Package: DPI
Title: The Directed Prediction Index for Causal Direction Inference
        from Observational Data
Version: 2026.2
Date: 2026-02-25
Authors@R: 
    c(person(given = "Han Wu Shuang",
             family = "Bao",
             role = c("aut", "cre"),
             email = "baohws@foxmail.com",
             comment = c(ORCID = "0000-0003-3043-710X")))
Maintainer: Han Wu Shuang Bao <baohws@foxmail.com>
Description: 
    The Directed Prediction Index ('DPI') is a causal discovery method
    for observational data designed to quantify the relative endogeneity
    of outcome (Y) versus predictor (X) variables in regression models.
    By comparing the coefficients of determination (R-squared)
    between the Y-as-outcome and X-as-outcome models
    while controlling for sufficient confounders and
    simulating k random covariates, it can quantify relative endogeneity,
    providing a necessary but insufficient condition for causal direction
    from a less endogenous variable (X) to a more endogenous variable (Y).
    Methodological details are provided at
    <https://psychbruce.github.io/DPI/>.
    This package also includes functions for data simulation and network
    analysis (correlation, partial correlation, and Bayesian Networks).
License: GPL-3
Encoding: UTF-8
URL: https://psychbruce.github.io/DPI/
BugReports: https://github.com/psychbruce/DPI/issues
Depends: R (>= 4.0.0)
Imports: glue, crayon, cli, ggplot2, cowplot, qgraph, bnlearn, MASS
Suggests: bruceR, aplot, bayestestR
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-02-26 07:39:57 UTC; baohw
Author: Han Wu Shuang Bao [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-3043-710X>)
Repository: CRAN
Date/Publication: 2026-02-26 07:50:03 UTC
Built: R 4.6.0; ; 2026-04-29 15:48:33 UTC; unix
