origami - Generalized Framework for Cross-Validation
A general framework for the application of cross-validation schemes to particular functions. By allowing arbitrary lists of results, origami accommodates a range of cross-validation applications. This implementation was first described by Coyle and Hejazi (2018) <doi:10.21105/joss.00512>.
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cross-validationmachine-learning
10.64 score 27 stars 16 dependents 582 scripts 4.8k downloadshal9001 - 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
9.87 score 49 stars 7 dependents 483 scripts 3.7k downloadssl3 - 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
9.32 score 107 stars 5 dependents 920 scriptstmle3 - 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
7.57 score 43 stars 4 dependents 290 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
6.91 score 23 stars 6 dependents 39 scripts 882 downloadstmle3shift - Targeted Learning of the Causal Effects of Stochastic Interventions
Targeted maximum likelihood estimation (TMLE) of population-level causal effects under stochastic treatment regimes and related nonparametric variable importance analyses. Tools are provided for TML estimation of the counterfactual mean under a stochastic intervention characterized as a modified treatment policy, such as treatment policies that shift the natural value of the exposure. The causal parameter and estimation were described in Díaz and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x> and an improved estimation approach was given by Díaz and van der Laan (2018) <doi:10.1007/978-3-319-65304-4_14>.
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causal-inferencemachine-learningmarginal-structural-modelsstochastic-interventionstargeted-learningtreatment-effectsvariable-importance
5.46 score 17 stars 1 dependents 56 scriptstmle3mopttx - Targeted Maximum Likelihood Estimation of the Mean under Optimal Individualized Treatment
This package estimates the optimal individualized treatment rule for the categorical treatment using Super Learner (sl3). In order to avoid nested cross-validation, it uses split-specific estimates of Q and g to estimate the rule as described by Coyle et al. In addition, it provides the Targeted Maximum Likelihood estimates of the mean performance using CV-TMLE under such estimated rules. This is an adapter package for use with the tmle3 framework and the tlverse software ecosystem for Targeted Learning.
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categorical-treatmentcausal-inferenceheterogeneous-effectsmachine-learningoptimal-individualized-treatmenttargeted-learningvariable-importance
4.38 score 13 stars 1 dependents 61 scriptstmle3mediate - Targeted Learning for Causal Mediation Analysis
Targeted maximum likelihood (TML) estimation of population-level causal effects in mediation analysis. The causal effects are defined by joint static or stochastic interventions applied to the exposure and the mediator. Targeted doubly robust estimators are provided for the classical natural direct and indirect effects, as well as the more recently developed population intervention direct and indirect effects.
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causal-inferencecausal-mediation-analysismachine-learningmediation-analysisstochastic-interventionstargeted-learningtreatment-effects
2.98 score 5 stars 19 scripts