analysis:
 
-```{r, eval = requireNamespace("afex", quietly = TRUE) && requireNamespace("emmeans", quietly = TRUE)}
+```{r, eval = .eval_if_requireNamespace("afex", "emmeans")}
 pairs(emmeans(aov_fit, ~angle))
 
 t_to_eta2(
@@ -130,7 +135,7 @@ t_to_eta2(
 
 ### In Linear Mixed Models
 
-```{r, eval = requireNamespace("lmerTest", quietly = TRUE), message=FALSE}
+```{r, eval = .eval_if_requireNamespace("lmerTest"), message=FALSE}
 library(lmerTest)
 
 fit_lmm <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
@@ -143,7 +148,7 @@ F_to_eta2(45.8, 1, 17)
 We can also use `t_to_eta2()` for the slope of `Days` (which in this case gives
 the same result).
 
-```{r, eval = requireNamespace("lmerTest", quietly = TRUE)}
+```{r, eval = .eval_if_requireNamespace("lmerTest")}
 parameters::model_parameters(fit_lmm, effects = "fixed", ci_method = "satterthwaite")
 
 t_to_eta2(6.77, df_error = 17)
@@ -171,7 +176,7 @@ better).
 
 ### For Slopes
 
-```{r, eval = requireNamespace("lmerTest", quietly = TRUE)}
+```{r, eval = .eval_if_requireNamespace("lmerTest")}
 parameters::model_parameters(fit_lmm, effects = "fixed", ci_method = "satterthwaite")
 
 t_to_r(6.77, df_error = 17)
@@ -193,7 +198,7 @@ t_to_r(
 
 to:
 
-```{r, eval=require(correlation, quietly = TRUE)}
+```{r, eval=.eval_if_requireNamespace("correlation")}
 correlation::correlation(attitude,
  select = "rating",
  select2 = c("complaints", "critical"),
@@ -206,7 +211,7 @@ correlation::correlation(attitude,
 This measure is also sometimes used in contrast analysis, where it is called the
 point bi-serial correlation - $r_{pb}$ [@cohen1965some; @rosnow2000contrasts]:
 
-```{r, eval = requireNamespace("afex", quietly = TRUE) && requireNamespace("emmeans", quietly = TRUE)}
+```{r, eval = .eval_if_requireNamespace("afex", "emmeans")}
 pairs(emmeans(aov_fit, ~angle))
 
 t_to_r(
@@ -226,7 +231,7 @@ These can be useful in contrast analyses.
 
 ### Between-Subject Contrasts
 
-```{r, eval = requireNamespace("emmeans", quietly = TRUE)}
+```{r, eval = .eval_if_requireNamespace("emmeans")}
 m <- lm(breaks ~ tension, data = warpbreaks)
 
 em_tension <- emmeans(m, ~tension)
@@ -242,13 +247,13 @@ However, these are merely approximations of a *true* Cohen's *d*. It is advised
 to directly estimate Cohen's *d*, whenever possible. For example, here with
 `emmeans::eff_size()`:
 
-```{r, eval = requireNamespace("emmeans", quietly = TRUE)}
+```{r, eval = .eval_if_requireNamespace("emmeans")}
 eff_size(em_tension, sigma = sigma(m), edf = df.residual(m))
 ```
 
 ### Within-Subject Contrasts
 
-```{r, eval = requireNamespace("afex", quietly = TRUE) && requireNamespace("emmeans", quietly = TRUE)}
+```{r, eval = .eval_if_requireNamespace("afex", "emmeans")}
 pairs(emmeans(aov_fit, ~angle))
 
 t_to_d(

vignettes/standardized_differences.Rmd

@@ -22,6 +22,11 @@ knitr::opts_chunk$set(comment = ">")
 options(digits = 3)
 
 set.seed(7)
+
+.eval_if_requireNamespace <- function(...) {
+ pkgs <- c(...)
+ knitr::opts_chunk$get("eval") && all(sapply(pkgs, requireNamespace, quietly = TRUE))
+}
 ```
 
 This vignette provides a review of effect sizes for comparisons of groups, which
@@ -115,7 +120,7 @@ hedges_g(extra ~ group, data = sleep, paired = TRUE)
 A Bayesian estimate of Cohen's *d* can also be provided based on `BayesFactor`'s
 version of a *t*-test via the `effectsize()` function:
 
-```{r, eval = requireNamespace("BayesFactor", quietly = TRUE), message=FALSE}
+```{r, eval = .eval_if_requireNamespace("BayesFactor"), message=FALSE}
 library(BayesFactor)
 BFt <- ttestBF(formula = mpg ~ am, data = mtcars)
 
@@ -310,7 +315,7 @@ A Bayesian estimate of (the parametric version of) these effect sizes can also
 be provided based on `BayesFactor`'s version of a *t*-test via the
 `effectsize()` function:
 
-```{r, eval = requireNamespace("BayesFactor", quietly = TRUE)}
+```{r, eval = .eval_if_requireNamespace("BayesFactor")}
 effectsize(BFt, type = "p_superiority")
 
 effectsize(BFt, type = "u1")

vignettes/xtabs.Rmd

@@ -22,6 +22,11 @@ knitr::opts_chunk$set(comment = ">")
 options(digits = 3)
 
 set.seed(7)
+
+.eval_if_requireNamespace <- function(...) {
+ pkgs <- c(...)
+ knitr::opts_chunk$get("eval") && all(sapply(pkgs, requireNamespace, quietly = TRUE))
+}
 ```
 
 This vignette provides a review of effect sizes for 1- and 2-D contingency
@@ -104,7 +109,7 @@ cohens_w(Music_preferences2) # > 1
 A Bayesian estimate of these effect sizes can also be provided based on
 `BayesFactor`'s version of a $\chi^2$-test via the `effectsize()` function:
 
-```{r, eval = requireNamespace("BayesFactor", quietly = TRUE), message=FALSE}
+```{r, eval = .eval_if_requireNamespace("BayesFactor"), message=FALSE}
 library(BayesFactor)
 BFX <- contingencyTableBF(MPG_Gear, sampleType = "jointMulti")
 
@@ -194,7 +199,7 @@ cohens_h(RCT_table)
 A Bayesian estimate of these effect sizes can also be provided based on
 `BayesFactor`'s version of a $\chi^2$-test via the `effectsize()` function:
 
-```{r, eval = requireNamespace("BayesFactor", quietly = TRUE)}
+```{r, eval = .eval_if_requireNamespace("BayesFactor")}
 BFX <- contingencyTableBF(RCT_table, sampleType = "jointMulti")
 
 effectsize(BFX, type = "or")