A scikit-learn-compatible module to estimate prediction intervals and control risks based on conformal predictions.

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Python package for conformal prediction

📊 Computation and processing of models' parameters

A package for statistically rigorous scientific discovery using machine learning. Implements prediction-powered inference.

A comprehensive exploration of Statistics and Probability Theory concepts, with practical implementations in Python

Statistical bootstrapping library for Julia

Randomization-based inference in Python

Robust statistics in Python

Honest inference in regression discontinuity designs

PyTorch Code for running various time series models for different time stamps and confidence intervals for Solar Irradiance prediction.

Bringing back uncertainty to machine learning.

Statistical functions based on bootstrapping for computing confidence intervals and p-values comparing machine learning models and human readers

Collection of Artificial Intelligence Algorithms implemented on various problems

An R-Package to estimate and plot confounder-adjusted survival curves (single event survival data) and confounder-adjusted cumulative incidence functions (data with competing risks) using various methods.

Wilson score interval implemented in javascript

Collection of Matlab functions for the computation of measures of effect size

Adaptive Conformal Prediction Intervals (ACPI) is a Python package that enhances the Predictive Intervals provided by the split conformal approach by employing a weighting strategy.

Computationally efficient confidence intervals for cross-validated AUC estimates in R

Library for bootstrapping statistics