Develop Nonlinear Mixed Effects (NLME) models for pharmacometrics using a 'shiny'
      interface. The Pharmacometric Modeling Language (PML) code updates in real time given 
      changes to user inputs. Models can be executed using the 'Certara.RsNLME' package. 
      Additional support to generate the underlying 'Certara.RsNLME' code to recreate
      the corresponding model in R is provided in the user interface.

Execute Nonlinear Mixed Effects (NLME) models for pharmacometrics using a 'shiny'
      interface. Specify engine parameters and select from different run options, including 
      simple estimation, stepwise covariate search, bootstrapping, simulation, visual predictive
      check, and more. Models are executed using the 'Certara.RsNLME' package.

Utilize the 'shiny' interface to parameterize a Visual Predictive Check (VPC),
    including selecting from different binning or binless methods and performing stratification,
    censoring, and prediction correction. Generate the underlying 'tidyvpc' and 'ggplot2' code
    directly from the user interface and download R or Rmd scripts to reproduce the VPCs in R.

Facilitates the creation of 'xpose' data objects from Nonlinear Mixed
      Effects (NLME) model outputs produced by 'Certara.RsNLME' or Phoenix NLME. This
      integration enables users to utilize all 'ggplot2'-based plotting functions available
      in 'xpose' for thorough model diagnostics and data visualization. Additionally, the 
      package introduces specialized plotting functions tailored for covariate model
      evaluation, extending the analytical capabilities beyond those offered by 'xpose' alone.

Provides tools to easily access and analyze Canadian Election Study data.
    The package simplifies the process of downloading, cleaning, and using 'CES' datasets
    for political science research and analysis. The Canadian Election Study ('CES')
    has been conducted during federal elections since 1965, surveying Canadians
    on their political preferences, engagement, and demographics. Data is accessed
    from multiple sources including the 'Borealis' Data repository 
    <https://borealisdata.ca/> and the official 'Canadian Election Study' website 
    <https://ces-eec.arts.ubc.ca/>. This package is not officially affiliated 
    with the Canadian Election Study, 'Borealis' Data, or the University of British 
    Columbia, and users should cite the original data sources in their work.