Provides the event table and support functions needed for
   'rxode2' (Wang, Hallow and  James (2016) <doi:10.1002/psp4.12052>) and 'nlmixr2'
    (Fidler et al (2019) <doi:10.1002/psp4.12445>).
    This split will reduce computational burden of recompiling 'rxode2'.

Provides the log-likelihoods with gradients from 'stan'
    (Carpenter et al (2015), <arXiv:1509.07164>) needed
    for generalized log-likelihood estimation in 'nlmixr2'
    (Fidler et al (2019) <doi:10.1002/psp4.12445>). This is
    split of to reduce computational burden of recompiling 'rxode2'
    (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) which runs
    the 'nlmixr2' models during estimation.

Provides the parsing needed for 'rxode2' (Wang, Hallow and
    James (2016) <doi:10.1002/psp4.12052>). It also provides the 'stan'
    based advan linear compartment model solutions with gradients
    (Carpenter et al (2015), <arXiv:1509.07164>) needed in 'nlmixr2'
    (Fidler et al (2019) <doi:10.1002/psp4.12445>).
    This split will reduce computational burden of recompiling 'rxode2'.

Provides the random number generation (in parallel) needed for
   'rxode2' (Wang, Hallow and  James (2016) <doi:10.1002/psp4.12052>) and 'nlmixr2'
    (Fidler et al (2019) <doi:10.1002/psp4.12445>).
    This split will reduce computational burden of recompiling 'rxode2'.

Analysis of combined total and allele specific reads from the reciprocal cross study with RNA-seq data.

Functions are provided to calculate and display ridge TRACE Diagnostics for a 
  variety of alternative Shrinkage Paths. While all methods focus on Maximum Likelihood 
  estimation of unknown true effects under normal distribution-theory, some estimates are 
  modified to be Unbiased or to have "Correct Range" when estimating either [1] the noncentrality 
  of the F-ratio for testing that true Beta coefficients are Zeros or [2] the "relative" MSE 
  Risk (i.e. MSE divided by true sigma-square, where the "relative" variance of OLS is known.) 
  The eff.ridge() function implements the "Efficient Shrinkage Path" introduced in Obenchain
  (2022) <Open Statistics>. This "p-Parameter" Shrinkage-Path always passes through the
  vector of regression coefficient estimates Most-Likely to achieve the overall Optimal
  Variance-Bias Trade-Off and is the shortest Path with this property. Functions eff.aug() and
  eff.biv() augment the calculations made by eff.ridge() to provide plots of the bivariate
  confidence ellipses corresponding to any of the p*(p-1) possible ordered pairs of shrunken
  regression coefficients. Functions for plotting TRACE Diagnostics now have more options.