Code
This page contains links to code for some recent projects from the Yu group (see specific papers for other code repositories). Going forward, code will be added to the Yu-group github.- Multipurpose data-science libraries
- VeridicalFlow🚰: a library for building stable, trustworthy data-science pipelines based on the PCS framework
- simChef 🍳: An R package to facilitate PCS simulation studies
- imodels 🔎: a python package for fitting interpretable models (contains code for FIGS and Hierarchical shrinkage)
- Stable modeling
- iRF: an implementation of iterative Random Forests in python based on scikit-learn
- escv selection of regularization for lasso for sparser and more interpretable models. (paper)
- stanmf: stability driven non-negative matrix factorization
- Causal inference: X-learner: CATE prediction, staDISC: discovering stable subgroups
- Interpretation / Visualization
- Interpreting neural networks 🧠: ACD: hierarchical interpretations, TRIM: interpreting transformations, CDEP: penalizing explanations
- Adaptive wavelets 🌊: AWD: adaptive wavelets and wavelet distillation
- Visualization 🔥: Superheat: An r package for generating beautiful and customizable heatmaps
- Applied projects
- epiTree — Learning epistatic polygenic phenotypes with boolean interactions
- Extensive and accessible COVID-19 data + forecasting at the county-level + hospital-level. (website)
- staDRIP: drug-response prediction
- Theory
- Theory work: MDI OOB: Debiased RF feature importance, MDL-Complexity