performance

• check_outliers() now computes the default threshold for Cook's distance as qf(0.5, p, n - p), as the documentation states. Here p is the number of fixed effects of the conditional model, and n is the number of observations in the fit. Before, p was the number of numeric variables in the data. If n - p is less than 1, method = "cook" gives an error.
• check_outliers() gets a group argument for models from packages *glmmTMB* and *lme4*. It gives Cook's distance for each level of a grouping variable. The model is refit without each level in turn. With G the number of levels, levels above 4 / G are flagged (#95).
• check_outliers() now supports models from package *glmmTMB*. Cook's distance is computed by refitting the model without each observation, and all data-based methods (like "zscore_robust" or "mahalanobis") work as well (#95).
• model_performance() now supports models fitted to multiple imputed data sets with package *lavaan.mi*. The fit indices are pooled across imputations by lavaan::fitMeasures(), and pooling arguments such as pool.method can be passed through ... (#888).
• check_overdispersion() now warns for models with a quasipoisson or quasibinomial family. These models already estimate a dispersion parameter, so their standard errors account for overdispersion. When over- or underdispersion is detected, the printed conclusion also says so (#734).

Bug fixes

• check_collinearity() now gives the correct adjusted VIF (SE_factor) for factor terms in models with a factor response, such as logistic and ordinal models, and in mixed models with grouping factors. Before, these models got the square root of the VIF for every term, also for factors with more than two levels.
• check_collinearity() now gives the correct VIFs for ordinal models from MASS::polr(), survey::svyolr(), ordinal::clm2(), ordinal::clmm2(), and rstanarm::stan_polr(). These models have one threshold for each outcome level except one, and no single intercept. Before, check_collinearity() dropped only the first column of the variance-covariance matrix as the intercept. The VIFs then included the thresholds. For MASS::polr(), survey::svyolr(), and rstanarm::stan_polr(), they also left out the first slope. Thus the VIFs were wrong.
• Some models drop an aliased predictor, which is a linear combination of other predictors. For such ordinal::clm() and MASS::polr() models, check_collinearity() now warns that the model matrix is rank deficient. It also labels each VIF with its own term.
• check_collinearity() uses a new method for the confidence intervals of VIF and tolerance. The method inverts the noncentral F distribution of the auxiliary regression of each term, as MBESS::ci.R2() does for fixed predictors (Kelley 2007). The old interval had an infinite upper limit for VIFs close to 1, and no upper limit for a VIF of exactly 1 (#597).
• check_overdispersion() now uses the residual degrees of freedom of the model (df.residual()) for glm and *fixest* models, but not for svyglm() models (package *survey*). Before, it subtracted the number of coefficients from the number of observations. That was wrong for models with aliased coefficients and for *fixest* models with fixed effects. Now, the dispersion ratio of quasipoisson and quasibinomial glm models with aliased coefficients matches summary(model)$dispersion up to the fitting tolerance. It already did so for other stats::glm() models. Other classes that inherit from glm now also use their own df.residual(). For example, gam() models (package *mgcv*) use the effective degrees of freedom.
• check_overdispersion() now gives correct results for models from package *glmx* with a negative binomial family, or with a binomial family and more than one trial. *glmx* has no method for Pearson residuals, so its stored deviance contributions were used before. Bernoulli models and other *glmx* families give an error.
• If a model has no Pearson residuals, check_overdispersion() now gives an error. Before, it used the formula for Poisson models, which is wrong for other families.
• The overdispersion plot of check_model() now uses the estimated dispersion for quasipoisson glm models. Before, it showed the variance of a Poisson model.
• Fixed issue in check_predictions() for *brms* models with dec() response (Wiener and RT-choice families); the RT part of the model is now checked.
• test_performance() now works for models from package *lavaan*. Before, it failed with an infinite recursion. It returns the Bayes factor and the likelihood-ratio test from lavaan::lavTestLRT(), and compares the models in order of their degrees of freedom. test_vuong() and test_wald() now give a clear error for *lavaan* models instead of the same recursion (#427).
• check_collinearity() uses a new method for the confidence intervals of VIF and tolerance. The method inverts the noncentral F distribution of the auxiliary regression of each term, as MBESS::ci.R2() does for fixed predictors (Kelley 2007). The old interval had an infinite upper limit for VIFs close to 1, and no upper limit for a VIF of exactly 1 (#597).
• check_overdispersion() now uses the residual degrees of freedom of the model (df.residual()) for glm and *fixest* models, but not for svyglm() models (package *survey*). Before, it subtracted the number of coefficients from the number of observations. That was wrong for models with aliased coefficients and for *fixest* models with fixed effects. Now, the dispersion ratio of quasipoisson and quasibinomial glm models with aliased coefficients matches summary(model)$dispersion up to the fitting tolerance. It already did so for other stats::glm() models. Other classes that inherit from glm now also use their own df.residual(). For example, gam() models (package *mgcv*) use the effective degrees of freedom.
• check_overdispersion() now gives correct results for models from package *glmx* with a negative binomial family, or with a binomial family and more than one trial. *glmx* has no method for Pearson residuals, so its stored deviance contributions were used before. Bernoulli models and other *glmx* families give an error.
• If a model has no Pearson residuals, check_overdispersion() now gives an error. Before, it used the formula for Poisson models, which is wrong for other families.
