StatsForecast is a production-grade Python library for large-scale statistical time series forecasting. It provides a scikit-learn-like API (fit/predict) with over 30 models including AutoARIMA, AutoETS, AutoCES, AutoTheta, TBATS, MSTL, GARCH, and intermittent demand models. The library achieves high performance through a hybrid architecture: core algorithms are implemented in C++ using Eigen and pybind11, while Python handles orchestration, model selection, and validation. It supports distributed computing via Fugue (Spark, Dask, Ray) and multiprocessing, enabling forecasting on millions of time series. The library also includes conformal prediction intervals, cross-validation, simulation, and feature engineering utilities. While primarily an application library, the repository has a strong research component with extensive benchmarking experiments against R's forecast package, pmdarima, Prophet, NeuralProphet, and Amazon Forecast. These experiments are well-documented with reproducible environments (Docker, conda, Makefiles) and use public datasets (M3, M4, M5, Tourism, ERCOT). The codebase is actively maintained with CI/CD across multiple OS/Python versions, automated wheel building, and documentation pipelines using Quarto and Mintlify. The architecture is clean and modular: models inherit from a common base class (_TS), distributed backends implement a ParallelBackend interface, and C++ code is separated into submodules. Notable reusable components include a generic Nelder-Mead optimizer, a unified distribution framework, and a stepwise ARIMA search algorithm ported from R. The library is well-tested with comprehensive unit and integration tests.
EconML is a Python library for estimating heterogeneous treatment effects (CATE) from observational and experimental data using machine learning. It implements state-of-the-art causal inference methods including Double/Debiased Machine Learning (DML), Generalized Random Forests (GRF), Orthogonal Random Forests, Doubly Robust Learners, Instrumental Variable estimators (DMLIV, DRIV, OrthoIV), and Meta-Learners (S, T, X, DA). The library provides a unified scikit-learn-compatible API across all estimators with fit, effect, marginal_effect, and inference methods (confidence intervals, p-values). It includes advanced features such as cross-fitting, sample weights, missing data support, federated learning, sensitivity analysis, and model selection via RScorer. The repository also contains extensive documentation, notebooks, Monte Carlo tests, and a Cython-optimized tree implementation for performance.