Software
Selected projects:
- Conformalized learning: uncertainty-aware training of deep neural network classifiers.
- CHR: powerful and adaptive conformal prediction intervals for regression.
- Conformal p-values: software for computing calibration-conditional conformal p-values for outlier detection.
- KnockoffGWAS: GWAS method localizing causal variants across the genome, accounting for population structure and familial relatedness (aka KnockoffZoom v2).
- ARC: Python package for adaptive and reliable classification.
- KnockoffZoom: Flexible tool for the multi-resolution localization of causal variants across the genome.
- DeepKnockoffs: Python package for approximate knockoffs and model-free variable selection.
- SNPknock: R package for generating knockoffs of hidden Markov models and genetic data.
- SNPknock for Python: Python package for generating knockoffs of hidden Markov models and genetic data. Note: this package is not up-to-date compared to the corresponding R package; the R package is currently recommended.
- Knockoffs: R and MATLAB packages for powerful and versatile controlled variable selection.
A full list of projects can be found on GitHub.