effectsize 0.8.0

Breaking Changes

{effectsize} now requires R >= 3.6
fei(), cohens_w() and pearsons_c() always rescale the p input to sum-to-1.
The order of some function arguments have been rearranged to be more consistent across functions:
(phi(), cramers_v(), p_superiority(), cohens_u3(), p_overlap(), rank_biserial(), cohens_f/_squared(), chisq_to_phi(), chisq_to_cramers_v(), F/t_to_f/2(), .es_aov_*()).
normalized_chi() has been renamed fei().
cles, d_to_cles and rb_to_cles are deprecated in favor of their respective effect size functions.

phi() and cramers_v() (and chisq_to_phi/cramers_v()) now apply the small sample bias correction by default. To restore previous behavior, set adjust = FALSE.

Set options(es.use_symbols = TRUE) to print proper symbols instead of transliterated effect size names. (On Windows, requires R >= 4.2.0)
effectsize() supports fisher.test().
New datasets used in examples and vignettes - see data(package = "effectsize").
tschuprows_t() and chisq_to_tschuprows_t() for computing Tschuprow's T - a relative of Cramer's V.
mahalanobis_d() for multivariate standardized differences.
Rank based effect sizes now accept ordered (ordered()) outcomes.
rank_eta_squared() for one-way rank ANOVA.
For Common Language Effect Sizes:
- wmw_odds() and rb_to_wmw_odds for the Wilcoxon-Mann-Whitney odds (thanks @arcaldwell49! #479).
- p_superiority() now supports paired and one-sample cases.
- vd_a() and rb_to_vda() for Vargha and Delaney's A dominance effect size (aliases for p_superiority(parametric = FALSE) and rb_to_p_superiority()).
- cohens_u1(), cohens_u2(), d_to_u1(), and d_to_u2() added for Cohen's U1 and U2.

Common-language effect sizes now respects mu argument for all effect sizes.
mad_pooled() not returns correct value (previously was inflated by a factor of 1.4826).
pearsons_c() and chisq_to_pearsons_c() lose the adjust argument which applied an irrelevant adjustment to the effect size.
Effect sizes for goodness-of-fit now work when passing a p that is a table.