Interactive visualization to explore subgroup effects
📦 An R package implementing deft approach for meta analysis
R and Stata package for weighted subgroup analysis (WSGA) in regression discontinuity and difference-in-differences designs via inverse probability weighting.
DoRiS (Dose Response in Subgroups) is a shiny application for the identification of patients for personalized dosing
R code for the discovery of COVID-19 subgroups by symptoms and comorbidities.
AI-integrated meta-analysis platform for research and education
Meta-Analysis for jamovi
This repo contains the code for "Houston we have a Divergence: A Subgroup Performance Analysis of ASR Models"
The Adaptive Local Knockoff Filter
Shiny app for Bayesian-ECD survival modeling with subgroup analysis, RMST summaries, prediction workflows, and posterior probability utilities.
A R package to jointly embed peaks and genes from paired scATAC-seq and scRNA-seq data into a shared low-dimensional space and enable condition comparison.
R package for causal inference with generalized random forests, including causal forest, causal survival forest, and instrumental forest workflows for heterogeneous effect estimation.
Reproducible NHANES health-data pipeline with interpretable ML, calibration, subgroup validation, and governed challenger-model evaluation.
Checks a medical-imaging model's predictions.csv for train/test patient leakage and for subgroups that underperform the rest. Python computes every metric, flag and risk level; Claude writes the validation risk report from those numbers. Synthetic data only, not medical advice.
Responsible student-success predictive modeling in R with temporal validation, calibration, capacity-aware thresholds, subgroup diagnostics, and reproducible scoring.
Testing subgroup and biomarker methods on simulated oncology trials with a CDISC pipeline in R (admiral, tern, bonsaiforest).
AI in Education research bundle auditing an enrollment-time dropout-risk model with calibration, subgroup evidence, robustness, uncertainty, and governance boundaries.
Subgroup-stratified, calibration-aware fairness auditing for ML models: DeLong AUC confidence intervals, per-subgroup calibration error and multiple-comparison-corrected tests. pip install fairscope
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