Fortran Statistics and Machine Learning Library
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Updated
Feb 8, 2026 - Fortran
Fortran Statistics and Machine Learning Library
A modern Fortran statistical library.
Statistical evaluation framework for AI agents
AI-powered trading research platform. Test any idea on stocks, futures, and crypto with event studies, backtesting, and statistical validation. MCP server with 8 tools. pip install varrd.
AI Firewall and guardrails for LLM-based Elixir applications
High-performance statistical testing and regression for Polars DataFrames, powered by Rust.
Significant Network Interval Mining
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
Token-efficient stochastic testing for AI agents. 5-20x cost reduction. 10 framework adapters. Paper: arXiv:2603.02601
Fairness and bias detection library for Elixir AI/ML systems
Multivariate analysis (MVA) of high dimensional heterogeneous data
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.
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