Man pages for collinear
Automated Multicollinearity Management
| add_white_noise | Add White Noise to Encoded Predictor |
| case_weights | Case Weights for Unbalanced Binomial or Categorical Responses |
| collinear | Automated multicollinearity management |
| collinear-package | collinear |
| cor_clusters | Hierarchical Clustering from a Pairwise Correlation Matrix |
| cor_cramer_v | Bias Corrected Cramer's V |
| cor_df | Pairwise Correlation Data Frame |
| cor_matrix | Pairwise Correlation Matrix |
| cor_select | Automated Multicollinearity Filtering with Pairwise... |
| drop_geometry_column | Removes geometry column in sf data frames |
| encoded_predictor_name | Name of Target-Encoded Predictor |
| f_auc | Association Between a Binomial Response and a Continuous... |
| f_auto | Select Function to Compute Preference Order |
| f_auto_rules | Rules to Select Default f Argument to Compute Preference... |
| f_functions | Data Frame of Preference Functions |
| f_r2 | Association Between a Continuous Response and a Continuous... |
| f_r2_counts | Association Between a Count Response and a Continuous... |
| f_v | Association Between a Categorical Response and a Categorical... |
| f_v_rf_categorical | Association Between a Categorical Response and a Categorical... |
| identify_predictors | Identify Numeric and Categorical Predictors |
| identify_predictors_categorical | Identify Valid Categorical Predictors |
| identify_predictors_numeric | Identify Valid Numeric Predictors |
| identify_predictors_type | Identify Predictor Types |
| identify_predictors_zero_variance | Identify Zero and Near-Zero Variance Predictors |
| identify_response_type | Identify Response Type |
| model_formula | Generate Model Formulas |
| performance_score_auc | Area Under the Curve of Binomial Observations vs... |
| performance_score_r2 | Pearson's R-squared of Observations vs Predictions |
| performance_score_v | Cramer's V of Observations vs Predictions |
| preference_order | Quantitative Variable Prioritization for Multicollinearity... |
| preference_order_collinear | Preference Order Argument in collinear() |
| target_encoding_lab | Target Encoding Lab: Transform Categorical Variables to... |
| target_encoding_methods | Target Encoding Methods |
| toy | One response and four predictors with varying levels of... |
| validate_data_cor | Validate Data for Correlation Analysis |
| validate_data_vif | Validate Data for VIF Analysis |
| validate_df | Validate Argument df |
| validate_encoding_arguments | Validates Arguments of 'target_encoding_lab()' |
| validate_predictors | Validate Argument predictors |
| validate_preference_order | Validate Argument preference_order |
| validate_response | Validate Argument response |
| vi | Example Data With Different Response and Predictor Types |
| vif_df | Variance Inflation Factor |
| vif_select | Automated Multicollinearity Filtering with Variance Inflation... |
| vi_predictors | All Predictor Names in Example Data Frame vi |
| vi_predictors_categorical | All Categorical and Factor Predictor Names in Example Data... |
| vi_predictors_numeric | All Numeric Predictor Names in Example Data Frame vi |
