corrselect-package: corrselect: Correlation-Based and Model-Based Predictor...

In corrselect: Correlation-Based and Model-Based Predictor Pruning

corrselect: Correlation-Based and Model-Based Predictor Pruning

Description

Provides tools for reducing multicollinearity in predictor sets through association-based and model-based approaches. The package offers both fast greedy algorithms for quick pruning and exact graph-theoretic algorithms for exhaustive subset enumeration.

Association-Based Pruning

These functions identify variable subsets where all pairwise correlations or associations remain below a user-defined threshold:

corrPrune

Association-based pruning for numeric or mixed-type data, using exact search by default and falling back to a fast greedy algorithm for large predictor sets

corrSelect

Exhaustive enumeration for numeric data frames

assocSelect

Exhaustive enumeration for mixed-type data (numeric, factor, ordered)

MatSelect

Direct interface using a pre-computed correlation matrix

Model-Based Pruning

These functions use variance inflation factors (VIF) to iteratively remove collinear predictors from regression models:

modelPrune

VIF-based pruning for lm, glm, lme4, and glmmTMB models

Algorithms

The exact enumeration functions (corrSelect, assocSelect, MatSelect) use two graph-theoretic algorithms:

Eppstein-Loffler-Strash (ELS)

Recommended when using force_in constraints

Bron-Kerbosch

Default algorithm, with optional pivoting for performance

Helpers

corrSubset

Extract specific subsets from results

CorrCombo

Class holding enumeration results

Author(s)

Maintainer: Gilles Colling [e-mail] (ORCID) [copyright holder]

Authors:

• Gilles Colling [e-mail] (ORCID) [copyright holder]

See Also

Vignettes: vignette("quickstart", package = "corrselect"), vignette("advanced", package = "corrselect")

corrselect documentation built on July 19, 2026, 1:06 a.m.