A series of functions in some way considered useful to the author.
These include functions for subsetting tables and generating indices for
arrays, conditioning and intervening in probability distributions,
generating combinations and more...

A series of functions in some way considered useful to the author.  These
include methods for subsetting tables and generating indices for arrays,
conditioning and intervening in probability distributions, generating
combinations, fast transformations, and more...

A series of useful functions, some available in different
forms in other packages, but which have been extended or
modified in some way considered useful to the author.

A series of useful functions, some available in different
forms in other packages, but which have been extended, sped up, or
otherwise modified in some way considered useful to the author.

c(person("Nick", "Gelling", email="ngelling@maths.otago.ac.nz",
role=c("aut","cre")),
person(c("Matthew", "R."), "Schofield",
email="mschofield@maths.otago.ac.nz", role="aut"),
person(c("Richard", "J."), "Barker",
email="rbarker@maths.otago.ac.nz", role="aut"))
Performs reversible-jump MCMC (Green, 1995)
<doi:10.2307/2337340>, specifically the restriction introduced by
Barker & Link (2013) <doi:10.1080/00031305.2013.791644>. By utilising
a 'universal parameter' space, RJMCMC is treated as a Gibbs sampling
problem. Previously-calculated posterior distributions are used to
quickly estimate posterior model probabilities. Jacobian matrices are
found using automatic differentiation.
Nick Gelling [aut, cre],
Matthew R. Schofield [aut],
Richard J. Barker [aut]

Performs reversible-jump Markov chain Monte Carlo (Green, 1995)
<doi:10.2307/2337340>, specifically the restriction introduced by
Barker & Link (2013) <doi:10.1080/00031305.2013.791644>. By utilising
a 'universal parameter' space, RJMCMC is treated as a Gibbs sampling
problem. Previously-calculated posterior distributions are used to
quickly estimate posterior model probabilities. Jacobian matrices are
found using automatic differentiation.