Application of genome prediction for a continuous variable, focused 
             on genotype by environment (GE) genomic selection models (GS). It consists a group of functions 
             that help to create regression kernels for some GE genomic models proposed by Jarquín et al. (2014) <doi:10.1007/s00122-013-2243-1>
             and Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>. Also, it computes genomic predictions based on Bayesian approaches.
             The prediction function uses an orthogonal transformation of the data and specific priors
             present by Cuevas et al. (2014) <doi:10.1534/g3.114.013094>.

Fit Bayesian Gaussian graphical models. The methods are separated into 
    two Bayesian approaches for inference: hypothesis testing and estimation. There are 
    extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, 
    and node wise predictability. These methods were recently introduced in the Gaussian 
    graphical model literature, including 
    Williams (2019) <doi:10.31234/osf.io/x8dpr>, 
    Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>,
    Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

Provides a Metropolis-coupled Markov chain Monte Carlo sampler,
    post-processing and parameter estimation functions, and plotting utilities
    for the generalized graded unfolding model of Roberts, Donoghue, and
    Laughlin (2000) <doi:10.1177/01466216000241001>.

A collection of box-geometry model (BGM) files for the Atlantis 
    ecosystem model. Atlantis is a deterministic, biogeochemical, 
    whole-of-ecosystem model (see <http://atlantis.cmar.csiro.au/> for more information).

Provides functions for behavior genetics analysis, 
    including variance component model identification [Hunter et al. (2021) <doi:10.1007/s10519-021-10055-x>],
    calculation of relatedness coefficients using path-tracing methods 
    [Wright (1922) <doi:10.1086/279872>; McArdle & McDonald (1984) <doi:10.1111/j.2044-8317.1984.tb00802.x>], 
    inference of relatedness, pedigree conversion, and simulation of multi-generational family data 
    [Lyu et al. (2024) <doi:10.1101/2024.12.19.629449>]. For a full overview, 
    see [Garrison et al. (2024) <doi:10.21105/joss.06203>].