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 |
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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