Description
Construct and Audit Longitudinal Decision Paths.
Description
Tools for constructing and auditing longitudinal decision paths from panel data. Implements a decision infrastructure framework for representing institutional AI systems as generators of time-ordered binary decision sequences. Provides functions to build path objects from panel data, summarise per-unit descriptors (dosage, switching rate, onset, duration, longest run), compute the Decision Reliability Index (DRI) following Cronbach (1951) <doi:10.1007/BF02310555>, estimate Shannon decision-path entropy following Shannon (1948) <doi:10.1002/j.1538-7305.1948.tb01338.x>, classify systems by infrastructure type (static, periodic, continuous, human-in-the-loop), and evaluate subgroup disparities in decision exposure and stability. Applications include education, policy, health, and organisational research.
README.md
decisionpaths
The goal of decisionpaths is to …
Installation
You can install the development version of decisionpaths like so:
# FILL THIS IN! HOW CAN PEOPLE INSTALL YOUR DEV PACKAGE?
Example
This is a basic example which shows you how to solve a common problem:
library(decisionpaths)
## basic example code
What is special about using README.Rmd instead of just README.md? You can include R chunks like so:
summary(cars)
#> speed dist
#> Min. : 4.0 Min. : 2.00
#> 1st Qu.:12.0 1st Qu.: 26.00
#> Median :15.0 Median : 36.00
#> Mean :15.4 Mean : 42.98
#> 3rd Qu.:19.0 3rd Qu.: 56.00
#> Max. :25.0 Max. :120.00
You’ll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this.
You can also embed plots, for example:
In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.