corrselect 3.0.2

CRAN Compliance

• Single-quoted software names in DESCRIPTION (‘lme4’, ‘glmmTMB’) per CRAN policy

Documentation

• Updated vignettes with improved examples and workflows

corrselect 3.0.1

Bug Fixes

• modelPrune(): Fixed infinite loop when VIF computation encountered perfect multicollinearity
  • Added proper handling of Inf and NA VIF values in pruning loop
  • Clamped extreme R² values (> 0.9999) to prevent division by near-zero
  • Added safety checks to prevent removing all variables
• modelPrune(): Fixed design matrix extraction for lme4 and glmmTMB engines
  • Now uses stats::model.matrix() for all engines (more robust)
  • Eliminated “Could not find columns” warnings
• Test suite: All 261 tests pass with zero warnings (CRAN-compliant)

corrselect 3.0.0

Major Release: Predictor Pruning Toolkit

Version 3.0.0 represents a major expansion of corrselect from a specialized subset enumeration tool into a comprehensive predictor pruning toolkit. Fully backward compatible with 2.x - all existing code continues to work.

Major Features

New Functions

• corrPrune(): High-level association-based predictor pruning
  • Model-free pruning using pairwise correlations or associations
  • Automatic measure selection (measure = "auto")
  • Supports exact mode (small p), greedy mode (large p), or auto-selection
  • force_in parameter to protect important predictors
  • Returns single pruned data.frame with pairwise associations ≤ threshold
• modelPrune(): Model-based predictor pruning using diagnostics
  • VIF-based iterative removal of multicollinear predictors
  • Supports multiple engines: lm, glm, lme4, glmmTMB
  • Custom engine support: Define your own modeling backends (INLA, mgcv, brms, etc.)
  • Prunes fixed effects only (preserves random effects in mixed models)
  • force_in parameter for protecting important variables
  • Returns pruned data.frame with final fitted model

New C++ Backend

• Fast deterministic greedy pruning algorithm
  • Polynomial-time complexity O(p² × k) vs exponential for exact search
  • Handles p > 100 efficiently
  • Deterministic tie-breaking for reproducibility
  • Used by corrPrune(mode = "greedy") and mode = "auto"

Enhancements

• Exact methods (corrSelect(), assocSelect()) now integrate seamlessly with corrPrune()
• Deterministic subset selection when multiple maximal sets exist
• Improved error messages for threshold feasibility checks
• Better handling of edge cases (single predictor, all correlated, etc.)
• Custom engine interface for modelPrune(): Users can define custom modeling backends with fit and diagnostics functions, enabling integration with any R modeling package

Documentation

• Five new comprehensive vignettes (~60 minutes of content):
  • Quick Start: 5-minute introduction to corrPrune() and modelPrune()
  • Complete Workflows: Real-world examples across 4 domains (ecology, social science, genomics, clinical)
  • Comparison with Alternatives: When to choose corrselect vs caret, Boruta, glmnet
  • Performance Benchmarks: Timing comparisons, scalability tests, and optimization guidelines
  • Advanced Topics: Algorithms, custom engines (INLA, mgcv), performance optimization, troubleshooting
• Four new example datasets with full documentation (bioclim, survey, genes, longitudinal)
• Updated README with quickstart examples and custom engine support
• Full documentation for corrPrune() and modelPrune()
• Usage examples for all modeling engines

Package Changes

• Added lme4 and glmmTMB to Suggests (required for respective engines)
• Version bumped to 3.0.0 (major feature release)
• Updated package description to reflect expanded pruning functionality

Notes

• No breaking changes: Version 3.0.0 is fully backward compatible with 2.0.1
• For large predictor sets (p > 20), use corrPrune(mode = "auto") for best performance
• Mixed model engines require optional packages: install with install.packages(c("lme4", "glmmTMB"))

corrselect 2.0.1

Bug Fixes

• force_in in MatSelect() now correctly accepts character column names.
• els now correctly lists all valid subsets when a single variable is forced in.
• corrSelect() now displays an appropriate warning if only one variable remains after dropping unsupported columns.
• Association matrix construction in assocSelect() now safely falls back to 0 for failed or meaningless associations (e.g. empty chi-squared tables due to sparse combinations or unused factor levels).

Features Added

• assocSelect() now supports logical columns by automatically converting them to factors.

corrselect 2.0.0

Major Release: Mixed-Type Association Selection

Version 2.0.0 introduces support for mixed-type data through the new assocSelect() function, enabling subset selection on datasets containing numeric, factor, and ordered variables.

Major Features

• assocSelect(): New function for mixed-type data frame interface
  • Handles numeric, factor, and ordered variables
  • Automatic association measure selection based on variable pair types
  • Supports Pearson, Spearman, Kendall correlations
  • Computes Eta-squared for numeric-factor pairs
  • Computes Cramér’s V for factor-factor pairs

Enhancements

• Improved algorithm selection logic
• Better handling of edge cases in subset enumeration
• Enhanced documentation with examples for mixed-type workflows