tech, statistics, machine learning, computer simulation, numerical optimization
- nnetsauce for Python and nnetsauce for R Statistical/Machine Learning using Randomized and Quasi-Randomized (neural) networks. Read https://thierrymoudiki.github.io/blog/#QuasiRandomizedNN.
- ahead for Python, ahead for R and ahead for Julia Univariate and Multivariate time series forecasting with uncertainty quantification (including simulation). Home of:
dynrmandridge2. Read https://thierrymoudiki.github.io/blog/2024/02/26/python/r/julia/ahead-v0100.
- mlsauce for Python and mlsauce for R Miscellaneous Statistical/Machine Learning stuff. Home of LSBoost: https://www.researchgate.net/publication/346059361_LSBoost_gradient_boosted_penalized_nonlinear_least_squares and `GenericBooster`: https://thierrymoudiki.github.io/blog/2024/10/06/python/r/genericboosting and https://www.researchgate.net/publication/386212136_Scalable_Gradient_Boosting_using_Randomized_Neural_Networks. Read https://thierrymoudiki.github.io/blog/2023/11/05/python/r/adaopt/lsboost/mlsauce_classification.
- GPopt Bayesian optimization using Gaussian Process Regression (useful for Machine learning hyperparameter tuning). Read https://thierrymoudiki.github.io/blog/2024/01/29/python/gpopt-new.
- bcn for Python and bcn for R Boosted Configuration (neural) Networks for supervised learning. Read https://thierrymoudiki.github.io/blog/2022/07/21/r/misc/boosted-configuration-networks and https://www.researchgate.net/publication/380760578_Boosted_Configuration_neural_Networks_for_supervised_classification.
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genbooster A fast gradient boosting and bagging (RandomBagClassifier, similar to RandomForestClassifier) implementation using Rust and Python. Any base learner can be employed. See https://thierrymoudiki.github.io/blog/2025/01/27/python/r/genbooster-rust
- unifiedbooster Unified interface for Gradient Boosted Decision Trees algorithms. Read https://thierrymoudiki.github.io/blog/2024/08/05/python/r/unibooster
- teller Model-agnostic Statistical/Machine Learning explainability. Read https://thierrymoudiki.github.io/blog/2024/02/19/python/quasirandomizednn/explainableml/nnetsauce-dl-data.
- querier A query language for Python Data Frames. Read https://thierrymoudiki.github.io/blog/2022/06/06/python/lsboost/explainableml/mlsauce/techtonique-workflow.
- learningmachine for Python and learningmachine for R. Machine Learning with uncertainty quantification and interpretability. Read https://thierrymoudiki.github.io/blog/2024/04/01/python/learningmachine-python and https://thierrymoudiki.github.io/blog/2024/03/25/r/learningmachine
- esgtoolkit for R and esgtoolkit for Python A toolkit for Monte Carlo Simulations in Finance, Economics, Insurance, Physics. Read https://thierrymoudiki.github.io/blog/2023/12/18/r/python/esgtoolkit-python.
- rtopy Calling R functions in Python. Read https://thierrymoudiki.github.io/blog/2024/03/04/python/r/rtopyintro.
- crossvalidation Generic R functions for cross-validation. Read https://thierrymoudiki.github.io/blog/2021/08/06/r/crossvalidation-svm-r.
- glmnet for python Lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, poisson regression and the cox model. Read https://thierrymoudiki.github.io/blog/2024/11/18/python/r/GLMNet-post
- survivalist Model-agnostic Survival analysis with Machine Learning and uncertainty quantification. See https://thierrymoudiki.github.io/blog/2025/02/12/r/R-agnostic-survival-analysis
- tisthemachinelearner Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor. See https://thierrymoudiki.github.io/blog/2025/02/17/python/r/tisthemllearner
- mlreserving Machine learning-based probabilistic reserving model for (longitudinal data) insurance claims. See [https://thierrymoudiki.github.io/blog/2025/06/06/python/ml-reserve-advanced-models](https://thierrymoudiki.github.io/blog/2025/06/06/python/ml-reserve-advanced-models)
- cybooster A high-performance generic gradient boosting (any based learner can be used, and is randomized at each boosting iteration) library designed for classification and regression tasks (can also be used in conjunction with [nnetsauce](https://github.com/Techtonique/nnetsauce)'s `MTS` for time series forecasting). It is built on Cython (that is, C) for speed and efficiency. This version will also be more GPU friendly, thanks to JAX, making it suitable for large datasets
