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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "rCausalMGM" in publications use:'
type: software
license: GPL-3.0-only
title: 'rCausalMGM: Scalable Causal Discovery and Model Selection on Mixed Datasets
  with ''rCausalMGM'''
version: 1.0.1
doi: 10.32614/CRAN.package.rCausalMGM
abstract: Scalable methods for learning causal graphical models from mixed data, including
  continuous, discrete, and censored variables. The package implements CausalMGM,
  which combines a convex, score-based approach for learning an initial moralized
  graph with a producer-consumer scheme that enables efficient parallel conditional
  independence testing in constraint-based causal discovery algorithms. The implementation
  supports high-dimensional datasets and provides individual access to core components
  of the workflow, including MGM and the PC-Stable and FCI-Stable causal discovery
  algorithms. To support practical applications, the package includes multiple model
  selection strategies, including information criteria based on likelihood and model
  complexity, cross-validation for out-of-sample likelihood estimation, and stability-based
  approaches that assess graph robustness across subsamples.
authors:
- family-names: Lovelace
  given-names: Tyler C
- family-names: Dudek
  given-names: Max
- family-names: Fiore
  given-names: Jack
- family-names: Benos
  given-names: Panayiotis V
  email: pbenos@ufl.edu
repository: https://cran.r-universe.dev
commit: 9e0101244c379a33a1329dfdcb6b9ffcb01fb0d4
date-released: '2026-03-13'
contact:
- family-names: Benos
  given-names: Panayiotis V
  email: pbenos@ufl.edu