Effect size measures and significance tests
r2() and icc(), also by adding more references.re_grp_var() to find group factors of random effects in mixed models.omega_sq() and eta_sq() give more informative messages when using non-supported objects.r2() and icc() give more informative warnings and messages.tidy_stan() supports printing simplex parameters of monotonic effects of brms models.grpmean() and mwu() get a file and encoding argument, to save the HTML output as file.model_frame() now correctly names the offset-columns for terms provided as offset-argument (i.e. for models where the offset was not specified inside the formula).weights-argument in grpmean() when variable name was passed as character vector.r2() for glmmTMB models with ar1 random effects structure.wtd_chisqtest() to compute a weighted Chi-squared test.wtd_median() to compute the weighted median of variables.wtd_cor() to compute weighted correlation coefficients of variables.mediation() can now cope with models from different families, e.g. if the moderator or outcome is binary, while the treatment-effect is continuous.model_frame(), link_inverse(), pred_vars(), resp_var(), resp_val(), r2() and model_family() now support clm2-objects from package ordinal.anova_stats() gives a more informative message for non-supported models or ANOVA-options.model_family() and link_inverse() for models fitted with pscl::hurdle() or pscl::zeroinfl().grpmean() for grouped data frames, when grouping variable was an unlabelled factor.model_frame() for coxph-models with polynomial or spline-terms.mediation() for logical variables.wtd_ttest() to compute a weighted t-test.wtd_mwu() to compute a weighted Mann-Whitney-U or Kruskal-Wallis test.robust() was revised, getting more arguments to specify different types of covariance-matrix estimation, and handling these more flexible.print()-method for tidy_stan() for brmsfit-objects with categorical-families.se() now also computes standard errors for relative frequencies (proportions) of a vector.r2() now also computes r-squared values for glmmTMB-models from genpois-families.r2() gives more precise warnings for non-supported model-families.xtab_statistics() gets a weights-argument, to compute measures of association for contingency tables for weighted data.statistics-argument in xtab_statistics() gets a "fisher"-option, to force Fisher's Exact Test to be used.icc() for generalized linear mixed models with Poisson or negative binomial families.icc() gets an adjusted-argument, to calculate the adjusted and conditional ICC for mixed models.weight.by is now deprecated and renamed into weights.grpmean() now also adjusts the n-columm for weighted data.icc(), re_var() and get_re_var() now correctly compute the random-effect-variances for models with multiple random slopes per random effect term (e.g., (1 + rs1 + rs2 | grp)).tidy_stan(), mcse(), hdi() and n_eff() for stan_polr()-models.equi_test() did not work for intercept-only models.hdi(), rope(), equi_test() etc. are now more generic, and function usage for each supported object is now included in the documentation.icc(), r2(), p_value(), se(), and std_beta().print()-methods for some more functions, for a clearer output.r2() for mixed models (packages lme4, glmmTMB). The r-squared value should be much more precise now, and reports the marginal and conditional r-squared values.stanmvreg-models are now supported by many functions.binned_resid() to plot binned residuals for logistic regression models.error_rate() to compute model quality for logistic regression models.auto_prior() to quickly create automatically adjusted priors for brms-models.difficulty() to compute the item difficulty.icc() gets a ppd-argument for Stan-models (brmsfit and stanreg), which performs a variance decomposition based on the posterior predictive distribution. This is the recommended way for non-Gaussian models.icc() now also computes the HDI for the ICC and random-effect variances. Use the prob-argument to specify the limits of this interval.link_inverse() and model_family() now support clmm-models (package ordinal) and glmRob and lmRob-models (package robust).model_family() gets a multi.resp-argument, to return a list of family-informations for multivariate-response models (of class brmsfit or stanmvreg).link_inverse() gets a multi.resp-argument, to return a list of link-inverse-functions for multivariate-response models (of class brmsfit or stanmvreg).p_value() now supports rlm-models (package MASS).check_assumptions() for single models with as.logical = FALSE now has a nice print-method.eta_sq() and omega_sq() now also work for repeated-measure Anovas, i.e. Anova with error term (requires broom > 0.4.5).model_frame() and var_names() now correctly cleans nested patterns like offset(log(x + 10)) from column names.model_frame() now returns proper column names from gamm4 models.model_frame() did not work when the model frame had spline-terms and weights.robust() when exponentiate = TRUE and conf.int = FALSE.reliab_test() returned an error when the provided data frame has less than three columns, instead of returning NULL.equi_test() to test if parameter values in Bayesian estimation should be accepted or rejected.mediation() to print a summary of a mediation analysis from multivariate response models fitted with brms.link_inverse() now also returns the link-inverse function for cumulative-family brms-models.model_family() now also returns an is_ordinal-element with information if the model is ordinal resp. a cumulative link model.model_family()) now better support vglm-models (package VGAM).r2() now also calculates the standard error for brms or stanreg models.r2() gets a loo-argument to calculate LOO-adjusted rsquared values for brms or stanreg models. This measure comes conceptionally closer to an adjusted r-squared measure.anova_stats(), eta_sq() etc.) are now also computed for mixed models.n_eff() now computes the number of effective samples, and no longer its ratio in relation to the total number of samples.tidy_stan() is now named neff_ratio, to avoid confusion.se() for icc()-objects, where random effect term could not be found.se() for merMod-objects.p_value() for mixed models with KR-approximation, which is now more accurate.cv_error() and cv_compare() to compute the root mean squared error for test and training data from cross-validation.props() to calculate proportions in a vector, supporting multiple logical statements.or_to_rr() to convert odds ratio estimates into risk ratio estimates.mn(), md() and sm() to calculate mean, median or sum of a vector, but using na.rm = TRUE as default.svyglm.nb-models: family(), print(), formula(), model.frame() and predict().mse().