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
SimTOST is an R package designed to facilitate the sample size estimation for bioequivalence studies using a simulation-based approach.
SimOutUtils - Utilities for analyzing time series simulation output
AskoR pipeline: analysis of gene expression data, using edgeR.
Spatial Inference for Tractometry
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.
Statistical testing framework using Monte Carlo simulations.
This code was written and used for statistical analysis and visualisation of data included in Drube et al. 2021.
R package for sign-flip permutation inference on group fMRI, reporting FWER-corrected p-values. Offers a quantum amplitude-estimation estimator (emulated in R, with an optional real Qiskit backend) next to the classical permutation test.
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.
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
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