Computes p-values based on (a) Satterthwaite or
    Kenward-Rogers degree of freedom methods and (b) parametric bootstrap
    for mixed effects models as implemented in the 'lme4'
    package. Implements parametric bootstrap test for generalized linear
    mixed models as implemented in 'lme4' and generalized linear
    models. The package is documented in the paper by Halekoh and
    Højsgaard, (2012, <doi:10.18637/jss.v059.i09>).  Please see
    'citation("pbkrtest")' for citation details.

Bivariate additive categorical regression via penalized maximum likelihood. 
             Under a multinomial framework, the method fits bivariate models where both 
             responses are nominal, ordinal, or a mix of the two. Partial proportional 
             odds models are supported, with flexible (non-)uniform association structures. 
             Various logit types and parametrizations can be specified for both marginals 
             and the association, including Dale’s model. The association structure can 
             be regularized using polynomial-type penalty terms. Additive effects are 
             modeled using P-splines. Standard methods such as summary(), residuals(), 
             and predict() are available.

Binding models which are useful when analysing protein-ligand interactions by techniques such as Biolayer Interferometry (BLI) or Surface Plasmon Resonance (SPR). Naman B. Shah, Thomas M. Duncan (2014) <doi:10.3791/51383>. Hoang H. Nguyen et al. (2015) <doi:10.3390/s150510481>. After initial binding parameters are known, binding curves can be simulated and parameters can be varied. The models within this package may also be used to fit a curve to measured binding data using non-linear regression.

A light-weight package helps you track and visualize
  the progress of parallel version of vectorized R functions (mc*apply).
  Parallelization (mc.core > 1) works only on *nix (Linux, Unix such as macOS) system due to
  the lack of fork() functionality, which is essential for mc*apply, on Windows.