Fortran Statistics and Machine Learning Library

A modern Fortran statistical library.

Statistical evaluation framework for AI agents

AI Firewall and guardrails for LLM-based Elixir applications

Significant Network Interval Mining

High-performance statistical testing and regression for Polars DataFrames, powered by Rust.

Quantitative research tool analyzing stock performance around US Thanksgiving. 354 stocks, 8,293 observations (2000-2024). Statistical significance testing included.

Statistical tests in Rust

Customer base analysis is concerned with using the observed past purchase behavior of customers to understand their current and likely future purchase patterns. More specifically, as developed in Schmittlein et al. (1987), customer base analysis uses data on the frequency, timing, and dollar value of each customer's past purchases

This repository is a fork of a repository originally created by Lucas Descause. It is the codebase used for my Master's dissertation "Reinforcement Learning with Function Approximation in Continuing Tasks: Discounted Return or Average Reward?" which was also an extension of Luca's work.

Yeast TMT data - 3 different carbon sources (from Gygi lab) analyzed with PAW pipeline and MaxQuant

Zero-error LLM execution via SPRT voting. Rust library and MCP server implementing the MAKER algorithm for mathematically-grounded error correction in long-horizon AI agent tasks. Research experiment based on arXiv:2511.09030

MATLAB functions for Beta distribution test

Multivariate analysis (MVA) of high dimensional heterogeneous data

Fairness and bias detection library for Elixir AI/ML systems

Analysis of 2.2 million Realtor.com listings using Python and machine learning to uncover U.S. real estate market patterns. The project identifies market segments, predicts property prices, and reveals regional trends, providing data-driven insights for real estate professionals and investors.

Interactive Phoenix LiveView demonstrations of the Crucible Framework - showcasing ensemble voting, request hedging, statistical analysis, and more with mock LLMs

This repository will include Python | Jupyter-Notebook statistical testing | tests and analysis. Highly useful for in depth data analysis & model development.