Releases: easystats/effectsize
Releases · easystats/effectsize
CRAN 1.0.0
effectsize 1.0.0
First stable release of {effectsize}! 🥳
New features
oddsratio_to_d()and related functions gain ap0argument for exact conversion between odds ratios and Cohen's d (thanks @KohlRaphael for the suggestion).interpret*()now accept (and return) matrices and arrays.
Breaking Changes
interpret_oddsratio()drops the default"chen2010"as it was used incorrectly (thanks to @KohlRaphael).- Functions that have been deprecated since September 2022 have been removed.
CRAN 0.8.5
effectsize 0.8.5
New features
interpret_cfi()gains a new rule option:"hu&bentler1999"( #538 ).cohens_f()added option to return unbiased estimators (based on Omega- or Epsilon-squared).tschuprows_t()now returns an effect size corrected for small-sample bias. Setadjust = FALSEto preserve old behavior.w_to_v()and others for converting between effect sizes of Chi-square tests.arr()andnnt()for Absolute Risk Reduction or Number Needed to Treat.oddsratio_to_arr(),riskratio_to_arr(),nnt_to_arr()and their inverses.logoddsratio_to_*()and*_to_logoddsratio()have been added as convenient shortcuts foroddsratio_to_*(log = TRUE)and*_to_oddsratio(log = TRUE).- Added all missing functions to convert between (log) OR, RR, ARR, and NNT.
Changes
fei()gives a more informative error method for invalid table inputs (#566).convert_*()aliases are deprecated.
Breaking Changes
*_to_riskratio()andriskratio_to_*()argumentlognot longer converts RR to/from log(RR).interpret_gfi()and friends: some previously named"default"rules have been re-labelled as"byrne1994".
Bug fixes
CRAN 0.8.3
effectsize 0.8.3
Changes
mahalanobis_d()now defaults to one-sided CIs.
New features
means_ratio()for computing ratios of two means for ratio-scales outcomes (thanks to @arcaldwell49!)r_to_d()family of functions gain arguments for specifying group size ( #534 )r2_semipartialfor semi-partial squared correlations of model terms / parameters.
Bug fixes
- Fixed error in
cohens_w()for 2-by-X tables. - Solved integer overflow errors in
rank_biserial()( #476 )
CRAN 0.8.2
effectsize 0.8.2
Breaking Changes
omega_squared()andepsilon_squared()(andF_to_omega2()andF_to_epsilon2()) always return non-negative estimates (previously estimates were negative when the observed effect size is very small).rank_eta_squared()always returns a non-negative estimate (previously estimates were negative when the observed effect size is very small).
CRAN 0.8.1
effectsize 0.8.1
Changes
- cohens_w() has an exact upper bound when used as an effect size for goodness-of-fit.
Bug fixes
- When using formula input to effect size function,
na.actionarguments are respected (#517)
CRAN 0.8.0
effectsize 0.8.0
Breaking Changes
{effectsize}now requiresR >= 3.6fei(),cohens_w()andpearsons_c()always rescale thepinput 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 renamedfei().cles,d_to_clesandrb_to_clesare deprecated in favor of their respective effect size functions.
Changes
phi()andcramers_v()(andchisq_to_phi/cramers_v()) now apply the small sample bias correction by default. To restore previous behavior, setadjust = FALSE.
New features
- Set
options(es.use_symbols = TRUE)to print proper symbols instead of transliterated effect size names. (On Windows, requiresR >= 4.2.0) effectsize()supportsfisher.test().- New datasets used in examples and vignettes - see
data(package = "effectsize"). tschuprows_t()andchisq_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()andrb_to_wmw_oddsfor the Wilcoxon-Mann-Whitney odds (thanks @arcaldwell49! #479).p_superiority()now supports paired and one-sample cases.vd_a()andrb_to_vda()for Vargha and Delaney's A dominance effect size (aliases forp_superiority(parametric = FALSE)andrb_to_p_superiority()).cohens_u1(),cohens_u2(),d_to_u1(), andd_to_u2()added for Cohen's U1 and U2.
Bug fixes
- Common-language effect sizes now respects
muargument for all effect sizes. mad_pooled()not returns correct value (previously was inflated by a factor of 1.4826).pearsons_c()andchisq_to_pearsons_c()lose theadjustargument which applied an irrelevant adjustment to the effect size.- Effect sizes for goodness-of-fit now work when passing a
pthat is a table.
CRAN 0.7.0.5
v0.7.0: CRAN 0.7 (#447)
effectsize 0.7.0
Breaking Changes
standardize_parameters(),standardize_posteriors(), &standardize_info()have been moved to theparameterspackage.standardize()(for models) has been moved to thedatawizardpackage.phi()only works for 2x2 tables.cramers_v()only works for 2D tables.
New features
normalized_chi()gives an adjusted Cohen's w for goodness of fit.cohens_w()is now a fully-fledged function for x-tables and goodness-of-fit effect size (not just an alias forphi()).- Support for
insight'sdisplay,print_mdandprint_htmlfor all{effectsize}outputs.
Bug fixes
kendalls_w()now deals with ties.eta_squared()works withcar::Manova()that does not have an i-design.
CRAN release 0.6.0.1
CRAN release 0.6.0
effectsize 0.6.0
Breaking Changes
pearsons_c()effect size column name changed toPearsons_cfor consistency.
New features
New API
See Support functions for model extensions vignette.
Other features
eta_squared()family now supportsafex::mixed()models.cles()for estimating common language effect sizes.rb_to_cles()for converting rank-biserial correlation to Probability of superiority.
Changes
effectsize()forBayesFactorobjects returns the same standardized output as forhtest.
Bug fixes
eta_squared()for MLM return effect sizes in the correct order of the responses.eta_squared()family no longer fails when CIs fail due to non-finite Fs / degrees of freedom.standardize()for multivariate models standardizes the (multivariate) response.standardize()for models with offsets standardizes offset variables according toinclude_responseandtwo_sd( #396 ).eta_squared(): fixed a bug that causedafex_aovmodels with more than 2 within-subject factors to return incorrect effect sizes for the lower level factors ( #389 ).