Make some distributions from the 'C++' library 'Boost'
    available in 'R'. In addition, the normal-inverse Gaussian distribution
    and the generalized inverse Gaussian distribution are provided. The 
    distributions are represented by 'R6' classes. The method to sample from 
    the generalized inverse Gaussian distribution is the one given in 
    "Random variate generation for the generalized inverse Gaussian 
    distribution" Luc Devroye (2012) <doi:10.1007/s11222-012-9367-z>.

Output formats and utilities for authoring books and technical documents with R Markdown.

Tools for optimal and approximate state estimation as well as
    network inference of Partially-Observed Boolean Dynamical Systems.

Functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks <doi:10.1093/bioinformatics/btq124>.

A C++ library for Bayesian modeling, with an emphasis on Markov
   chain Monte Carlo.  Although boom contains a few R utilities (mainly plotting
   functions), its primary purpose is to install the BOOM C++ library on your
   system so that other packages can link against it.

Provides debugging tools that let you inspect the
    intermediate results of a call. The output looks as if we explode a
    call into its parts hence the package name.

Spike and slab regression with a variety of residual error
  distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a
  few others.  Spike and slab regression is Bayesian regression with prior
  distributions containing a point mass at zero.  The posterior updates the
  amount of mass on this point, leading to a posterior distribution that is
  actually sparse, in the sense that if you sample from it many coefficients are
  actually zeros.  Sampling from this posterior distribution is an elegant way
  to handle Bayesian variable selection and model averaging.  See
  <DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.