Penalized Fast Causal Inference for High-Dimensional Structure Learning R package PFCI version 0.1.0 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) <doi:10.48550/arXiv.2507.00173> for the underlying theory. Software Imports: stats, glasso, methods Suggests: pcalg, graph, RBGL, Rgraphviz, testthat (>= 3.0.0), knitr, rmarkdown, spelling Dhrubajyoti Ghosh <dghosh3@kennesaw.edu> Comprehensive R Archive Network (CRAN) Samhita Pal [aut] (ORCID: <https://orcid.org/0009-0001-4930-916X>), Dhrubajyoti Ghosh [aut, cre] (ORCID: <https://orcid.org/0000-0002-3360-3786>), Shu Yang [aut] (ORCID: <https://orcid.org/0000-0001-7703-707X>) MIT + file LICENSE (https://CRAN.R-project.org/package=PFCI/LICENSE) 2026-06-02 application/tgz https://CRAN.R-project.org/package=PFCI doi:10.32614/CRAN.package.PFCI en-US