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.
| Version: |
0.1.0 |
| Imports: |
stats, fdrtool, nimble |
| Published: |
2022-08-23 |
| Author: |
Dexter Cahoy [aut, cre],
Joseph Sedransk [aut] |
| Maintainer: |
Dexter Cahoy <dexter.cahoy at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| CRAN checks: |
InvStablePrior results |
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