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PEPBVS
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]
[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 Feb. 2025.
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Complete documentation for PEPBVS
Functions, R codes and Examples using
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