The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.