sl3 - Pipelines for Machine Learning and Super Learning
A modern implementation of the Super Learner prediction algorithm, coupled with a general purpose framework for composing arbitrary pipelines for machine learning tasks.
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data-scienceensemble-learningensemble-modelmachine-learningmodel-selectionregressionstackingstatistics
9.67 score 108 stars 4 dependents 2.6k scriptshal9001 - The Scalable Highly Adaptive Lasso
A scalable implementation of the highly adaptive lasso algorithm, including routines for constructing sparse matrices of basis functions of the observed data, as well as a custom implementation of Lasso regression tailored to enhance efficiency when the matrix of predictors is composed exclusively of indicator functions. For ease of use and increased flexibility, the Lasso fitting routines invoke code from the 'glmnet' package by default. The highly adaptive lasso was first formulated and described by MJ van der Laan (2017) <doi:10.1515/ijb-2015-0097>, with practical demonstrations of its performance given by Benkeser and van der Laan (2016) <doi:10.1109/DSAA.2016.93>. This implementation of the highly adaptive lasso algorithm was described by Hejazi, Coyle, and van der Laan (2020) <doi:10.21105/joss.02526>.
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cross-validationlasso-regressionmachine-learning-algorithmsnonparametric-regressioncpp
9.53 score 49 stars 6 dependents 381 scripts 2.5k downloadstmle3 - The Extensible TMLE Framework
A general framework supporting the implementation of targeted maximum likelihood estimators (TMLEs) of a diverse range of statistical target parameters through a unified interface. The goal is that the exposed framework be as general as the mathematical framework upon which it draws.
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causal-inferencemachine-learningtargeted-learningvariable-importance
8.29 score 43 stars 3 dependents 2.0k scriptsdelayed - A Framework for Parallelizing Dependent Tasks
Mechanisms to parallelize dependent tasks in a manner that optimizes the compute resources available. It provides access to "delayed" computations, which may be parallelized using futures. It is, to an extent, a facsimile of the 'Dask' library (<https://www.dask.org/>), for the 'Python' language.
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parallel-computing
6.88 score 23 stars 5 dependents 44 scripts 855 downloads