• The overdispersion plot of check_model() now uses the estimated dispersion for quasipoisson glm models. Before, it showed the variance of a Poisson model.
• Fixed issue in check_predictions() for *brms* models with dec() response (Wiener and RT-choice families); the RT part of the model is now checked.
• test_performance() now works for models from package *lavaan*. Before, it failed with an infinite recursion. It returns the Bayes factor and the likelihood-ratio test from lavaan::lavTestLRT(), and compares the models in order of their degrees of freedom. test_vuong() and test_wald() now give a clear error for *lavaan* models instead of the same recursion (#427).
• check_collinearity() now gives correct VIFs for multinomial models from package *brglm2* (brmultinom(), and bracl() for adjacent-category models). It also gives correct VIFs for VGAM::vglm() models with the multinomial or cumulative family. Before, brmultinom() models and parallel bracl() models got VIFs for the wrong coefficients. Other bracl() models got no result, and vglm() models gave an error. Some models keep all coefficients for exactly collinear predictors, for example nnet::multinom(). For a singular correlation matrix of the coefficients, check_collinearity() now gives a warning and returns NULL. Before, it returned meaningless VIFs.
• If you combine "cook" with a method that flags no observation, such as "zscore_robust", check_outliers() no longer fails. Cook's distance now also works for binomial models with only factor predictors. For *glmmTMB* models, the refits for Cook's distance now use the data given to the model. Thus, models with missing values, a subset, or weights and offsets outside the data get correct values. If a refit fails with the full model's estimates as start values, check_outliers() tries it again without them.
• check_collinearity() now gives correct VIFs for multinomial models from packages *nnet* (multinom()) and *mclogit* (mblogit()). Before, only the first intercept was removed and coefficients were matched to terms by position, so the VIFs belonged to the wrong coefficients. Now each term gets one VIF, computed from its slopes across all outcome levels, so it does not depend on the reference level (#907).
• check_model() now uses the value of show_ci that the user sets. Before, it ignored that value. It always hid the confidence bands for models with only categorical predictors and always showed them for other models (#642, #490).
• check_collinearity() now returns interaction terms for *brms* models. Before, a model such as y ~ a * b gave no a:b row, and the VIFs of the other terms belonged to the wrong coefficients. Models with more than one intercept, such as ordinal models, also gave VIFs for the wrong coefficients.
• compare_performance() no longer fails when only some of the models have LOOIC, for example a *brms* model and a glm (#738). It now computes stacking weights for the models that have LOOIC and gives NA for the others. A missing weight now prints as an empty cell, not as (>.999).
• check_model() no longer fails for *brms* models that cannot be converted to a frequentist model. It now returns only the posterior predictive check and multicollinearity panels, and a message lists the panels that were skipped (#461).
• check_collinearity() now labels terms correctly for *brms* models that use the 0 + Intercept formula syntax. Before, each VIF was shown under the name of the previous term and the last term was missing (#417).
• r2_nakagawa() and icc() now include the random-slope variance for *lme4* models with uncorrelated random slopes, such as (x || group). The fix is in insight::get_variance(). For these models, a shift of a predictor changes the values, and the documentation of both functions now says so (#428).
• model_performance() for *lavaan* models now passes further arguments in ... to lavaan::fitMeasures() by name. Before, any extra argument other than estimator caused an error, even an argument that fitMeasures() accepts, such as fm_args (fm.args in *lavaan* before 0.7).
• model_performance() for *lavaan* models no longer errors when lavaan::fitMeasures() does not return an index, for example the loglikelihood, AIC, and BIC of models with ordinal indicators. These indices are now NA.
• check_overdispersion() now computes the Pearson chi-squared statistic from the model's own Pearson residuals whenever it does not use simulated residuals. Results change, and are now correct, for binomial models with more than one trial per observation (a cbind() response, or proportions with weights) and for models with prior weights. Before, the statistic used unweighted Poisson-style residuals and could be far off.
• check_model() now correctly returns the random-effects normality panel for mixed models that specify random slopes using split terms such as (1 | group) + (0 + slope | group) (including || expansions, #845).
• check_singularity() for models from package *nlme* no longer errors for nested random effects with more than one grouping level and now applies the tolerance check to the absolute values of the variance-covariance diagonal.
• check_collinearity() no longer drops interaction terms when fallback term assignment is used (for example, in some brmsfit models, #936).
• Preserve method–threshold alignment in print() method for check_outliers() for multi-method input.
• r2_tjur(), r2_somers(), r2_efron() and r2_mckelvey() now warn and return NULL for binomial models that are not Bernoulli models, like r2(). Examples are a proportion response with weights and a cbind() response. Before, these functions returned inaccurate values or failed with an unclear error (#165).
• The influential observations plot of check_model() and check_outliers() for linear mixed models from package *lme4* now shows standardized residuals instead of raw residuals. Before this fix, points fell outside the Cook's distance lines but were not flagged as outliers (#857).
• The Cook's distance lines in the influential observations plot of check_model() and check_outliers() now use the threshold of check_outliers(), including a user-supplied threshold. Before this fix, linear models always used a threshold from the model's F statistic, so the lines did not match the flagged outliers (#916).