sureLDA: A Novel Multi-Disease Automated Phenotyping Method for the EHR

A statistical learning method to simultaneously predict a range of target phenotypes using codified and natural language processing (NLP)-derived Electronic Health Record (EHR) data. See Ahuja et al (2020) JAMIA <doi:10.1093/jamia/ocaa079> for details.

Version: 0.1.0-1
Depends: R (≥ 3.0), Matrix
Imports: pROC, glmnet, MAP, Rcpp, foreach, doParallel
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2020-11-10
Author: Yuri Ahuja [aut, cre], Tianxi Cai [aut], PARSE LTD [aut]
Maintainer: Yuri Ahuja <Yuri_Ahuja at hms.harvard.edu>
BugReports: https://github.com/celehs/sureLDA/issues
License: GPL-3
URL: https://github.com/celehs/sureLDA
NeedsCompilation: yes
Materials: README
CRAN checks: sureLDA results

Downloads:

Reference manual: sureLDA.pdf
Vignettes: Simulated Example
Package source: sureLDA_0.1.0-1.tar.gz
Windows binaries: r-devel: sureLDA_0.1.0-1.zip, r-release: sureLDA_0.1.0-1.zip, r-oldrel: sureLDA_0.1.0-1.zip
macOS binaries: r-release: sureLDA_0.1.0-1.tgz, r-oldrel: sureLDA_0.1.0-1.tgz

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