InvStablePrior - Inverse Stable Prior for Widely-Used Exponential Models
Contains functions that allow Bayesian inference on a
parameter of some widely-used exponential models. The functions
can generate independent samples from the closed-form posterior
distribution using the inverse stable prior. Inverse stable is
a non-conjugate prior for a parameter of an exponential
subclass of discrete and continuous data distributions (e.g.
Poisson, exponential, inverse gamma, double exponential
(Laplace), half-normal/half-Gaussian, etc.). The prior class
provides flexibility in capturing a wide array of prior beliefs
(right-skewed and left-skewed) as modulated by a parameter that
is bounded in (0,1). The generated samples can be used to
simulate the prior and posterior predictive distributions. More
details can be found in Cahoy and Sedransk (2019)
<doi:10.1007/s42519-018-0027-2>. The package can also be used
as a teaching demo for introductory Bayesian courses.
