corrselect 3.2.3

Bug Fixes

• C++ backend: runELS() was a single greedy expansion per seed vertex, not an implementation of Eppstein-Loffler-Strash, and could silently miss valid maximal subsets. Replaced with a genuine ELS implementation (degeneracy ordering + per-vertex bounded expansion), sharing a verified Bron-Kerbosch pivot core with the "bron-kerbosch" method. Verified against brute-force enumeration.
• Greedy backend: an undefined (NaN) association was silently treated as compatible (NaN > threshold is false in C++); NaN now always registers as a threshold violation.
• MatSelect: the symmetry check used exact floating-point equality, inconsistent with the R layer’s 1e-8 tolerance, and could reject matrices the R layer had already accepted as symmetric. Added a minimum ncol >= 2 guard, and n_rows_used no longer reports a fabricated row count for matrix input (now NA).
• corrSelect: numeric force_in indices were checked against the final (filtered) correlation matrix but never remapped from the original data frame’s column positions, so a numeric index could silently force the wrong variable into every subset after non-numeric or constant columns were dropped.
• corrPrune: no longer errors on trivially satisfiable inputs (a single predictor, or a set where every pair exceeds the threshold) – the pairwise constraint holds vacuously for one variable, so one is now retained instead of raising “No valid subsets found”.
• corrPrune / modelPrune / assocSelect: undefined (NA) associations were handled inconsistently – silently treated as zero association, silently passed through the greedy backend, or produced blank/uninformative errors. All undefined associations are now surfaced explicitly with a clear message identifying the affected variable pair(s).
• corrPrune: the measure argument had no effect on mixed-type data (numeric-numeric pairs always used Pearson regardless of the requested measure). It now customizes numeric-numeric pairs as documented, and the measure actually used per pair-type is reported via a new assoc_methods_used attribute.
• modelPrune: VIF and condition-number computation matched design-matrix columns to predictor names with a prefix-based regex, which could silently collide (e.g. "x1" matching the "x10" column). Columns are now resolved via the model’s own assign bookkeeping.
• modelPrune: formulas with a transformed response (e.g. log(mpg) ~ .) crashed during formula parsing.
• corrSubset: which = "best" on a CorrCombo with no subsets raised an uninformative “subscript out of bounds” error instead of a clear message.
• Internal Rcpp exports (runELS, runBronKerbosch) now validate force_in bounds directly rather than relying solely on the R-level dispatcher.
• Added duplicate-column-name checks, force_in/by overlap detection, and a coverage warning when most groups are skipped during grouped aggregation in corrPrune.

Test Coverage Improvements

• Added recovery-style and reference-verified tests for corrPrune and modelPrune: hand-computed grouped quantile aggregation, exact-value tie-break tests (lexicographic and greedy), a greedy-vs-exact identity check, VIF verified against car::vif(), condition-number verified against a manual SVD reference, and seed-repeated recovery tests against simulated ground truth.
• Fixed five modelPrune tests that silently passed a nonexistent threshold argument instead of limit.

corrselect 3.2.2

Maintenance

• Fixed .Rbuildignore to exclude non-standard hidden directories flagged by CRAN incoming checks.
• Removed temporary files from package source.

corrselect 3.2.0

Breaking Changes

• CorrCombo class migrated from S4 to S7: The CorrCombo result class now uses the modern S7 object system instead of S4. This brings cleaner construction (CorrCombo(...) instead of new("CorrCombo", ...)), built-in validation, and forward-looking OOP design.
• names property renamed to var_names: S7 reserves names as a property name. Code accessing result@names must be updated to result@var_names. All other @ property access (@subset_list, @avg_corr, etc.) is unchanged.
• The methods package is no longer imported; S7 is now a dependency.

corrselect 3.1.0

Bug Fixes

• corrPrune: Fixed numeric-numeric pair handling in mixed-type data (was incorrectly using Cramer’s V instead of Pearson correlation)
• corrPrune: Fixed numeric-ordered pair handling (now properly converts ordered to numeric for Spearman correlation)

Test Coverage Improvements

Coverage improved from 92% to 94%:

• Added tests for optional package measures (bicor, distance, maximal) with proper skip_if_not_installed() guards
• Added tests for lme4 and glmmTMB engines in modelPrune
• Added chi-squared edge case tests (sparse contingency tables, NA handling)
• Added VIF edge case tests (perfect collinearity, single predictor)
• Added lexicographic tie-breaking tests with synthetic correlation structures
• Added mixed-type data tests (numeric-ordered, ordered-ordered, factor-factor pairs)
• Added condition_number criterion tests
• findAllMaxSets.R now at 100% coverage
• corrPrune.R now at 97% coverage

corrselect 3.0.7

New Features

corrPrune Enhancements

• Grouped pruning: New by parameter computes association matrices per group and aggregates using the group_q quantile (default: 0.5 = median). Useful when correlations vary across experimental conditions or subpopulations.
• Additional measures for numeric data:
  • bicor: Biweight midcorrelation (requires WGCNA package)
  • distance: Distance correlation (requires energy package)
  • maximal: Maximal information coefficient (requires minerva package)

modelPrune Enhancements

• Condition number criterion: New criterion = "condition_number" option uses SVD-based condition indices for detecting multicollinearity. Higher values indicate greater collinearity.

Tests

• Added comprehensive tests for grouped pruning functionality
• Added tests for condition_number criterion
• Added edge case tests for single-group and insufficient-rows scenarios

corrselect 3.0.4

Test Coverage Improvements

• Removed dead C++ code (isValidAddition, isValidCombination) from utils.cpp/utils.h
• Added edge case tests for ELS algorithm (force_in validation, threshold boundaries)
• Added edge case tests for association methods (Cramer’s V sparse tables, eta edge cases)
• Added tests for corrPrune lexicographic tiebreaker and factor handling
• Added tests for modelPrune custom engine error handling and VIF edge cases
• Test coverage improved from 91.86% to 93.44%

corrselect 3.0.3

JOSS Review Response

This release addresses reviewer feedback from the JOSS submission.

Documentation

• paper.md: Strengthened comparison with caret::findCorrelation() to emphasize the key difference (single solution vs. all maximal subsets)
• paper.md: Added explicit graph-theoretic context (maximal cliques / independent sets formulation)
• paper.md: Clarified that Bron-Kerbosch and ELS algorithms are implemented natively in C++, not as wrappers around igraph
• paper.md: Added note about NP-hard complexity and the recommendation to use exact mode only for p ≤ 100
• paper.md: Added code snippet demonstrating the “all subsets” output
• paper.bib: Added citations for igraph (Csardi & Nepusz, 2006) and FCBF (Yu & Liu, 2003)
• README.md: Added CRAN installation instructions (install.packages("corrselect"))
• README.md: Fixed mixed model example with suppressWarnings() to hide expected VIF computation warnings
• quickstart vignette: Fixed GitHub repository reference (GillesColling → gcol33)

Testing

• Added edge case test: identity matrix (all off-diagonals = 0) returns single subset with all variables
• Added edge case test: perfect duplicates (r = 1.0) are correctly separated into different subsets
• Added threshold boundary test for correlation exactly at threshold

Infrastructure

• Added GitHub Actions workflow for cross-platform R CMD check (Ubuntu, macOS, Windows)
• Added GitHub Actions workflow for test coverage reporting
• Updated .gitignore to exclude build artifacts (*.Rcheck/, *.tar.gz, CRAN-SUBMISSION)

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