POLI 450A (Stanford): regression lecture slides, worksheets, R code and data
Statsmodels: statistical modeling and econometrics in Python
A unified framework for machine learning with time series
Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Collection of notebooks about quantitative finance, with interactive python code.
Catch CSS curve balls.
Automate running Lighthouse for every commit, viewing the changes, and preventing regressions
Statistical Machine Intelligence & Learning Engine
π :bar_chart: :bulb: Orange: Interactive data analysis
mlpack: a fast, header-only C++ machine learning library
Portfolio Optimization in Python
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
NeuralProphet: A simple forecasting package
Scalable and user friendly neural :brain: forecasting algorithms.
Symbolic Learning in Python and Julia
β¨ Jest matcher for image comparisons. Most commonly used for visual regression testing.
simple statistics for node & browser javascript
D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement [ICLR 2025 Spotlight]
πΆοΈ A curated list of resources around the topic: visual regression testing
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
A high-level machine learning and deep learning library for the PHP language.
Enhancing {ggplot2} plots with statistical analysis ππ£
A Julia machine learning framework
A package for the sparse identification of nonlinear dynamical systems from data
Rust library for quantitative finance.
Python AutoML for Trading Systems and Sports Betting
TensorLy: Tensor Learning in Python.
Machine Learning in R
A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.
Fast and customizable framework for automatic ML model creation (AutoML)
A toolkit for time series machine learning and deep learning
Owl - OCaml Scientific Computing @ https://ocaml.xyz
BAyesian Model-Building Interface (Bambi) in Python.
:recycle: Visual Regression Testing tool
The open-source Playwright library for AI browser regression testing with intelligent caching, auto-healing, and multi-model verification.
A contract-driven gRPC regression harness for AI coding agents, with human-approved baselines. Snapshot the valid call chains of an API so an agent can look one up instead of tracing for it. gRPC today, OpenAPI next
Presentation-Ready Data Summary and Analytic Result Tables
:milky_way: The R easystats-project
Trace-native CI/CD for AI agents: grade production traces, cluster failures, freeze them into replayable regression tests, and block PRs that regress. Self-hostable.
Time Series Feature Extraction Library (TSFEL): 60+ statistical, temporal, spectral and fractal features for time series classification, regression and signal processing in Python
Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;
mlr3: Machine Learning in R - next generation
Additional linear models including instrumental variable and panel data models that are missing from statsmodels.
The book every data scientist needs on their desk.
A comprehensive library for machine learning and numerical computing. Apply Machine Learning with Rust leveraging first principles.
Curso de IntroducciΓ³n a Machine Learning con Python
Open source local-first PR scanner that finds dead code, security bugs, secrets, quality regressions, and AI-code mistakes before merge. For first timers refer to https://duriantaco.github.io/skylos/repo-map/
Distributed High-Performance Symbolic Regression in Julia
GeoDa: An introduction to spatial data analysis
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse
Canonical sources for HOL4 theorem-proving system. Branch develop is where βmainline developmentβ occurs; when develop passes our regression tests, master is merged forward to catch up.
Easy regression testing for iOS accessibility
An extension of XGBoost to probabilistic modelling
MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
Module for adding visual regression testing to Cypress
The open source visual testing platform for teams and AI agents. Review the product, not just the code.
Compendium of free ML reading resources
Visual regression testing and accessibility testing
Verifiable handwriting synthesis for failure-driven multimodal training and regression.