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PEPBVS  

Bayesian Variable Selection using Power-Expected-Posterior Prior
View on CRAN: Click here


Download and install PEPBVS package within the R console
Install from CRAN:
install.packages("PEPBVS")

Install from Github:
library("remotes")
install_github("cran/PEPBVS")

Install by package version:
library("remotes")
install_version("PEPBVS", "2.1")



Attach the package and use:
library("PEPBVS")
Maintained by
Konstantina Charmpi
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-09-19
Latest Update: 2023-09-19
Description:
Performs Bayesian variable selection under normal linear models for the data with the model parameters following as prior either the power-expected-posterior (PEP) or the intrinsic (a special case of the former) (Fouskakis and Ntzoufras (2022) , Fouskakis and Ntzoufras (2020) ). The prior distribution on model space is the uniform on model space or the uniform on model dimension (a special case of the beta-binomial prior). The selection can be done either with full enumeration of all possible models or using the Markov Chain Monte Carlo Model Composition (MC3) algorithm (Madigan and York (1995) ). Complementary functions for making predictions, as well as plotting and printing the results are also provided.
How to cite:
Konstantina Charmpi (2023). PEPBVS: Bayesian Variable Selection using Power-Expected-Posterior Prior. R package version 2.1, https://cran.r-project.org/web/packages/PEPBVS. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0 (2023-09-19 18:40), 2.0 (2024-10-30 11:50)
Other packages that cited PEPBVS R package
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Complete documentation for PEPBVS
Functions, R codes and Examples using the PEPBVS R package
Full PEPBVS package functions and examples
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