Density, distribution function, quantile function, 
    and random generation function based on Kittipong Klinjan,Tipat Sottiwan and Sirinapa Aryuyuen (2024)<DOI:10.28919/cmbn/8833>.

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.

Provides tools for designing and analyzing agricultural
    experiments. It includes functions for generating randomized
    treatment layouts for standard experimental designs such as
    Completely Randomized Design (CRD), Randomized Block Design (RBD),
    Latin Square Design (LSD), Factorial Randomized Block Design
    (FRBD), split-plot design, and strip-plot design. The package
    implements one-factor and two-factor analysis of variance (ANOVA)
    and offers multiple comparison procedures, including Least
    Significant Difference (lsd), Tukey, and Duncan tests, to compare
    treatment means in single-factor and factorial experiments. The
    methods follow classical experimental design principles described 
    in Gomez and Gomez (1984, Statistical Procedures for Agricultural Research, John Wiley & Sons, New York).

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.

Perform simultaneous estimation and variable selection for correlated bivariate
    mixed outcomes (one continuous outcome and one binary outcome per cluster) using
    penalized generalized estimating equations. In addition, clustered Gaussian and binary
    outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported.
    Cross-validation can be performed to find the optimal regularization parameter(s).