false-discovery-rate

MaAsLin2: Microbiome Multivariate Association with Linear Models

Multiple hypothesis testing in Python

Conformal Anomaly Detection & Change-Point Detection

Conditional calibration of conformal p-values for outlier detection.

Statistical inference for Sharpe ratios: probabilistic Sharpe ratio, minimum track record length, and FDR/FWER corrections for screening many strategies

Benchmarking study of recent covariate-adjusted FDR methods

A workflow for metabolite identification and accurate profiling in multidimensional LC-IM-MS-DIA measurements. DOI: 10.5281/zenodo.

Report various statistics stemming from a confusion matrix in a tidy fashion. 🎯

Knockoff-based analysis of GWAS summary statistics data

Adjust p-values for multiple comparisons

Variable Selection with Knockoffs

Large-scale Benchmarking of Microbial Multivariable Association Methods

Reproducible experiments conducted in the paper 'Uncertainty Quantification in Anomaly Detection with Cross-Conformal p-Values'.

Reference implementations of 5 statistical methods for honest AI evals; companion to the PyCon Colombia 2026 talk 'Your AI Eval Is Lying To You'

Sequential Hypothesis Testing with e-Values and p-Values

This repository includes the scripts to replicate the results of my paper entitled "A False Discovery Rate Approach to Optimal Volatility Forecasting Model Selection".

Large-scale Benchmarking of Microbial Multivariable Association Methods

A Python implementation of the "Controlling the False Discovery Rate via Knockoffs" paper from 2015, designed to provide tools for generating knockoff features and applying controlled variable selection techniques in high-dimensional data settings.

Unified Python framework for permutation-based statistical inference on brain connectivity networks (NBS, TFNBS, cNBS, NI-TFNBS, FBC-TFNBS, GLM, ComBat)

The Julia package for estimating and testing a generalized linear mixed model with normal mixture random effects