eta_squared()family now indicate the type of sum-of-squares used.
rank_biserial()estimates CIs using the normal approximation (previously used bootstrapping).
hedges_g()now used exact bias correction (thanks to @mdelacre for the suggestion!)
glass_delta()now estimates CIs using the NCP method based on Algina et al (2006).
kendalls_w()now actually returns correct effect size. Previous estimates were incorrect, and based on transposing the groups and blocks.
effectsize now supports
R >= 3.4.
standardize_parameters()now supports bootstrapped estimates (from
unstandardize()which will reverse the effects of
interpret_kendalls_w()to interpret Kendall’s coefficient of concordance.
eta_squared()family of functions can now also return effect sizes for the intercept by setting
include_intercept = TRUE( #156 ).
riskratio()- order of groups has been changed (the first groups is now the treatment group, and the second group is the control group), so that effect sizes are given as treatment over control (treatment / control) (previously was reversed). This is done to be consistent with other functions in R and in
cohens_h() effect size for comparing two independent proportions.
keep_intercept argument to keep the intercept.
eta_squared() family of functions supports
Anova.mlm objects (from the
supports Cohen’s g for McNemar’s test.
Extracts OR from Fisher’s Exact Test in the 2x2 case.
cohens_d() family of functions gain
htest objects now tries first to extract the data used for testing, and computed the effect size directly on that data.
eta_squared() family of functions gains a
verbose argument more strictly respected.
glass_delta() returns CIs based on the bootstrap.
type = argument for specifying which effect size to return.
eta_squared_posterior() can return a generalized Eta squared.
eta_squared() family available for
standardize_parameters() for multi-component models (such as zero-inflated) now returns the unstandardized parameters in some cases where standardization is not possible (previously returned
Column name changes:
F_to_eta2 families of function now has the
Eta2 format, where previously was
cramers_v is now
effectsize() added support for
BayesFactor objects (Cohen’s d, Cramer’s v, and r).
cohens_g() effect size for paired contingency tables.
Generalized Eta Squared now available via
eta_squared(generalized = ...).
standardize_parameters() can now return Odds ratios / IRRs (or any exponentiated parameter) by setting
exponentiate = TRUE.
hardlyworking (simulated) dataset, for use in examples.
interpret_* ( #131 ):
oddsratio_to_riskratio() for converting OR to RR.
CIs for Omega-/Epsilon-squared and Adjusted Phi/Cramer’s V return 0s instead of negative values.
Internal changes to
standardize_parameters() (reducing co-dependency with
parameters) - argument
parameters has been dropped.
ranktransform(sign = TURE) correctly (doesn’t) deal with zeros.
standardize_parameters() for post-hoc correctly standardizes transformed outcome.
standardize_info(include_pseudo = TRUE) /
standardize_parameters(method = "pseudo") are less sensitive in detecting between-group variation of within-group variables.
interpret_oddsratio() correctly treats extremely small odds the same as treats extremely large ones.
standardize_parameters(method = "pseudo") returns pseudo-standardized coefficients for (G)LMM models.
d_to_common_language() for common language measures of standardized differences (a-la Cohen’s d).
Fix minor miss-calculation of Chi-squared for 2*2 table with small samples ( #102 ).
Fixed bug in
standardize() for standard objects with non-standard class-attributes (like vectors of class
New general purpose
Effectsize for differences have CI methods, and return a data frame.
Effectsize for ANOVA all have CI methods, and none are based on bootstrapping.
F_to_eta2() family of functions now support CIs (via the ncp method), and return a data frame.
standardize() for model-objects has a default-method, which usually accepts all models. Exception for model-objects that do not work will be added if missing.
suffix arguments, to add (instead of replace) standardized variables to the returned data frame.