pp_check()to compute posterior predictive checks for frequentist models.
model_performance.rma()now includes results from heterogeneity test for meta-analysis objects.
check_normality()now also works for mixed models (with the limitation that studentized residuals are used).
effects-argument for mixed models, to check random effects for normality.
model_performance()to meet changes in mlogit package.
by_groupargument, to compute ICCs per different group factors in mixed models with multiple levels or cross-classified design.
by_groupargument, to compute explained variance at different levels (following the variance-reduction approach by Hox 2010).
performance_lrt()now works on lavaan objects.
bayesfactorargument, to include or exclude the Bayes factor for model comparisons in the output.
performance_aic()for models from package survey, which returned three different AIC values. Now only the AIC value is returned.
check_collinearity()for glmmTMB models when zero-inflated formula only had one predictor.
check_model()for lme models.
check_distribution()for brmsfit models.
check_heteroscedasticity()for aov objects.
logLik.felm(), because this method is now implemented in the lfe package.
model_performance()now calculates AIC for Tweedie models.
rank-argument, to rank models according to their overall model performance.
compare_performance()has a nicer
compare_performance()was slightly adjusted.
model_performance()-methods for different objects now also have a
check_collinearity()now no longer returns backticks in row- and column names.
check_outliers()now also works on data frames.
performance_score()now also works on
check_singularity()now works with models of class clmm.
r2()now works with models of class clmm, bigglm and biglm.
check_overdispersion()for mixed models now checks that model family is Poisson.
r2_bayes()) was renamed from
SEto be in line with the naming convention of other easystats-packages.
compare_performance()now shows the Bayes factor when all compared models are fit from the same data. Previous behaviour was that the BF was shown when models were of same class.
model_performance()now also works for lavaan-objects.
method-argument to choose the method for detecting outliers. Furthermore, two new methods (Mahalanobis Distance and Invariant Coordinate Selection) were implemented.
check_model()now performs more checks for GLM(M)s and other model objects.
check-argument to plot selected checks only.
r2_nakagawa()now returns r-squared for models with singular fit, where no random effect variances could be computed. The r-squared then does not take random effect variances into account. This behaviour was changed to be in line with
MuMIn::r.squaredGLMM(), which returned a value for models with singular fit.
check_distribution()now detects negative binomial and zero-inflated distributions. Furthermore, attempt to improve accuracy.
check_distribution()now also accepts a numeric vector as input.
compare_performance()warns if models were not fit from same data.
check_homogeneity()to check models for homogeneity of variances.
check_collinearity()for zero-inflated models, where the zero-inflation component had not enough model terms to calculate multicollinearity.
performance_*()functions for models with binary outcome, when outcome variable was a factor.
performance_accuracy(), which calculates the predictive accuracy of linear or logistic regression models.
performance_logloss()to compute the log-loss of models with binary outcome. The log-loss is a proper scoring function comparable to the
performance_score()to compute the logarithmic, quadratic and spherical proper scoring rules.
performance_pcp()to calculate the percentage of correct predictions for models with binary outcome.
performance_roc(), to calculate ROC-curves.
performance_aicc(), to calculate the second-order AIC (AICc).
check_collinearity()to calculate the variance inflation factor and check model predictors for multicollinearity.
check_outliers()to check models for influential observations.
check_heteroscedasticity()to check models for (non-)constant error variance.
check_normality()to check models for (non-)normality of residuals.
check_autocorrelation()to check models for auto-correlated residuals.
check_distribution()to classify the distribution of a model-family using machine learning.
model_performance.brmsfit()now also return the WAIC (widely applicable information criterion).
r2_nakagawa()now calculates the full R2 for mixed models with zero-inflation.
NULLand no longer stops when no mixed model is provided.
compare_performance()now shows the Bayes factor when all compared models are of same class.
verbose-argument to show or suppress warnings.