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InvStablePrior
View on CRAN: Click
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Download and install InvStablePrior package within the R console
Install from CRAN:
install.packages("InvStablePrior")
Install from Github:
library("remotes")
install_github("cran/InvStablePrior")
Install by package version:
library("remotes")
install_version("InvStablePrior", "0.1.1")
Attach the package and use:
library("InvStablePrior")
Maintained by
Dexter Cahoy
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-08-23
Latest Update: 2023-08-21
Description:
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) . The package can also be used as a teaching demo for introductory Bayesian courses.
How to cite:
Dexter Cahoy (2022). InvStablePrior: Inverse Stable Prior for Widely-Used Exponential Models. R package version 0.1.1, https://cran.r-project.org/web/packages/InvStablePrior. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.1.0 (2022-08-23 14:30)
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Complete documentation for InvStablePrior
Functions, R codes and Examples using
the InvStablePrior R package
Some associated functions: InvStablePrior . isdexp . isexp . ishalfn . isinvgam . ispoi .
Some associated R codes: InvStablePrior.R . isDexp.R . isExp.R . isHalfN.R . isInvGam.R . isPoi.R . Full InvStablePrior package functions and examples
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