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Changelog

Source: NEWS.md

corrselect 3.2.3

CRAN release: 2026-07-18

Breaking Changes

  • assocSelect / corrPrune: numeric-categorical pairs were thresholded on eta-squared, a proportion of variance, while numeric-numeric and categorical-categorical pairs were thresholded on a correlation magnitude. At threshold = 0.7 a numeric pair was cut at |r| > 0.7 but a numeric-factor pair only at |r| > 0.837, so re-encoding a binary column from 0/1 numeric to a two-level factor could change which variables survived. Numeric-categorical pairs now use eta, the correlation ratio, which is the multiple correlation between the numeric variable and the factor and equals the absolute point-biserial correlation for a two-level factor. Every pair type is now thresholded on one scale, and mixed-type results may differ from previous versions (#129).

Bug Fixes

  • modelPrune: criterion = "condition_number" paired each predictor with the singular value sitting at its column position. Since svd()$d is ordered by principal direction rather than by column, the overall condition number always landed on the last column of the design matrix and pruning stripped terms from the end of the formula whatever the collinearity structure was. Predictors are now scored through the Belsley-Kuh-Welsch variance decomposition, as the condition index of the principal directions carrying their coefficient variance. On mtcars with limit = 10 this removes disp instead of gear and carb (#126).

  • assocSelect / corrPrune: eta returned NA whenever a factor carried a level with no observations, because tapply() fills an empty level with NA and the between-group sum of squares propagated it. The term for an empty level is n_g * (xbar_g - xbar)^2 with n_g = 0, so the association was well defined all along. This aborted corrPrune(by = ...) on mixed-type data whenever a single group was missing one factor level, which is the ordinary case for small groups (#127).

  • C++ backend / corrPrune: two documented determinism claims did not hold across platforms. Subsets tying on both size and average correlation were ordered by std::sort, which leaves tied elements in an unspecified relative order that can differ between compilers; the ordering corrSubset(which = "best") and print() read is now stable and follows the enumeration order. Separately, corrPrune()’s exact-mode lexicographic tie-break used the session’s collation locale, so variable names mixing case or punctuation could select a different subset in two sessions on the same machine; it now compares in C order (#128).

  • 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