Process and analyze electronic health record (EHR) data. The 'EHR'
    package provides modules to perform diverse medication-related studies using
    data from EHR databases. Especially, the package includes modules to perform
    pharmacokinetic/pharmacodynamic (PK/PD) analyses using EHRs, as outlined in
    Choi, Beck, McNeer, Weeks, Williams, James, Niu, Abou-Khalil, Birdwell, Roden, 
    Stein, Bejan, Denny, and Van Driest (2020) <doi:10.1002/cpt.1787>. Additional 
    modules will be added in future. In addition, this package provides various 
    functions useful to perform Phenome Wide Association Study (PheWAS) to explore 
    associations between drug exposure and phenotypes obtained from EHR data, as 
    outlined in Choi, Carroll, Beck, Mosley, Roden, Denny, and Van Driest (2018) 
    <doi:10.1093/bioinformatics/bty306>.

Functions to delineate temporal dataset shifts in Electronic Health 
             Records through the projection and visualization of dissimilarities 
             among data temporal batches. This is done through the estimation of 
             data statistical distributions over time and their projection in 
             non-parametric statistical manifolds, uncovering the patterns of the 
             data latent temporal variability. 'EHRtemporalVariability' is 
             particularly suitable for multi-modal data and categorical variables 
             with a high number of values, common features of biomedical data where 
             traditional statistical process control or time-series methods may not 
             be appropriate. 'EHRtemporalVariability' allows you to explore and 
             identify dataset shifts through visual analytics formats such as 
             Data Temporal heatmaps and Information Geometric Temporal (IGT) plots. 
             An additional 'EHRtemporalVariability' Shiny app can be used to load 
             and explore the package results and even to allow the use of these 
             functions to those users non-experienced in R coding. (Sáez et al. 2020)
             <doi:10.1093/gigascience/giaa079>.