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 3.2.3

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

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.