Parametric proportional hazards fitting with left truncation and
        right censoring for common families of distributions, piecewise constant 
        hazards, and discrete models. Parametric accelerated failure time models
        for left truncated and right censored data. Proportional hazards
        models for tabular and register data. Sampling of risk sets in Cox 
        regression, selections in the Lexis diagram, bootstrapping. 
        Broström (2022) <doi:10.1201/9780429503764>.

Calculates 15 different goodness of fit criteria. These are; standard deviation ratio (SDR), coefficient of variation (CV), relative root mean square error (RRMSE), Pearson's correlation coefficients (PC), root mean square error (RMSE), performance index (PI), mean error (ME), global relative approximation error (RAE), mean relative approximation error (MRAE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), coefficient of determination (R-squared), adjusted coefficient of determination (adjusted R-squared), Akaike's information criterion (AIC), corrected Akaike's information criterion (CAIC), Mean Square Error (MSE), Bayesian Information Criterion (BIC) and Normalized Mean Square Error (NMSE).

A tool for the preparation and enrichment of health datasets for analysis (Toner et al. (2023) <doi:10.1093/gigascience/giad030>). Provides functionality for assessing data quality and for improving the reliability and machine interpretability of a dataset. 
     'eHDPrep' also enables semantic enrichment of a dataset where metavariables are discovered from the relationships between input variables determined from user-provided ontologies.

By overloading the R help() function, this package allows users to use "docstring" style comments within their own defined functions. The package also provides additional functions to mimic the R basic example() function and the prototyping of packages.

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>.