Run a Function Iteratively While Varying Parameters (paramtest)
Run simulations or other functions while easily varying parameters from one iteration to the next. Some common use cases would be grid search for machine learning algorithms, running sets of simulations (e.g., estimating statistical power for complex models), or bootstrapping under various conditions. See the ‘paramtest’ documentation for more information and examples.

Matrix Variate Distributions (matdist)
It provides tools for computing densities and generating random samples from matrix variate distributions, including matrix normal, Wishart, matrix Student-t, matrix Dirichlet and matrix beta distributions. For complete disposition, see Gupta and Nagar (1999) <ISBN:978-1584880462>.

Bayesian Variable Selection with Shrinking and Diffusing Priors (basad)
Provides a Bayesian variable selection approach using continuous spike and slab prior distributions. The prior choices here are motivated by the shrinking and diffusing priors studied in Narisetty & He (2014) <DOI:10.1214/14-AOS1207>.

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