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.
- 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.
- 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.
- 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.
- 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.
- unifiedbooster Unified interface for Gradient Boosted Decision Trees algorithms. Read https://thierrymoudiki.github.io/blog/2024/08/05/python/r/unibooster
- 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
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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
