README

RobustFlow is an R package for auditing temporal drift, subgroup disparities, and robustness in longitudinal decision systems. It implements three new metrics and provides an interactive Shiny application for exploratory analysis and reproducible reporting.

Motivation

Researchers who study repeated decisions — risk classifications, tracking placements, intervention assignments — face three questions that existing tools do not address together:

Signature Metrics

Installation

Metric	Symbol	Definition
Drift Intensity Index	DII	Frobenius norm of consecutive transition matrix differences
Bias Amplification Index	BAI	Gap_T − Gap_1 (change in disparity from first to last wave)
Temporal Fragility Index	TFI	Minimum hidden-bias attenuation to nullify a longitudinal trend

# install.packages("pak")
pak::pak("causalfragility-lab/RobustFlow")

install.packages("RobustFlow")

Quick Start

library(RobustFlow)

# 1. Validate panel data
validated <- validate_panel_data(
  data     = my_data,
  id       = "child_id",
  time     = "wave",
  decision = "risk_math",
  group    = "ses_group"
)

# 2. Build decision paths
paths <- build_paths(
  data     = validated$data,
  id       = "child_id",
  time     = "wave",
  decision = "risk_math"
)
paths$path_counts        # frequency table
paths$transition_matrix  # pooled transition matrix
paths$path_entropy       # Shannon entropy

# 3. Drift Intensity Index (DII)
drift <- compute_drift(validated$data, "child_id", "wave", "risk_math")
drift$summary            # DII per wave
drift$mean_dii           # mean DII across all periods

# 4. Group gaps and Bias Amplification Index (BAI)
gaps <- compute_group_gaps(validated$data, "wave", "risk_math", "ses_group")
bai  <- compute_bai(gaps$gap)
bai$bai                  # e.g. 0.12
bai$direction            # "amplification"

# 5. Temporal Fragility Index (TFI)
tfi <- compute_tfi_simple(drift$summary$DII[!is.na(drift$summary$DII)])
tfi$tfi                  # minimum attenuation to nullify trend

# 6. Launch the interactive Shiny app
run_app()

The Shiny Application

Function Reference

Example Dataset

The package vignette demonstrates a simulated longitudinal study of mathematics risk status across five elementary school waves for 200 children, with three SES groups. This mimics the structure of the Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011).

Tab	Content
Data	Upload (CSV/RDS), variable mapping, balance diagnostics, missingness
Paths	Path bar chart, frequency table, transition matrix
Drift	Prevalence over time, DII trend plot, drift summary table
Disparities	Group trajectories, gap plot, BAI summary
Robustness	TFI value, sensitivity curve, robustness summary
Intervention	Highest-risk transitions, disparity-generating steps
Report	HTML report download, CSV bundle, reproducible R script

Function	Purpose
`validate_panel_data()`	Validate and prepare longitudinal panel data
`build_paths()`	Construct individual decision paths and transition matrix
`compute_transition_matrix_all()`	Pooled transition matrix across all individuals
`compute_drift()`	Compute the Drift Intensity Index (DII)
`compute_group_gaps()`	Group-specific trajectories and disparity gaps
`compute_bai()`	Compute the Bias Amplification Index (BAI)
`compute_tfi_simple()`	Compute the Temporal Fragility Index (TFI)
`generate_r_script()`	Export a self-contained reproducible R script
`run_app()`	Launch the interactive Shiny application

vignette("introduction", package = "RobustFlow")

Citation

@Manual{RobustFlow2026,
  title  = {{RobustFlow}: Robustness and Drift Auditing for Longitudinal
            Decision Systems},
  author = {Subir Hait},
  year   = {2026},
  note   = {R package version 0.1.0},
  url    = {https://github.com/causalfragility-lab/RobustFlow}
}

RobustFlow