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

Documentation:

Reference manual:	sureLDA.pdf
Vignettes:	Simulated Example

Downloads:

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 (arm64): sureLDA_0.1.0-1.tgz, r-oldrel (arm64): sureLDA_0.1.0-1.tgz, r-release (x86_64): sureLDA_0.1.0-1.tgz, r-oldrel (x86_64): sureLDA_0.1.0-1.tgz

Linking:

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