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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
DOI: 10.32614/CRAN.package.sureLDA
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:

Please use the canonical form https://CRAN.R-project.org/package=sureLDA to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.