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Changelog

Source: NEWS.md

corrselect 3.2.3

CRAN release: 2026-07-18

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.
  • assocSelect: method = "eta" computed eta (the square root) instead of eta-squared, contradicting its own documentation.
  • modelPrune: VIF for a multi-level factor averaged its dummy columns into one number instead of computing a generalized VIF, which could report no collinearity for a factor that was severely collinear. Now uses the standard Fox & Monette GVIF determinant ratio, verified against car::vif().
  • modelPrune: refitting a reduced formula with a single random-effect term (e.g. (1 | group)) via unparenthesized term | group fragments was silently reinterpreted by lme4/glmmTMB as a random slope instead of a random intercept, and rejected outright for two or more random-effect terms.
  • modelPrune: added an explicit non-finite design-matrix check, a zero-variance guard for VIF/condition-number scoring (constant predictors now get NA instead of floating-point noise or Inf), and fixed a crash when the surviving fixed-effect terms included interactions or transformations (poly(), log(), :).
  • corrPrune: the returned data frame was built from the internally type-converted copy of data (character/logical coerced to factor, integer to numeric), so a kept column’s type could silently change; it is now subset from the caller’s original, untouched columns.
  • corrPrune: the threshold upper bound ([0, 1]) was validated only when mode = "auto" routed through MatSelect(); threshold > 1 silently succeeded in mode = "greedy". Now validated once up front for every mode.
  • corrPrune: grouped (by =) aggregation quantiled signed correlations, letting a strong negative association in one group be averaged away by a weak positive one in another; associations are now abs()-clamped before aggregation, matching the mixed-type code path.
  • corrPrune: grouped aggregation had four related silent data-loss bugs – an unused factor level within one group produced an NA silently dropped from the group_q quantile (breaking the documented “group_q = 1 holds in every group” guarantee), a suppressWarnings() call hid the informative NA-row-removal warning, n_rows_used double-counted rows from skipped groups, and NA in the by column itself silently excluded rows with no warning at all.
  • corrPrune: force_in association magnitude is now compared with abs() rather than the signed value, so negatively-correlated force_in pairs are correctly rejected in exact mode (greedy mode already matched).
  • corrPrune: errors when by names every column, instead of silently returning a zero-predictor result.
  • corrPrune: distance/maximal association metrics leaked upstream package warnings for constant columns, unlike every other method in the same dispatch; now suppressed to match.
  • corrPrune: hand-rolled its own type-pair-to-association-method table for the assoc_methods_used attribute instead of reusing the table actually used to compute the matrix, risking drift between the two.
  • corrPrune / assocSelect: a constant column crashed corrPrune()’s all-numeric branch while assocSelect() handled the identical input gracefully; constant-column zeroing is now applied structurally for every caller.
  • corrSelect / assocSelect / corrPrune: corrSelect() excluded constant (zero-variance) columns with a warning, while assocSelect() and corrPrune() instead kept them and treated their association with everything as 0, so the same data set could return a different variable set depending on which function was used (#117). All three now exclude constant columns with a warning; a force_in variable excluded for being constant now errors with a specific message instead of falling through to a generic one. Columns constant only within one group of corrPrune()’s by/group_q aggregation are unaffected and still use the previous zero-association handling.
  • modelPrune: the returned data.frame excluded random-effect grouping/slope columns (e.g. site in (1|site)) for mixed-model engines, so refitting from the returned data and attr(., "selected_vars") failed with “object not found”; those columns are now included (#112).
  • modelPrune: .compute_vif()/.compute_condition_indices() scored a constant single remaining predictor as a “perfect” 1.0 instead of the documented NA, because their single-predictor shortcut ran before the zero-variance guard; the guard now runs first (#113).
  • CorrCombo: the S7 validator checked avg_corr/min_corr/max_corr lengths but not that threshold, search_type, and cor_method are scalars, so a malformed object (e.g. a vectorized threshold, or an invalid search_type) built successfully and produced garbled print() output; the validator now rejects these at construction (#114).
  • MatSelect: force_in was resolved against mat’s column names/count before mat was validated as a numeric matrix, so an invalid mat combined with force_in produced a misleading force_in-flavored error instead of the real “must be a numeric matrix” error; matrix validation now runs first (#115).
  • assocSelect: reported a misleading “sparse combinations” error when the real cause was fewer than two complete-case rows; now uses the same explicit check corrSelect() already had.
  • MatSelect: the force_in mutual-violation warning now names the offending pair and value.
  • MatSelect: use_pivot = now errors on non-coercible input instead of silently falling back to the default, and warns when supplied together with method = "els" (a no-op there).
  • MatSelect / assocSelect: force_in is now validated as whole numbers, matching corrSelect()’s existing check, so non-integer indices error instead of silently truncating to a different column.
  • MatSelect / corrSelect / assocSelect: added a warning for combinatorial blowup on large, permissive inputs where both the variable count and compatibility-graph density make exponential enumeration plausible.
  • C++ backend: validateMatrixStructure() let NA/NaN correlation entries silently pass symmetry and diagonal checks (IEEE-754 comparisons against NaN are always false), and checked the diagonal only on the upper-triangular path, letting a symmetric matrix with a wrong diagonal bypass validation entirely when a backend was called directly.
  • C++ backend: validateForcedIndices() now deduplicates force_in so runELS()/runBronKerbosch() can’t return a variable twice.
  • C++ backend: the shared Bron-Kerbosch base case never sorted a clique’s element order, so the default search path (bron-kerbosch with pivoting) returned scrambled combos, silently reordering corrPrune()/corrSubset() output columns.
  • C++ backend: greedyPruneBackend(), the fourth Rcpp-exported backend, was missed by an earlier shared-validation refactor and skipped validateCorMatrix()/validateForcedIndices() entirely.
  • corrSubset: the missing-value warning now lists only the subsets that actually contain NAs.
  • Two package examples called corrSelect(cor(mat)) where MatSelect(cor(mat)) was intended, silently computing correlations of the correlation matrix rather than using it directly.
  • C++ backend: the force_in mutual-violation warning (naming the offending pair when forced variables exceed the threshold against each other) lived only in MatSelect()’s R layer, so calling the exported runELS()/runBronKerbosch() directly gave no signal at all. The check now lives in a shared C++ helper (utils.cpp) called by both, so every entry point that forces such variables in also warns about it (#111).

