sjstats: Statistical Functions for Regression Models.

General

• The S3-generics for functions like hdi(), rope(), equi_test() etc. are now more generic, and function usage for each supported object is now included in the documentation.
• Following functions are now S3-generic: icc(), r2(), p_value(), se(), and std_beta().
• Added print()-methods for some more functions, for a clearer output.
• Revised 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.
• Reduced package dependencies and removed apaTables and MBESS from suggested packages
• stanmvreg-models are now supported by many functions.

New 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.

Changes to functions

• 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.
• For Stan-models (brmsfit and stanreg), 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).

Bug fixes

• 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.
• Fix issue in 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.