https://psychbruce.github.io/DPI/>. This package also provides functions for data simulation and network analysis (correlation, partial correlation, and Bayesian networks).">

DPI: The Directed Prediction Index for Causal Inference from Observational Data (original) (raw)

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 more exogenous variable (X) to a more endogenous variable (Y). Methodological details are provided at <https://psychbruce.github.io/DPI/>. This package also provides functions for data simulation and network analysis (correlation, partial correlation, and Bayesian networks).

Version: 2025.10-1
Depends: R (≥ 4.0.0)
Imports: glue, crayon, cli, ggplot2, cowplot, qgraph, bnlearn, MASS
Suggests: bruceR, aplot, bayestestR
Published: 2025-10-23
DOI: 10.32614/CRAN.package.DPI
Author: Han Wu Shuang Bao ORCID iD [aut, cre]
Maintainer: Han Wu Shuang Bao
BugReports: https://github.com/psychbruce/DPI/issues
License: GPL-3
URL: https://psychbruce.github.io/DPI/
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: DPI results

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