Hospital machine learning and ai data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include predicting length of stay, and readmits. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.
|Depends:||R (≥ 3.3)|
|Imports:||magrittr, rlang (≥ 0.1.2), yardstick (≥ 0.0.8), utils, broom, ggrepel, tibble, dplyr, ggplot2, tidyr, forcats, recipes (≥ 1.0.0), purrr, h2o, stats, dials, parsnip, tune, workflows, modeltime|
|Suggests:||rmarkdown, knitr, roxygen2, healthyR.data, scales, tidyselect, janitor, timetk, plotly, rsample, kknn, hardhat, uwot, stringr|
|Author:||Steven Sanderson [aut, cre, cph]|
|Maintainer:||Steven Sanderson <spsanderson at gmail.com>|
|License:||MIT + file LICENSE|
|CRAN checks:||healthyR.ai results|
Auto K-Means with healthyR.ai
Getting Started with healthyR.ai
Clustering with K-Means and UMAP
|Windows binaries:||r-devel: healthyR.ai_0.0.13.zip, r-release: healthyR.ai_0.0.13.zip, r-oldrel: healthyR.ai_0.0.13.zip|
|macOS binaries:||r-release (arm64): healthyR.ai_0.0.13.tgz, r-oldrel (arm64): healthyR.ai_0.0.13.tgz, r-release (x86_64): healthyR.ai_0.0.13.tgz, r-oldrel (x86_64): healthyR.ai_0.0.13.tgz|
|Old sources:||healthyR.ai archive|
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