Utilities that support the usage of 'pyDarwin' (<https://certara.github.io/pyDarwin/>) for ease of setup 
    and execution of a machine learning based pharmacometric model search with Certara's Non-Linear Mixed Effects (NLME) 
    modeling engine.

Facilitate Pharmacokinetic (PK) and Pharmacodynamic (PD) modeling
    and simulation with powerful tools for Nonlinear Mixed-Effects (NLME)
    modeling. The package provides access to the same advanced Maximum Likelihood
    algorithms used by the NLME-Engine in the Phoenix platform. These tools support
    a range of analyses, from parametric methods to individual and pooled data, and
    support integrated use within the Pirana pharmacometric workbench
    <doi:10.1002/psp4.70067>. Execution is supported both locally or on
    remote machines.

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.