Automated Multicollinearity Management
Add White Noise to Encoded Predictor
Case Weights for Unbalanced Binomial or Categorical Responses
collinear
Automated multicollinearity management
Hierarchical Clustering from a Pairwise Correlation Matrix
Bias Corrected Cramer's V
Pairwise Correlation Data Frame
Pairwise Correlation Matrix
Automated Multicollinearity Filtering with Pairwise Correlations
Removes geometry column in sf data frames
Name of Target-Encoded Predictor
Association Between a Binomial Response and a Continuous Predictor
Rules to Select Default f Argument to Compute Preference Order
Select Function to Compute Preference Order
Data Frame of Preference Functions
Association Between a Count Response and a Continuous Predictor
Association Between a Continuous Response and a Continuous Predictor
Association Between a Categorical Response and a Categorical or Numeri...
Association Between a Categorical Response and a Categorical Predictor
Identify Valid Categorical Predictors
Identify Valid Numeric Predictors
Identify Predictor Types
Identify Zero and Near-Zero Variance Predictors
Identify Numeric and Categorical Predictors
Identify Response Type
Generate Model Formulas
Area Under the Curve of Binomial Observations vs Probabilistic Model P...
Pearson's R-squared of Observations vs Predictions
Cramer's V of Observations vs Predictions
Preference Order Argument in collinear()
Quantitative Variable Prioritization for Multicollinearity Filtering
Target Encoding Lab: Transform Categorical Variables to Numeric
Target Encoding Methods
Validate Data for Correlation Analysis
Validate Data for VIF Analysis
Validate Argument df
Validates Arguments of target_encoding_lab()
Validate Argument predictors
Validate Argument preference_order
Validate Argument response
Variance Inflation Factor
Automated Multicollinearity Filtering with Variance Inflation Factors
Effortless multicollinearity management in data frames with both numeric and categorical variables for statistical and machine learning applications. The package simplifies multicollinearity analysis by combining four robust methods: 1) target encoding for categorical variables (Micci-Barreca, D. 2001 <doi:10.1145/507533.507538>); 2) automated feature prioritization to prevent key variable loss during filtering; 3) pairwise correlation for all variable combinations (numeric-numeric, numeric-categorical, categorical-categorical); and 4) fast computation of variance inflation factors.