DPI: The Directed Prediction Index for Causal Inference from
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 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 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
glue, crayon, cli, ggplot2, cowplot, qgraph, bnlearn, MASS |
| Suggests: |
bruceR, aplot, bayestestR |
| Published: |
2025-10-16 |
| 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 |
Documentation:
Downloads:
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