InvStablePrior

InvStablePrior-package

Contains random number generation, plotting, and estimation algorithms for performing Bayesian inference on a parameter of some widely-used exponential data models.
The package contains algorithms for the inverse stable-Poisson, inverse stable-exponential, inverse stable-double exponential, inverse stable-inverse gamma, and inverse stable-half-normal 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.

Dexter Cahoy

Inverse Stable Prior for Widely-Used Exponential Models

Joseph Sedransk

InvStablePrior function

Dexter Cahoy <a href="/link/cahoyd%40uhd.edu?package=InvStablePrior&version=0.1.1" data-mini-rdoc="InvStablePrior::cahoyd@uhd.edu">cahoyd@uhd.edu</a>

Author

InvStablePrior Package — InvStablePrior

Dexter Cahoy <a href='mailto:cahoyd@uhd.edu'>cahoyd@uhd.edu</a>

InvStablePrior: InvStablePrior Package

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