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Changelog

Source: NEWS.md

collinear 3.0.0

Breaking Changes

API Changes

  • Argument response renamed to responses: Now accepts multiple response variables. Functions affected: collinear(), preference_order(), and related validation functions.

  • Argument encoding_method defaults to NULL in collinear(): Target encoding is now opt-in rather than automatic. Previously defaulted to "mean".

  • Default values changed for max_cor and max_vif: Both now default to NULL, triggering adaptive threshold computation based on the correlation structure of the data.

  • Output structure changed for collinear(): Now returns a list of class collinear_output containing sub-lists of class collinear_selection, each with response, df, preference_order, selection, and formulas slots. Previously returned a character vector or named list of character vectors.

Renamed Functions

Old Name (v2.0) New Name (v3.0)
identify_predictors() Split into identify_valid_variables(), identify_numeric_variables(), identify_categorical_variables(), identify_logical_variables()
identify_predictors_categorical() identify_categorical_variables()
identify_predictors_numeric() identify_numeric_variables()
identify_predictors_zero_variance() identify_zero_variance_variables()
identify_predictors_type() Removed (merged into identify_valid_variables())

Renamed f_ Functions for Preference Order

Old Name (v2.0) New Name (v3.0)
f_r2_glm_gaussian() f_numeric_glm()
f_r2_gam_gaussian() f_numeric_gam()
f_r2_rf() f_numeric_rf()
f_r2_glm_poisson() f_count_glm()
f_r2_gam_poisson() f_count_gam()
f_auc_glm_binomial() f_binomial_glm()
f_auc_gam_binomial() f_binomial_gam()
f_auc_rf_binomial() f_binomial_rf()
f_v_rf() f_categorical_rf()
— f_count_rf() (new)

Major New Features

Adaptive Multicollinearity Thresholds

When both max_cor = NULL and max_vif = NULL, the function now automatically determines optimal filtering thresholds using:

  • The 75th percentile of pairwise correlations as input
  • A sigmoid transformation that smoothly transitions between. conservative (VIF ≈ 2.5) and permissive (VIF ≈ 7.5) thresholds.
  • A GAM model (gam_cor_to_vif) mapping correlation thresholds to equivalent VIF values.

This data-driven approach adapts to each dataset’s correlation structure, preventing over-filtering while maintaining statistically meaningful bounds.

Tidymodels Integration

  • New step_collinear(): Recipe step for multicollinearity filtering in tidymodels workflows.
  • Implements proper prep() and bake() methods following recipes architecture.

Cross-Validation Support in Preference Order

  • New arguments cv_training_fraction and cv_iterations in preference_order() and passed through collinear().
  • Enables robust predictor ranking through repeated train/test splits.

Rich Output Structure

collinear() now returns comprehensive results including:

  • Filtered dataframe with response and selected predictors.
  • Preference order dataframe with rankings.
  • Ready-to-use model formulas (linear, smooth/GAM, classification).

S3 methods print() and summary() for collinear_output and collinear_selection classes provide clean output formatting.

Correlation Matrix Improvements

  • cor_matrix() now returns signed correlations, preserving the positive semi-definite property required for VIF calculations.
  • Absolute values applied only when comparing against max_cor thresholds.
  • Fixes numerical instability that could produce negative VIF scores.

New Functions

Multicollinearity Assessment

  • collinear_stats(): Compute summary statistics for both correlation and VIF.
  • cor_stats(): Summary statistics for pairwise correlations.
  • vif_stats(): Summary statistics for variance inflation factors.

Preference Order

  • f_count_rf(): Score integer count predictors with random forest.

S3 Methods

New Datasets and Models

Name Description
experiment_adaptive_thresholds Validation experiment results (10,000 iterations)
experiment_cor_vs_vif Correlation vs VIF equivalence experiment results
gam_cor_to_vif Fitted GAM for mapping max_cor to max_vif
prediction_cor_to_vif Look-up table for threshold equivalence
toy Simple dataset illustrating multicollinearity concepts
vi_smol Smaller version of vi dataset (610 rows) for faster examples
vi_responses Character vector of response variable names

Improvements

VIF Computation

  • Ridge regularization fallback for near-singular matrices.
  • Improved tolerance calculation for solve() to prevent false singularity detection.
  • VIF values exceeding 1M are now capped to Inf.

Validation

  • New validate_arg_*() functions provide consistent argument checking across the package.
  • Hierarchical function name tracking for clearer error messages.

Documentation

  • Comprehensive roxygen documentation with working examples.
  • @family tags for better cross-referencing.
  • @inheritSection for consistent documentation of shared concepts.

Bug Fixes

  • Fixed correlation matrix handling that destroyed positive semi-definite property when applying abs() before VIF computation.
  • Fixed edge cases in VIF computation for ill-conditioned matrices.
  • Proper handling of single-predictor cases across all functions.

Deprecated

  • Value "auto" in preference_order argument (ignored with message)

collinear 2.0.0

CRAN release: 2024-11-08

Main Improvements

  1. Expanded Functionality: Functions collinear() and preference_order() support both categorical and numeric responses and predictors, and can handle several responses at once.

  2. Robust Selection Algorithms: Enhanced selection in vif_select() and cor_select().

  3. Enhanced Functionality to Rank Predictors: New functions to compute association between response and predictors covering most use-cases, and automated function selection depending on data features.

  4. Simplified Target Encoding: Streamlined and parallelized for better efficiency, and new default is "loo" (leave-one-out).

  5. Parallelization and Progress Bars: Utilizes future and progressr for enhanced performance and user experience.

collinear 1.1.1

CRAN release: 2023-12-08

  • Initial CRAN release
  • Basic multicollinearity filtering with collinear(), cor_select(), and vif_select()
  • Target encoding methods: mean, rank, leave-one-out
  • Preference order functionality
  • Support for mixed numeric and categorical predictors