High-Throughput Phenotyping with EHR using a Common Automated Pipeline R package PheCAP version 1.2.1 Implement surrogate-assisted feature extraction (SAFE) and common machine learning approaches to train and validate phenotyping models. Background and details about the methods can be found at Zhang et al. (2019) <doi:10.1038/s41596-019-0227-6>, Yu et al. (2017) <doi:10.1093/jamia/ocw135>, and Liao et al. (2015) <doi:10.1136/bmj.h1885>. Software Depends: R (>= 3.3.0) Imports: graphics, methods, stats, utils, glmnet, RMySQL Suggests: ggplot2, e1071, randomForestSRC, xgboost, knitr, rmarkdown PARSE LTD <software@parse-health.org> Comprehensive R Archive Network (CRAN) Yichi Zhang [aut], Chuan Hong [aut], Tianxi Cai [aut], PARSE LTD [aut, cre] GPL-3 2020-09-17 application/tgz https://CRAN.R-project.org/package=PheCAP doi:10.32614/CRAN.package.PheCAP