R functions for project setup, data cleaning, machine learning, SuperLearner, parallelization, and targeted learning.
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Updated
Jul 23, 2024 - R
R functions for project setup, data cleaning, machine learning, SuperLearner, parallelization, and targeted learning.
Workshop (2-6 hours): cleaning, missing value imputation, EDA, ensemble learning, calibration, variable importance ranking, accumulated local effect plots. WIP.
Super LeArner Predictions using NAb Panels
SuperLearner R package: prediction model ensembling method
A collection of additional screening algorithms for SuperLearner
Implementing Gradient Boosting & SuperLearner in R and compare the classification accuracy of the two methods.
Hack Aotearoa 2020
npRR: Model-robust inference for the conditional relative risk function using targeted machine learning
Ensembled Feature Selection using Cross-Validated SuperLearner
This project aims to predict heart failure outcomes by applying statistical learning algorithms. The goal is to improve the prediction accuracy through the SuperLearner algorithm.
R code for evaluating adult HIV incidence, health, & implementation outcomes for the first phase of the SEARCH Study (https://www.searchendaids.com/). Full statistical analysis plan available at https://arxiv.org/abs/1808.03231
Ensemble feature ranking for SuperLearner variable selection
Corresponding code guide to the tutorial paper "Introducing longitudinal modified treatment policies: a unified framework for studying complex exposures" (Hoffman et al., 2023)
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