DPI
2026.2The Directed Prediction Index for Causal Direction Inference from Observational Data
Overview
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).
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 44%
- Documented parameters
- 92%
- Return-value docs
- 100%
- References docs
- 17%
Downloads
Repository
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Checks run against github.com/psychbruce/dpi on 2026-08-09.
Dependencies
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Code & Tests
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Package metadata
- First published
- 2025-06-18
- Total releases
- 7 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.0.0
- Download size
- 32 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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