Statistical package in Python based on Pandas

Multiple hypothesis testing in Python

The Scott-Knott Effect Size Difference (ESD) test

Python package for permutation tests, for statistics and machine learning.

Design of single- and multi-stage multi-arm clinical trials

A statistics package with a variety of bootstrap and other resampling tools

Adjust p-values for multiple comparisons

Romano-Wolf p-value adjustments for multiple hypotheses testing via the wild bootstrap for objects of type fixest and fixest_multi from the fixest package

python package for generating compact letter display, summarizing results of posthoc comparison tests after ANOVA

SimOutUtils - Utilities for analyzing time series simulation output

SimTOST is an R package designed to facilitate the sample size estimation for bioequivalence studies using a simulation-based approach.

AskoR pipeline: analysis of gene expression data, using edgeR.

A statistics package with a variety of bootstrap and other resampling tools. This repository is synced to the same-named repository owned by GNU-Octave. It exists to facilitate publication of the developmental version of the statistics-resampling toolbox at MathWorks FileExchange.

Spatial Inference for Tractometry

Statistical testing framework using Monte Carlo simulations.

A falsification-first quant research project: a confirmed multi-asset TSMOM core, then four overlays (crash-defense, vol-breakout, seasonality, yield-curve regime) and a cross-sectional momentum (XSMOM) counterpart — all systematically tested and honestly rejected, each with a mechanism. Paired-bootstrap + BH-FDR throughout.

This code was written and used for statistical analysis and visualisation of data included in Drube et al. 2021.

Simulation studies of power and Type I error of mass univariate statistics for ERP data

A Satirical-but-Theoretically-Grounded Treatment of the Only Technique You Will Ever Need in Machine Learning Research

This is my first repository, it contains my final project in the class of Experimental Design with R Programming. In this project i was working with my team to diagnose an appropiate statistical model for Randomized Complete Block Design (RCBD) with 3 factors.