CRAN release 0.4.0

Breaking Changes

• 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 NAs).
• Column name changes:
  • eta_squared() / F_to_eta2 families of function now has the Eta2 format, where previously was Eta_Sq.
  • cramers_v is now Cramers_v

New features

• 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 = ...).
• eta_squared(), omega_squared() and epsilon_squared() fully support aovlist, afex_aov and mlm (or maov) objects.
• standardize_parameters() can now return Odds ratios / IRRs (or any exponentiated parameter) by setting exponentiate = TRUE.
• Added cohens_f_squared() and F_to_f2() for Cohen's f-squared.
• cohens_f() / cohens_f_squared()can be used to estimate Cohen's f for the R-squared change between two models.
• standardize() and standardize_info() work with weighted models / data ( #82 ).
• Added hardlyworking (simulated) dataset, for use in examples.
• interpret_* ( #131 ):
  • interpret_omega_squared() added "cohen1992" rule.
  • interpret_p() added Redefine statistical significance rules.
• oddsratio_to_riskratio() for converting OR to RR.

Changes

• CIs for Omega-/Epsilon-squared and Adjusted Phi/Cramer's V return 0s instead of negative values.
• standardize() for data frames gains the remove_na argument for dealing with NAs ( #147 ).
• standardize() and standardize_info() now (and by extension, standardize_parameters()) respect the weights in weighted models when standardizing ( #82 ).
• Internal changes to standardize_parameters() (reducing co-dependency with parameters) - argument parameters has been dropped.

Bug fixes

• ranktransform(sign = TURE) correctly (doesn't) deal with zeros.
• effectsize() for htest works with Spearman and Kendall correlations ( #165 ).
• cramers_v() and phi() now work with goodness-of-fit data ( #158 )
• standardize_parameters() for post-hoc correctly standardizes transformed outcome.
• Setting two_sd = TRUE in standardize() and standardize_parameters() (correctly) on uses 2-SDs of the predictors (and not the response).
• standardize_info() / standardize_parameters(method = "posthoc") work for zero-inflated models ( #135 )
• 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.