Package: DPI
Title: The Directed Prediction Index for Causal Direction Inference
        from Observational Data
Version: 2025.11
Date: 2025-11-24
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 quasi-causal inference (causal discovery) method for observational data
    designed to quantify the relative endogeneity (relative dependence)
    of outcome (Y) versus predictor (X) variables in regression models.
    By comparing the proportion of variance explained (R-squared)
    between the Y-as-outcome model and the X-as-outcome model
    while controlling for a sufficient number of possible confounders,
    it can suggest a plausible (admissible) direction of influence
    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: 2025-11-24 09:52:16 UTC; baohw
Author: Han Wu Shuang Bao [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-3043-710X>)
Repository: CRAN
Date/Publication: 2025-11-24 10:10:08 UTC
Built: R 4.6.0; ; 2025-11-24 13:07:02 UTC; unix
