Utilize the 'shiny' interface to generate Goodness of Fit (GOF) plots and tables
      for Non-Linear Mixed Effects (NLME / NONMEM) pharmacometric models. From the interface,
      users can customize model diagnostics and generate the underlying R code to reproduce the 
      diagnostic plots and tables outside of the 'shiny' session. Model diagnostics can be included
      in a 'rmarkdown' document and rendered to desired output format.

Perform Nonlinear Mixed-Effects (NLME) Modeling using Certara's
    NLME-Engine. Access the same Maximum Likelihood engines used in the Phoenix
    platform, including algorithms for parametric methods, individual, and pooled
    data analysis.
    The Quasi-Random Parametric Expectation-Maximization Method (QRPEM) is also
    supported <https://www.page-meeting.org/default.asp?abstract=2338>. Execution
    is supported both locally or on remote machines. Remote execution includes
    support for Linux Sun Grid Engine (SGE), Simple Linux Utility for Resource Management
    (SLURM) grids, Linux and Windows multicore, and individual runs.

A convenient set of wrapper functions to install
    pharmacometric packages and Shiny applications developed by Certara
    PMX and Integrated Drug Development (iDD). The functions ensure the
    successful installation of packages from non-standard repositories.

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.