Loading NONMEM (NONlinear Mixed-Effect Modeling, <https://www.iconplc.com/solutions/technologies/nonmem/>) and PSN (Perl-speaks-NONMEM, <https://uupharmacometrics.github.io/PsN/>) output files to extract parameter estimates, provide visual predictive check (VPC) and goodness of fit (GOF) plots, and simulate with parameter uncertainty.

'NONMEM' has been a tool for running nonlinear mixed
    effects models since the 80s and is still used today (Bauer 2019
    <doi:10.1002/psp4.12404>). This tool allows you to convert
    'NONMEM' models to 'rxode2' (Wang, Hallow and James (2016)
    <doi:10.1002/psp4.12052>) and with simple models 'nlmixr2' syntax
    (Fidler et al (2019) <doi:10.1002/psp4.12445>). The 'nlmixr2'
    syntax requires the residual specification to be included and it
    is not always translated. If available, the 'rxode2' model will
    read in the 'NONMEM' data and compare the simulation for the
    population model ('PRED') individual model ('IPRED') and residual
    model ('IWRES') to immediately show how well the translation is
    performing. This saves the model development time for people who are
    creating an 'rxode2' model manually.  Additionally, this package reads
    in all the information to allow simulation with uncertainty (that is the
    number of observations, the number of subjects, and the covariance
    matrix) with a 'rxode2' model.  This is complementary to the
    'babelmixr2' package that translates 'nlmixr2' models to 'NONMEM' and can
    convert the objects converted from 'nonmem2rx' to a full 'nlmixr2' fit.

Systematically creates and modifies NONMEM(R) control streams. Harvests
 NONMEM output, builds run logs, creates derivative data, generates diagnostics.
 NONMEM (ICON Development Solutions <https://www.iconplc.com/>) is software for 
 nonlinear mixed effects modeling. See 'package?nonmemica'.

Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0),
  using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in
  (Li, Can, 2013, <https://www.hindawi.com/journals/jam/2013/986317/abs/>).