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: dynrm and ridge2. 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), and not only. Read https://thierrymoudiki.github.io/blog/2024/01/29/python/gpopt-new. or https://thierrymoudiki.github.io/blog/2024/12/09/python/bayesconfoptim  
  
  


• 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.  
  
  


• genbooster   A fast gradient boosting and bagging (RandomBagClassifier) implementation using Rust and Python. Any base learner can be employed. See blog post and blog post.
  
  


• 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 in Python   tisthemachinelearner in R   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
  
  


• cybooster   A high-performance generic gradient boosting library (any base learner can be used, randomized at each iteration), built on Cython and GPU-friendly via JAX.
  
  


• synthe   Synthetic data generation
  
  


• unifiedml   A Unified Machine Learning Interface for R