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Variational Bayes for Latent Patient Phenotypes in Electronic Health Records (EHR)
VBphenoR is an R package for discovering latent patient
phenotypes from realistically large EHR data using Bayesian statistics.
In order to computationally support EHR data, we employ variational
Bayes (VB). Currently, it supports latent class discovery using VB
Gaussian Mixture Model implemented with Coordinate-ascent Variational
Inference (CAVI) and VB Logistic Regression for biomarker levels shifted
for the latent phenotype. Please note this package is still under
development.
Prior to analyzing your EHR data, the R package needs to be
installed. The easiest way to install VBphenoR is through
CRAN:
install.packages("VBphenoR")VBphenoR can also be downloaded and installed via
GitHub. This is most useful for downloading a specific version
of the package (which can be found at https://github.com/buckleybrian/VBphenoR/releases):
devtools::install_github("buckleybrian/VBphenoR@vx.xx.x")
# or
devtools::install_version("VBphenoR", version = "x.x.x", repos = "http://cran.us.r-project.org")The latest developmental version of the package can be downloaded and installed by running:
devtools::install_github("buckleybrian/VBphenoR", build_vignettes = TRUE, build_manual=TRUE)After successful installation, the package must be loaded into the working space:
library(VBphenoR)See the vignettes for usage instructions and example.
VBphenoR is available under the open source MIT License
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.
Health stats visible at Monitor.