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corrselect

every uncorrelated subset of predictors

Exact enumeration of maximal predictor sets under a correlation threshold.

Feed it your predictors. corrselect returns the maximal sets whose pairwise correlations all stay under your threshold, found exactly by graph enumeration (Bron–Kerbosch / Eppstein–Löffler–Strash) in C++. A greedy filter hands you one set and hides the rest. This keeps every variable it can, and shows you all the valid choices.

library(corrselect)

# prune to one low-correlation set, in a single call
corrPrune(mtcars, threshold = 0.7)

# or enumerate *every* maximal low-correlation set
corrSelect(mtcars, threshold = 0.7)

Exact, not greedy

The usual tool, caret::findCorrelation(), removes variables greedily: it is order-dependent, non-deterministic, and typically drops more than it needs to. corrselect solves the same threshold constraint by maximal-clique enumeration, so it retains at least as many variables and returns the same answer every run.

m <- cor(mtcars)

caret::findCorrelation(m, cutoff = 0.7)              # greedy, ordering-dependent
corrPrune(mtcars, threshold = 0.7, mode = "exact")   # exact, deterministic

What’s in the box

• corrPrune(): association-based pruning, model-free. Exact mode for p <= 100, greedy mode for larger p, protect variables with force_in.
• modelPrune(): VIF-based pruning for lm, glm, lme4, glmmTMB, or any custom engine (INLA, mgcv, brms, …).
• corrSelect() / MatSelect(): exhaustive enumeration of all maximal sets, on a data frame or directly on a correlation matrix.
• assocSelect(): mixed-type data (numeric, factor, ordered), with the right association metric chosen per pair.

Multiple association metrics are supported: "pearson", "spearman", "kendall", "bicor" (WGCNA), "distance" (energy), "maximal" (minerva), and "eta" / "cramersv" for mixed-type data.

corrPrune or modelPrune?

Use corrPrune() first to cut dimensionality, then modelPrune() for final cleanup inside a modeling framework.

Model-based pruning with any engine

modelPrune() works with lm, glm, lme4, glmmTMB, or any package you wire in:

# linear model, VIF threshold
modelPrune(mpg ~ cyl + disp + hp + wt, data = mtcars, limit = 5)

# any modeling package, via a custom engine (here: INLA)
inla_engine <- list(
  name = "inla",
  fit  = function(formula, data, ...) {
    INLA::inla(formula, data = data, family = "gaussian", ...)
  },
  diagnostics = function(model, fixed_effects) {
    scores <- model$summary.fixed[, "sd"]          # posterior SD as the badness score
    setNames(scores, rownames(model$summary.fixed))[fixed_effects]
  }
)
modelPrune(y ~ x1 + x2, data = df, engine = inla_engine, limit = 0.5)

Mixed-type data

assocSelect() chooses the right association metric per pair (Pearson, Spearman, eta-squared, Cramér’s V):

df <- data.frame(
  height = rnorm(30, 170, 10),
  weight = rnorm(30, 70, 12),
  group  = factor(sample(c("A", "B"), 30, TRUE)),
  rating = ordered(sample(c("low", "med", "high"), 30, TRUE))
)

assocSelect(df, threshold = 0.6)

Installation

install.packages("corrselect")            # CRAN

install.packages("pak")                   # development version
pak::pak("gcol33/corrselect")

Documentation

• Quick Start
• Workflows
• Comparison
• Advanced Usage
• Theory

Support

“Software is like sex: it’s better when it’s free.” — Linus Torvalds

I’m a PhD student who builds R packages in my free time because I believe good tools should be free and open. I started these projects for my own work and figured others might find them useful too.

If this package saved you some time, buying me a coffee is a nice way to say thanks. It helps with my coffee addiction.

License

MIT (see the LICENSE.md file)

Citation

@article{corrselect,
  author  = {Colling, Gilles},
  title   = {corrselect: Fast and flexible predictor pruning for data analysis and modeling},
  journal = {Journal of Open Source Software},
  year    = {2026},
  volume  = {11},
  number  = {118},
  pages   = {9539},
  doi     = {10.21105/joss.09539},
  url     = {https://joss.theoj.org/papers/10.21105/joss.09539}
}

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.
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