Python library to handle Gene Ontology (GO) terms
Multiple hypothesis testing in Python
Statistics for MINC volumes: A library to integrate voxel-based statistics for MINC volumes into the R environment. Supports getting and writing of MINC volumes, running voxel-wise linear models, correlations, etc.; correcting for multiple comparisons using the False Discovery Rate, and more. With contributions from Jason Lerch, Chris Hammill, J…
Clone of the Bioconductor repository for the onlineFDR package. See https://bioconductor.org/packages/devel/bioc/html/onlineFDR.html for the official development version, and https://dsrobertson.github.io/onlineFDR/ for easy access to documentation.
Best options to rotate defenders in Fantasy Premier League 2023-24
Knockoff-based analysis of GWAS summary statistics data
Adjust p-values for multiple comparisons
Variable Selection with Knockoffs
Developed a machine learning model to identify fraudulent credit applications, with a Fraud Detection Rate of 56.12% at 3% of the population. The resulting model can be utilized in a credit card fraud detection system.
Generic enrichment analysis
R-package and code for the paper 'Frequency Domain Statistical Inference for High-Dimensional Time Series'
Python library for decoding flight‑data recorder formats, including ARINC 717 and ARINC 767. It provides frame parsing, scheduling, VEC/PRM configuration handling, parameter decoding, and utilities for building modern FDR/QAR data pipelines.
Adjust supplied p-values for multiple comparisons via a specified method.
Iteratively randomly pooling scRNA-seq expressing a given gene from different numbers of cells and running DESeq2 with fdrtools correction to determine how many times which genes come out as enriched with said gene
Re-randomisation statistics toolkit in Python — Fisher's resampling test, pairwise multi-group comparisons with FDR / Bonferroni correction, binomial proportion tests with Wilson CIs, and a unified dispatcher for parametric / non-parametric hypothesis tests.
Julia package for "FDR Control via Data Splitting for Testing-after-Clustering (arXiv: 2410.06451)"
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