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| bootstrap_model | Model bootstrapping |
| bootstrap_parameters | Parameters bootstrapping |
| check_clusterstructure | Check suitability of data for clustering |
| check_factorstructure | Check suitability of data for Factor Analysis (FA) |
| check_kmo | Kaiser, Meyer, Olkin (KMO) Measure of Sampling Adequacy (MSA) for Factor Analysis |
| check_multimodal | Check if a distribution is unimodal or multimodal |
| check_sphericity | Bartlett's Test of Sphericity |
| ci.default | Confidence Interval (CI) |
| ci.glm | Confidence Interval (CI) |
| ci.glmmTMB | Confidence Interval (CI) |
| ci.hurdle | Confidence Interval (CI) |
| ci.merMod | Confidence Interval (CI) |
| ci.MixMod | Confidence Interval (CI) |
| ci.mixor | Confidence Interval (CI) |
| ci.zeroinfl | Confidence Interval (CI) |
| ci_robust | Confidence Interval (CI) |
| ci_wald | Wald-test approximation for CIs and p-values |
| cluster_analysis | Compute cluster analysis and return group indices |
| cluster_discrimination | Compute a linear discriminant analysis on classified cluster groups |
| cmds | Classical Multidimensional Scaling (cMDS) |
| convert_data_to_numeric | Convert data to numeric |
| convert_efa_to_cfa | Conversion between EFA results and CFA structure |
| convert_efa_to_cfa.fa | Conversion between EFA results and CFA structure |
| data_partition | Partition data into a test and a training set |
| data_to_numeric | Convert data to numeric |
| degrees_of_freedom | Degrees of Freedom (DoF) |
| demean | Compute group-meaned and de-meaned variables |
| describe_distribution | Describe a distribution |
| dof | Degrees of Freedom (DoF) |
| dof_kenward | p-values using Kenward-Roger approximation |
| dof_ml1 | p-values using the "m-l-1" heuristic |
| dof_satterthwaite | p-values using Satterthwaite approximation |
| DRR | Dimensionality Reduction via Regression (DRR) |
| efa_to_cfa | Conversion between EFA results and CFA structure |
| equivalence_test.lm | Equivalence test |
| factor_analysis | Factor Analysis (FA) |
| format_algorithm | Model Algorithm formatting |
| format_bf | Bayes Factor formatting |
| format_model | Model Name formatting |
| format_number | Convert number to words |
| format_order | Order (first, second, ...) formatting |
| format_p | p-values formatting |
| format_parameters | Parameter names formatting |
| format_pd | Probability of direction (pd) formatting |
| format_rope | Percentage in ROPE formatting |
| ICA | Independent Component Analysis (ICA) |
| kurtosis | Compute Skewness and Kurtosis |
| model_bootstrap | Model bootstrapping |
| model_parameters | Model Parameters |
| model_parameters.aov | Parameters from ANOVAs |
| model_parameters.befa | Parameters from PCA/FA |
| model_parameters.betareg | Parameters from (General) Linear Models |
| model_parameters.BFBayesFactor | Parameters from BayesFactor objects |
| model_parameters.brmsfit | Parameters from Bayesian Models |
| model_parameters.cgam | Parameters of Generalized Additive (Mixed) Models |
| model_parameters.clm2 | Parameters from (General) Linear Models |
| model_parameters.default | Parameters from (General) Linear Models |
| model_parameters.gam | Parameters of Generalized Additive (Mixed) Models |
| model_parameters.glmmTMB | Parameters from Mixed Models |
| model_parameters.glmx | Parameters from (General) Linear Models |
| model_parameters.htest | Parameters from Correlations and t-tests |
| model_parameters.kmeans | Parameters from Cluster Models (k-means, ...) |
| model_parameters.lavaan | Parameters from CFA/SEM models |
| model_parameters.Mclust | Mixture Models Parameters |
| model_parameters.merMod | Parameters from Mixed Models |
| model_parameters.mixor | Parameters from Mixed Models |
| model_parameters.omega | Parameters from Structural Models (PCA, EFA, ...) |
| model_parameters.PCA | Parameters from Structural Models (PCA, EFA, ...) |
| model_parameters.principal | Parameters from Structural Models (PCA, EFA, ...) |
| model_parameters.rqss | Parameters of Generalized Additive (Mixed) Models |
| model_parameters.stanreg | Parameters from Bayesian Models |
| model_parameters.zeroinfl | Model Parameters from Zero-Inflated Models |
| model_simulate | Simulated draws from model coefficients |
| n_clusters | Number of clusters to extract |
| n_factors | Number of components/factors to retain in PCA/FA |
| n_parameters | Count number of parameters in a model |
| n_parameters.brmsfit | Count number of parameters in a model |
| n_parameters.default | Count number of parameters in a model |
| n_parameters.gam | Count number of parameters in a model |
| n_parameters.glmmTMB | Count number of parameters in a model |
| n_parameters.merMod | Count number of parameters in a model |
| n_parameters.zeroinfl | Count number of parameters in a model |
| parameters | Model Parameters |
| parameters_bootstrap | Parameters bootstrapping |
| parameters_reduction | Dimensionality reduction (DR) / Features Reduction |
| parameters_selection | Automated selection of model parameters |
| parameters_simulate | Simulate Model Parameters |
| parameters_table | Parameter table formatting |
| parameters_type | Type of model parameters |
| principal_components | Principal Component Analysis (PCA) |
| Print model parameters | |
| print.parameters_model | Print model parameters |
| p_value | p-values |
| p_value.gee | p-values |
| p_value.glmmTMB | p-values |
| p_value.lmerMod | p-values |
| p_value.merMod | p-values |
| p_value.MixMod | p-values |
| p_value.mixor | p-values |
| p_value.rlmerMod | p-values |
| p_value_kenward | p-values using Kenward-Roger approximation |
| p_value_ml1 | p-values using the "m-l-1" heuristic |
| p_value_robust | p-values |
| p_value_satterthwaite | p-values using Satterthwaite approximation |
| p_value_wald | Wald-test approximation for CIs and p-values |
| p_value_wald.merMod | Wald-test approximation for CIs and p-values |
| reduce_parameters | Dimensionality reduction (DR) / Features Reduction |
| rescale_weights | Rescale design weights for multilevel analysis |
| reshape_loadings | Reshape loadings between wide/long formats |
| reshape_loadings.data.frame | Reshape loadings between wide/long formats |
| reshape_loadings.parameters_efa | Reshape loadings between wide/long formats |
| select_parameters | Automated selection of model parameters |
| select_parameters.lm | Automated selection of model parameters |
| select_parameters.merMod | Automated selection of model parameters |
| se_kenward | p-values using Kenward-Roger approximation |
| se_ml1 | p-values using the "m-l-1" heuristic |
| se_satterthwaite | p-values using Satterthwaite approximation |
| simulate_model | Simulated draws from model coefficients |
| simulate_model.glmmTMB | Simulated draws from model coefficients |
| simulate_parameters | Simulate Model Parameters |
| simulate_parameters.default | Simulate Model Parameters |
| skewness | Compute Skewness and Kurtosis |
| smoothness | Quantify the smoothness of a vector |
| standardize_names | Standardize column names |
| standardize_names.parameters_model | Standardize column names |
| standard_error | Standard Errors |
| standard_error.clm2 | Standard Errors |
| standard_error.default | Standard Errors |
| standard_error.factor | Standard Errors |
| standard_error.glmmTMB | Standard Errors |
| standard_error.merMod | Standard Errors |
| standard_error.MixMod | Standard Errors |
| standard_error.mixor | Standard Errors |
| standard_error_robust | Standard Errors |