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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

print.collinear_output()
print.collinear_selection()
summary.collinear_output()
summary.collinear_selection()

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

Main Improvements

Expanded Functionality: Functions collinear() and preference_order() support both categorical and numeric responses and predictors, and can handle several responses at once.
Robust Selection Algorithms: Enhanced selection in vif_select() and cor_select().
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.
Simplified Target Encoding: Streamlined and parallelized for better efficiency, and new default is "loo" (leave-one-out).
Parallelization and Progress Bars: Utilizes future and progressr for enhanced performance and user experience.

collinear 1.1.1

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

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They may not be fully stable and should be used with caution. We make no claims about them.
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