This function can be used in the family argument of create_sampler
or generate_data to specify a Gamma sampling distribution.
f_gamma(
link = "log",
shape.vec = ~1,
shape.prior = pr_gamma(0.1, 0.1),
control = set_MH(type = "RWLN", scale = 0.2, adaptive = TRUE)
)A family object.
the name of a link function. Currently the only allowed link function
for the gamma distribution is "log".
optional formula specification of unequal shape parameter.
prior for gamma shape parameter. Supported prior distributions:
pr_fixed with a default value of 1, pr_exp and
pr_gamma. The current default is pr_gamma(shape=0.1, rate=0.1).
options for the Metropolis-Hastings algorithm employed
in case the shape parameter is to be inferred. Function set_MH
can be used to change the default options. The two choices of proposal
distribution type supported are "RWLN" for a random walk proposal on the
log-shape scale, and "gamma" for an approximating gamma proposal, found using
an iterative algorithm. In the latter case, a Metropolis-Hastings accept-reject
step is currently omitted, so the sampling algorithm is an approximate one,
though often quite accurate and efficient.
J.W. Miller (2019). Fast and Accurate Approximation of the Full Conditional for Gamma Shape Parameters. Journal of Computational and Graphical Statistics 28(2), 476-480.