Generator and density function for the
  Generalized Inverse Gaussian (GIG) distribution.

Provides a simple to use, intuitive, and
  extensible interface to several stochastic simulation
  algorithms for generating simulated trajectories of finite
  population continuous-time model. Currently it implements
  Gillespie's exact stochastic simulation algorithm (Direct
  method) and several approximate methods (Explicit tau-leap,
  Binomial tau-leap, and Optimized tau-leap). The package also
  contains a library of template models that can be run as demo
  models and can easily be customized and extended. Currently the
  following models are included, 'Decaying-Dimerization' reaction
  set, linear chain system, logistic growth model, 'Lotka'
  predator-prey model, Rosenzweig-MacArthur predator-prey model,
  'Kermack-McKendrick' SIR model, and a 'metapopulation' SIRS model.
  Pineda-Krch et al. (2008) <doi:10.18637/jss.v025.i12>.

A fast, scalable, and versatile framework for
    simulating large systems with Gillespie's Stochastic Simulation
    Algorithm ('SSA').  This package is the spiritual successor to the
    'GillespieSSA' package originally written by Mario Pineda-Krch.
    Benefits of this package include major speed improvements (>100x),
    easier to understand documentation, and many unit tests that try to
    ensure the package works as intended. Cannoodt and Saelens et al. (2021) 
    <doi:10.1038/s41467-021-24152-2>.

Several methods may be found for selecting a subset of regressors from a set of k candidate variables in multiple linear regression.
        One possibility is to evaluate all possible regression models and comparing them using Mallows's Cp statistic (Cp) according to Gilmour original study.
        Full model is calculated, all possible combinations of regressors are generated, adjusted Cp for each submodel are computed, and the submodel with the minimum adjusted value Cp (ModelMin) is calculated.
        To identify the final model, the package applies a sequence of hypothesis tests on submodels nested within ModelMin, following the approach outlined in Gilmour's original paper.
        For more details see the help of the function final_model() and the original study (1996) <doi:10.2307/2348411>.