| as.dag | Check correct model adjustment for identifying causal effects |
| binned_residuals | Binned residuals for binomial logistic regression |
| check_autocorrelation | Check model for independence of residuals. |
| check_autocorrelation.default | Check model for independence of residuals. |
| check_clusterstructure | Check suitability of data for clustering |
| check_collinearity | Check for multicollinearity of model terms |
| check_collinearity.default | Check for multicollinearity of model terms |
| check_collinearity.glmmTMB | Check for multicollinearity of model terms |
| check_concurvity | Check for multicollinearity of model terms |
| check_convergence | Convergence test for mixed effects models |
| check_dag | Check correct model adjustment for identifying causal effects |
| check_distribution | Classify the distribution of a model-family using machine learning |
| check_factorstructure | Check suitability of data for Factor Analysis (FA) with Bartlett's Test of Sphericity and KMO |
| check_heterogeneity_bias | Check model predictor for heterogeneity bias |
| check_heteroscedasticity | Check model for (non-)constant error variance |
| check_heteroskedasticity | Check model for (non-)constant error variance |
| check_homogeneity | Check model for homogeneity of variances |
| check_homogeneity.afex_aov | Check model for homogeneity of variances |
| check_itemscale | Describe Properties of Item Scales |
| check_kmo | Check suitability of data for Factor Analysis (FA) with Bartlett's Test of Sphericity and KMO |
| check_model | Visual check of model assumptions |
| check_model.default | Visual check of model assumptions |
| check_multimodal | Check if a distribution is unimodal or multimodal |
| check_normality | Check model for (non-)normality of residuals. |
| check_normality.merMod | Check model for (non-)normality of residuals. |
| check_outliers | Outliers detection (check for influential observations) |
| check_outliers.data.frame | Outliers detection (check for influential observations) |
| check_outliers.default | Outliers detection (check for influential observations) |
| check_outliers.numeric | Outliers detection (check for influential observations) |
| check_outliers.performance_simres | Outliers detection (check for influential observations) |
| check_overdispersion | Check overdispersion (and underdispersion) of GL(M)M's |
| check_overdispersion.performance_simres | Check overdispersion (and underdispersion) of GL(M)M's |
| check_predictions | Posterior predictive checks |
| check_predictions.default | Posterior predictive checks |
| check_residuals | Check uniformity of simulated residuals |
| check_residuals.default | Check uniformity of simulated residuals |
| check_singularity | Check mixed models for boundary fits |
| check_sphericity | Check model for violation of sphericity |
| check_sphericity_bartlett | Check suitability of data for Factor Analysis (FA) with Bartlett's Test of Sphericity and KMO |
| check_symmetry | Check distribution symmetry |
| check_zeroinflation | Check for zero-inflation in count models |
| check_zeroinflation.default | Check for zero-inflation in count models |
| check_zeroinflation.performance_simres | Check for zero-inflation in count models |
| classify_distribution | Classify the distribution of a model-family using machine learning |
| compare_performance | Compare performance of different models |
| cronbachs_alpha | Cronbach's Alpha for Items or Scales |
| display.performance_model | Print tables in different output formats |
| icc | Intraclass Correlation Coefficient (ICC) |
| item_difficulty | Difficulty of Questionnaire Items |
| item_discrimination | Discrimination of Questionnaire Items |
| item_intercor | Mean Inter-Item-Correlation |
| item_reliability | Reliability Test for Items or Scales |
| item_split_half | Split-Half Reliability |
| looic | LOO-related Indices for Bayesian regressions. |
| mae | Mean Absolute Error of Models |
| model_performance | Model Performance |
| model_performance.BFBayesFactor | Performance of Bayesian Models |
| model_performance.ivreg | Performance of instrumental variable regression models |
| model_performance.kmeans | Model summary for k-means clustering |
| model_performance.lavaan | Performance of lavaan SEM / CFA Models |
| model_performance.lm | Performance of Regression Models |
| model_performance.merMod | Performance of Mixed Models |
| model_performance.rma | Performance of Meta-Analysis Models |
| model_performance.stanreg | Performance of Bayesian Models |
| mse | Mean Square Error of Linear Models |
| multicollinearity | Check for multicollinearity of model terms |
| performance | Model Performance |
| performance_accuracy | Accuracy of predictions from model fit |
| performance_aic | Compute the AIC or second-order AIC |
| performance_aic.default | Compute the AIC or second-order AIC |
| performance_aic.lmerMod | Compute the AIC or second-order AIC |
| performance_aicc | Compute the AIC or second-order AIC |
| performance_cv | Cross-validated model performance |
| performance_hosmer | Hosmer-Lemeshow goodness-of-fit test |
| performance_logloss | Log Loss |
| performance_mae | Mean Absolute Error of Models |
| performance_mse | Mean Square Error of Linear Models |
| performance_pcp | Percentage of Correct Predictions |
| performance_rmse | Root Mean Squared Error |
| performance_roc | Simple ROC curve |
| performance_rse | Residual Standard Error for Linear Models |
| performance_score | Proper Scoring Rules |
| print_md.compare_performance | Print tables in different output formats |
| print_md.performance_model | Print tables in different output formats |
| r2 | Compute the model's R2 |
| r2.default | Compute the model's R2 |
| r2.merMod | Compute the model's R2 |
| r2.mlm | Compute the model's R2 |
| r2_bayes | Bayesian R2 |
| r2_coxsnell | Cox & Snell's R2 |
| r2_efron | Efron's R2 |
| r2_ferrari | Ferrari's and Cribari-Neto's R2 |
| r2_ferrari.default | Ferrari's and Cribari-Neto's R2 |
| r2_kullback | Kullback-Leibler R2 |
| r2_kullback.glm | Kullback-Leibler R2 |
| r2_loo | LOO-adjusted R2 |
| r2_loo_posterior | LOO-adjusted R2 |
| r2_loo_posterior.brmsfit | LOO-adjusted R2 |
| r2_loo_posterior.stanreg | LOO-adjusted R2 |
| r2_mcfadden | McFadden's R2 |
| r2_mckelvey | McKelvey & Zavoinas R2 |
| r2_mlm | Multivariate R2 |
| r2_nagelkerke | Nagelkerke's R2 |
| r2_nakagawa | Nakagawa's R2 for mixed models |
| r2_posterior | Bayesian R2 |
| r2_posterior.BFBayesFactor | Bayesian R2 |
| r2_posterior.brmsfit | Bayesian R2 |
| r2_posterior.stanreg | Bayesian R2 |
| r2_somers | Somers' Dxy rank correlation for binary outcomes |
| r2_tjur | Tjur's R2 - coefficient of determination (D) |
| r2_xu | Xu' R2 (Omega-squared) |
| r2_zeroinflated | R2 for models with zero-inflation |
| residuals.performance_simres | Simulate randomized quantile residuals from a model |
| rmse | Root Mean Squared Error |
| simulate_residuals | Simulate randomized quantile residuals from a model |
| test_bf | Test if models are different |
| test_bf.default | Test if models are different |
| test_likelihoodratio | Test if models are different |
| test_lrt | Test if models are different |
| test_performance | Test if models are different |
| test_vuong | Test if models are different |
| test_wald | Test if models are different |
| variance_decomposition | Intraclass Correlation Coefficient (ICC) |