{
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  "Package": "DPI",
  "Title": "The Directed Prediction Index for Causal Direction Inference\nfrom Observational Data",
  "Version": "2026.2",
  "Date": "2026-02-25",
  "Authors@R": "c(person(given = \"Han Wu Shuang\",\nfamily = \"Bao\",\nrole = c(\"aut\", \"cre\"),\nemail = \"baohws@foxmail.com\",\ncomment = c(ORCID = \"0000-0003-3043-710X\")))",
  "Maintainer": "Han Wu Shuang Bao <baohws@foxmail.com>",
  "Description": "The Directed Prediction Index ('DPI') is a causal\ndiscovery method for observational data designed to quantify\nthe relative endogeneity of outcome (Y) versus predictor (X)\nvariables in regression models. By comparing the coefficients\nof determination (R-squared) between the Y-as-outcome and\nX-as-outcome models while controlling for sufficient\nconfounders and simulating k random covariates, it can quantify\nrelative endogeneity, providing a necessary but insufficient\ncondition for causal direction from a less endogenous variable\n(X) to a more endogenous variable (Y). Methodological details\nare provided at <https://psychbruce.github.io/DPI/>. This\npackage also includes functions for data simulation and network\nanalysis (correlation, partial correlation, and Bayesian\nNetworks).",
  "License": "GPL-3",
  "Encoding": "UTF-8",
  "URL": "https://psychbruce.github.io/DPI/",
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  "Repository": "https://psychbruce.r-universe.dev",
  "Date/Publication": "2026-02-26 08:50:28 UTC",
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    "causal-inference",
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