Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to 
    Mentré F, Mallet A, Baccar D (1997) <doi:10.1093/biomet/84.2.429>,
    Retout S, Comets E, Samson A, Mentré F (2007) <doi:10.1002/sim.2910>,
    Bazzoli C, Retout S, Mentré F (2009) <doi:10.1002/sim.3573>,
    Le Nagard H, Chao L, Tenaillon O (2011) <doi:10.1186/1471-2148-11-326>,
    Combes FP, Retout S, Frey N, Mentré F (2013) <doi:10.1007/s11095-013-1079-3> and
    Seurat J, Tang Y, Mentré F, Nguyen TT (2021) <doi:10.1016/j.cmpb.2021.106126>.

Builds and runs 'c++' code for classes that encapsulate state space model, particle filtering algorithm pairs.
    Algorithms include the Bootstrap Filter from Gordon et al. (1993) <doi:10.1049/ip-f-2.1993.0015>, the generic SISR filter, 
    the Auxiliary Particle Filter from Pitt et al (1999) <doi:10.2307/2670179>, and a variety of Rao-Blackwellized 
    particle filters inspired by Andrieu et al. (2002) <doi:10.1111/1467-9868.00363>. For more details on the 'c++' library 
    'pf', see Brown (2020) <doi:10.21105/joss.02599>.

Provides access to a high performant random distribution
    sampler for the Polya Gamma Distribution using either 'C++' headers for 
    'Rcpp' or 'RcppArmadillo' and 'R'.

This work is an extension of the state space model for Poisson count data, Poisson-Gamma model, towards a semiparametric specification. Just like the generalized additive models (GAM), cubic splines are used for covariate smoothing. The semiparametric models are fitted by an iterative process that combines maximization of likelihood and backfitting algorithm.

Generates random samples from the Polya-Gamma distribution using an implementation of the algorithm described in J. Windle's PhD thesis (2013) <https://repositories.lib.utexas.edu/bitstream/handle/2152/21842/WINDLE-DISSERTATION-2013.pdf>. The underlying implementation is in C.

Fits penalized generalized estimating equations to longitudinal data with high-dimensional covariates.