Other packages > Find by keyword >

BAS  

Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("BAS", "1.7.5")



Attach the package and use:
library("BAS")
Maintained by
Merlise Clyde
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2009-03-02
Latest Update: 2022-11-02
Description:
Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) for linear models or mixtures of g-priors from Li and Clyde (2019) in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
How to cite:
Merlise Clyde (2009). BAS: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling. R package version 1.7.5, https://cran.r-project.org/web/packages/BAS. Accessed 06 Jan. 2025.
Previous versions and publish date:
0.1 (2009-03-02 10:05), 0.3 (2009-05-29 09:01), 0.4 (2009-12-28 21:01), 0.45 (2009-12-30 09:03), 0.80 (2010-04-06 10:57), 0.85 (2010-04-29 08:51), 0.90 (2010-07-24 21:40), 0.91 (2010-09-09 18:54), 0.92 (2010-10-01 10:22), 1.0.5 (2015-09-10 16:38), 1.0.6 (2015-10-28 00:34), 1.0.8 (2015-11-12 18:59), 1.0.9 (2016-02-03 22:53), 1.0 (2012-06-01 06:41), 1.1.0 (2016-03-31 17:38), 1.2.0 (2016-04-12 01:26), 1.2.1 (2016-04-16 04:27), 1.2.2 (2016-07-01 01:15), 1.3.0 (2016-07-16 09:46), 1.4.0 (2016-08-27 07:52), 1.4.1 (2016-09-20 06:44), 1.4.2 (2016-10-13 20:36), 1.4.3 (2017-02-21 08:02), 1.4.4 (2017-03-14 19:45), 1.4.5 (2017-03-31 08:12), 1.4.6 (2017-05-26 23:12), 1.4.7 (2017-10-22 19:48), 1.4.8 (2018-03-12 16:04), 1.4.9 (2018-03-25 13:27), 1.5.0 (2018-05-03 23:19), 1.5.1 (2018-06-07 15:51), 1.5.2 (2018-10-25 10:00), 1.5.3 (2018-10-30 11:40), 1.5.4 (2020-01-19 18:00), 1.5.5 (2020-01-24 23:50), 1.6.0 (2021-11-13 00:00), 1.6.2 (2022-04-26 09:30), 1.6.3 (2022-10-19 08:47), 1.6.4 (2022-11-02 11:33), 1.6.6 (2023-11-29 01:10), 1.7.1 (2023-12-06 11:40), 1.7.2 (2024-09-17 02:00), 1.7.3 (2024-09-18 00:50)
Other packages that cited BAS R package
View BAS citation profile
Other R packages that BAS depends, imports, suggests or enhances
Complete documentation for BAS
Downloads during the last 30 days
Get rewarded with contribution points by helping add
Reviews / comments / questions /suggestions ↴↴↴

Today's Hot Picks in Authors and Packages

lmds  
Landmark Multi-Dimensional Scaling
A fast dimensionality reduction method scaleable to large numbers of samples. Landmark Multi-Dime ...
Download / Learn more Package Citations See dependency  
TBFmultinomial  
TBF Methodology Extension for Multinomial Outcomes
Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete ...
Download / Learn more Package Citations See dependency  
mlr3fairness  
Fairness Auditing and Debiasing for 'mlr3'
Integrates fairness auditing and bias mitigation methods for the 'mlr3' ecosystem. This includes ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
ROI.plugin.deoptim  
'DEoptim' and 'DEoptimR' Plugin for the 'R' Optimization Interface
Enhances the R Optimization Infrastructure ('ROI') package with the 'DEoptim' and 'DEoptimR' packag ...
Download / Learn more Package Citations See dependency  
RsqMed  
Total Mediation Effect Size Measure for High-Dimensional Mediators
An implementation of calculating the R-squared measure as a total mediation effect size measure and ...
Download / Learn more Package Citations See dependency  

23,440

R Packages

202,297

Dependencies

63,567

Author Associations

23,434

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA