Package: PFCI
Title: Penalized Fast Causal Inference for High-Dimensional Structure
        Learning
Version: 0.1.0
Date: 2026-05-28
Authors@R: c(
    person("Samhita", "Pal", email = "spal4@ncsu.edu",
           role = c("aut"), comment = c(ORCID = "0009-0001-4930-916X")),
    person("Dhrubajyoti", "Ghosh", email = "dghosh3@kennesaw.edu",
           role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3360-3786")),
    person("Shu", "Yang", email = "syang24@ncsu.edu",
           role = c("aut"), comment = c(ORCID = "0000-0001-7703-707X"))       
    )
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/djghosh1123/PFCI
BugReports: https://github.com/djghosh1123/PFCI/issues
RoxygenNote: 7.3.3
Imports: stats, glasso, methods
Suggests: pcalg, graph, RBGL, Rgraphviz, testthat (>= 3.0.0), knitr,
        rmarkdown, spelling
VignetteBuilder: knitr
Config/testthat/edition: 3
Language: en-US
NeedsCompilation: no
Packaged: 2026-05-29 19:16:19 UTC; dghosh3
Author: 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>)
Maintainer: Dhrubajyoti Ghosh <dghosh3@kennesaw.edu>
Repository: CRAN
Date/Publication: 2026-06-02 11:20:13 UTC
