DPI: The Directed Prediction Index for Causal Direction Inferencefrom Observational Data
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).
| Version: | 2025.11 |
| Depends: | R (≥ 4.0.0) |
| Imports: | glue,crayon,cli,ggplot2,cowplot,qgraph,bnlearn,MASS |
| Suggests: | bruceR,aplot,bayestestR |
| Published: | 2025-11-24 |
| DOI: | 10.32614/CRAN.package.DPI |
| Author: | Han Wu Shuang Bao [aut, cre] |
| Maintainer: | Han Wu Shuang Bao <baohws at foxmail.com> |
| 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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