{
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  "Package": "PFCI",
  "Title": "Penalized Fast Causal Inference for High-Dimensional Structure\nLearning",
  "Version": "0.1.1",
  "Date": "2026-05-28",
  "Authors@R": "c(\nperson(\"Samhita\", \"Pal\", email = \"spal4@ncsu.edu\",\nrole = c(\"aut\"), comment = c(ORCID = \"0009-0001-4930-916X\")),\nperson(\"Dhrubajyoti\", \"Ghosh\", email = \"dghosh3@kennesaw.edu\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-3360-3786\")),\nperson(\"Shu\", \"Yang\", email = \"syang24@ncsu.edu\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0001-7703-707X\"))\n)",
  "Description": "Implements Penalized Fast Causal Inference (PFCI), a\ntwo-stage causal structure learning procedure for\nhigh-dimensional settings with potential latent variables and\nselection bias. In the first stage, neighborhood selection via\nthe Lasso constructs a sparse undirected skeleton. In the\nsecond stage, the Fast Causal Inference (FCI) algorithm orients\nedges on this reduced graph, producing a Partial Ancestral\nGraph (PAG) that accounts for latent confounders. The method is\nconsistent under sparsity assumptions and substantially faster\nthan standard FCI and RFCI in high dimensions. See Pal, Ghosh,\nand Yang (2025) <doi:10.48550/arXiv.2507.00173> for the\nunderlying theory.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "URL": "https://github.com/djghosh1123/PFCI",
  "BugReports": "https://github.com/djghosh1123/PFCI/issues",
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  "Date/Publication": "2026-06-03 05:12:21 UTC",
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  "Author": "Samhita Pal [aut] (ORCID: <https://orcid.org/0009-0001-4930-916X>),\nDhrubajyoti Ghosh [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-3360-3786>),\nShu Yang [aut] (ORCID: <https://orcid.org/0000-0001-7703-707X>)",
  "Maintainer": "Dhrubajyoti Ghosh <dghosh3@kennesaw.edu>",
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        "Installation",
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        "Plotting the PAG",
        "Latent confounders",
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