Multiple Pairwise Comparisons (Post Hoc) Tests in Python

Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).

Hypothesis and statistical testing in Python

📦 Non-parametric Causal Effects Based on Modified Treatment Policies 🔮

The Wasserstein Distance and Optimal Transport Map of Gaussian Processes

Multivariate kernel density estimation

Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning

Perform inference on algorithm-agnostic variable importance

Perform inference on algorithm-agnostic variable importance in Python

R package for random forests model selection, inference, evaluation and validation

A C++ library for physics-informed spatial and functional data analysis over complex domains.

B-Spline Density Estimation Library - nonparametric density estimation using B-Spline density estimator from univariate sample.

A MATLAB package for multivariate permutation testing and effect size measurement

An R package for assumption-lean covariance matrix estimation in high dimensions

The R wrapper to the fdaPDE library for physics-informed spatial and functional data analysis.

Chinese Restaurant Process Models for Regression and Clustering. Master branch contains latest stable build.

Nonparametric kernel density estimation, bandwidth selection, and other utilities for analyzing directional data

Robust estimations from distribution structures: Central moments.

My implementation of Symbolic Transfer Entropy (STE): a measure of asymmetric information flow between stochastic processes.

about statistical techniques for Data Science