healthcareai: Tools for Healthcare Machine Learning

Aims to make machine learning in healthcare as easy as possible. You can develop customized, reliable, high-performance machine learning models with minimal code. Models are created with automatic preprocessing, hyperparameter tuning, and algorithm selection (between 'xgboost' Chen, T. & Guestrin, C. (2016) <arXiv:1603.02754>, 'ranger' Wright, M. N., & Ziegler, A. (2017) <doi:10.18637/jss.v077.i01>, and 'glm' Friedman J, Hastie T, Tibshirani R. (2010) <doi:10.18637/jss.v033.i01>) so that they can be easily put into production. Additionally, there are tools to help understand how a model makes its predictions, select prediction threshholds for operational use, and evaluate model performance over time. Code uses 'tidyverse' syntax and most methods have an associated visualization.

Version: 2.5.1
Depends: R (≥ 3.6), methods
Imports: caret (≥ 6.0.81), cowplot, data.table, dplyr (≥ 1.0.0), e1071, generics, ggplot2, glmnet, lubridate, MLmetrics, purrr, ranger (≥ 0.8.0), recipes (≥, rlang, ROCR, stringr, tibble (≥ 3.0.0), tidyr, xgboost
Suggests: covr, DBI, dbplyr, lintr, odbc, testthat
Published: 2022-09-05
Author: Levi Thatcher [aut], Michael Levy [aut], Mike Mastanduno [aut, cre], Taylor Larsen [aut], Taylor Miller [aut], Rex Sumsion [aut]
Maintainer: Mike Mastanduno <michael.mastanduno at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: healthcareai results


Reference manual: healthcareai.pdf


Package source: healthcareai_2.5.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): healthcareai_2.5.1.tgz, r-oldrel (arm64): healthcareai_2.5.1.tgz, r-release (x86_64): healthcareai_2.5.1.tgz, r-oldrel (x86_64): healthcareai_2.5.1.tgz
Old sources: healthcareai archive


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