New Features

  • Added summary.CorrCombo(), reporting aggregate statistics (size range, median, avg_corr range) distinct from print()’s per-subset listing.

Performance

  • runELS()’s degeneracy ordering uses a bucket-queue instead of an O(m^2) repeated min-degree scan, and no longer materializes the full n x n compatibility matrix when force_in restricts the search to a small induced subgraph.
  • The shared Bron-Kerbosch core backtracks its candidate set in place instead of copying it on every recursive call.
  • The greedy backend caches per-variable tie-break statistics incrementally instead of rescanning every candidate from scratch each outer iteration.

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.
  • Added test-brute-force-ground-truth.R: an independent brute-force maximal-subset enumerator, checked against ELS and Bron-Kerbosch (with and without pivoting) across 40 random seeds, plus 25 more under force_in constraints – validating maximality and exhaustiveness simultaneously.
  • Added independent hand-derived reference-value tests for Pearson, Spearman, Kendall, bicor, distance correlation, and maximal information coefficient (previously only eta-squared/Cramer’s V had these), and extended brute-force ground truth to near-0/near-1 threshold boundaries and larger force_in cases.
  • Extracted the previously duplicated Pearson/Spearman/Kendall/bicor/distance/maximal/eta/Cramer’s V logic (independently reimplemented across corrSelect(), assocSelect(), and both corrPrune() branches, with divergent NA/constant-column policies) into shared primitives in R/assoc-metrics.R, now the single source of truth for every caller.

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

CRAN release: 2026-01-08

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 release: 2025-11-29

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

CRAN release: 2025-09-08

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