Construct and Audit Longitudinal Decision Paths

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) , estimate Shannon decision-path entropy following Shannon (1948) , 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.

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.