performance 0.4.0

General

principal_components() was removed and re-implemented in the parameters-package. Please use parameters::principal_components() now.

check_outliers() now also works on data frames.
Added more methods to check_outliers().
performance_score() now also works on stan_lmer() and stan_glmer() objects.
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.

Fixed bug in compare_performance() that toggled a warning although models were fit from same data.
Fixed bug in check_model() for glmmTMB models that occured when checking for outliers.

Many check_*()-methods now get a plot()-method. Package see is required for plotting.
model_performance() gets a preliminary print()-method.

The attribute for the standard error of the Bayesian R2 (r2_bayes()) was renamed from std.error to SE to 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.
check_outliers() gets a 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_model() gets a 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.

Fixed issues with compare_performance() and row-ordering.
Fixed issue in check_collinearity() for zero-inlfated models, where the zero-inflation component had not enough model terms to calculate multicollinearity.
Fixed issue in some check_*() and performance_*() functions for models with binary outcome, when outcome variable was a factor.

r2() now works for more regression models.
r2_bayes() now works for multivariate response models.
model_performance() now works for more regression models, and also includes the log-loss, proper scoring rules and percentage of correct predictions as new metric for models with binary outcome.

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 rmse().
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.stanreg() and model_performance.brmsfit() now only return one R2-value and its standard error, instead of different (robust) R2 measures and credible intervals.
error_rate() is now integrated in the performance_pcp()-function.

model_performance.stanreg() and 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.
icc() now returns NULL and no longer stops when no mixed model is provided.
compare_performance() now shows the Bayes factor when all compared models are of same class.
Some functions get a verbose-argument to show or suppress warnings.

Renamed r2_coxnell() to r2_coxsnell().
Fix issues in r2_bayes() and model_performance() for ordinal models resp. models with cumulative link (#48).
compare_performance() did not sort the name-column properly, if the columns class and name were not in the same alphabetical order (#51).