Multiple Pairwise Comparisons (Post Hoc) Tests in Python
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Updated
Sep 19, 2024 - Python
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
R package for random forests model selection, inference, evaluation and validation
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
A C++ library for physics-informed spatial and functional data analysis over complex domains.
A MATLAB package for multivariate permutation testing and effect size measurement
An R package for assumption-lean covariance matrix estimation in high dimensions
B-Spline Density Estimation Library - nonparametric density estimation using B-Spline density estimator from univariate sample.
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
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