R-universe - djghosh1123 (Dhrubajyoti Ghosh)

PFCI - Penalized Fast Causal Inference for High-Dimensional Structure Learning

Implements Penalized Fast Causal Inference (PFCI), a two-stage causal structure learning procedure for high-dimensional settings with potential latent variables and selection bias. In the first stage, neighborhood selection via the Lasso constructs a sparse undirected skeleton. In the second stage, the Fast Causal Inference (FCI) algorithm orients edges on this reduced graph, producing a Partial Ancestral Graph (PAG) that accounts for latent confounders. The method is consistent under sparsity assumptions and substantially faster than standard FCI and RFCI in high dimensions. See Pal, Ghosh, and Yang (2025), accepted for publication in the Annals of Applied Statistics, <doi:10.48550/arXiv.2507.00173>, for the underlying theory.

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4.18 score 8 scripts 286 downloads

glmgraph - Graph-Constrained Regularization for Sparse Generalized Linear Models

We propose to use sparse regression model to achieve variable selection while accounting for graph-constraints among coefficients. Different linear combination of a sparsity penalty(L1) and a smoothness(MCP) penalty has been used, which induces both sparsity of the solution and certain smoothness on the linear coefficients.

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cpp

1.94 score 4 stars 22 scripts 4 downloads