Bayesian Model Averaging using Bayesian Adaptive Sampling

Package for Bayesian 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 in GLMs of Li and Clyde (2015) <>. 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 samples models using the BAS tree structure as an efficient hash table. 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. The user may force variables to always be included. Details behind the sampling algorithm are provided in Clyde, Ghosh and Littman (2010) . This material is based upon work supported by the National Science Foundation under Grant DMS-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.


BAS 1.4.2 October 12, 2016

  • added df to the object returned by bas.glm to simplify coefficients function.
  • corrected expected value of shrinkage for intrinsic, hyper-g/n and TCCH priors for glms

BAS 1.4.1 September 17, 2016

  • the modification in 1.4.0 to automatically handle NA's led to errors if the response was transformed as part of the forumula; this is fixed
  • added subset argument to bas.lm and bas.glm

BAS 1.4.0 August 25, 2016

  • added na.action for bas.lm and bas.glm to omit missing data.
  • new function to plot credible intervals created by confint.pred.bas or confint.coef.bas. See the help files for an example or the vignette.
  • added option in predict.basglm.
  • Added testBF as a betaprior option for bas.glm to implement Bayes Fatcors based on the likelihood ratio statistic's distribution for GLMs.
  • DOI for this version is

BAS 1.3.0 July 15, 2016

A vignette has been added at long last! This illustrates several of the new features in BAS such as

  • new functions for computing credible intervals for fitted and predicted values confint.pred.bas()
  • new function for adding credible intervals for coefficients confint.coef.bas()
  • added posterior standard deviations for fitted values and predicted values in predict.bas()
  • deprecated use of type to specify estimator in fitted.bas and replaced with estimator so that predict() and fitted() are compatible with other S3 methods.
  • updated funtions to be of class bas to avoid NAMESPACE conficts with other libraries

BAS 1.2.2 June 29, 2016

  • added option to find "Best Predictive Model" or "BPM" for fitted.bas or predict.bas
  • added local Empirical Bayes prior and fixed g-prior for bas.glm
  • added diagnostic() function for checking convergence of bas objects created with method = "MCMC""
  • added truncated power prior as in Yang, Wainwright & Jordan (2016)
  • bug fix in plot.bas that appears with Sweave
  • bug fix in coef.bma when there is just one predictor

BAS 1.2.1 April 16, 2016

  • bug fix for method="MCMC" with truncated prior distributions where MH ratio was incorrect allowing models with 0 probability to be sampled.
  • fixed error in Zellner-Siow prior (ZS-null) when n=p+1 or saturated model where log marginal likelihood should be 0

BAS 1.2.0 April 11, 2016

  • removed unsafe code where Rbestmarg (input) was being overwritten in .Call which would end up in corruption of the constant pool of the byte-code (Thanks to Tomas Kalibera for catching this!)
  • fixed issue with dimensions for use with Simple Linear Regression

BAS 1.1.0 March 31, 2016

  • added truncated Beta-Binomial prior and truncated Poisson (works only with MCMC currently)
  • improved code for finding fitted values under the Median
  • deprecated method = "AMCMC" and issue warning message
  • Changed S3 method for plot and image to use class bas rather than bma to avoid name conflicts with other packages

BAS 1.09

- added weights for linear models
- switched LINPACK calls in bayesreg to LAPACK finally should be
- fixed bug in intercept calculation for glms
- fixed inclusion probabilities to be a vector in the global EB
methods for linear models

BAS 1.08

- added intrinsic prior for GLMs
- fixed problems for linear models for p > n and R2 not correct

BAS 1.07

- added phi1 function from Gordy (1998)  confluent hypergeometric
function of two variables  also known as one of the Horn
hypergeometric functions or Humbert's phi1
- added Jeffrey's prior on g
- added the general tCCH prior and special cases of the hyper-g/n.
- TODO check shrinkage functions for all    

BAS 1.06

- new improved Laplace approximation for hypergeometric1F1
- added class basglm for predict
- predict function now handles glm output
- added dataframe option for newdata in predict.bas and predict.basglm
- renamed coefficients in output to be 'mle' in bas.lm to be consistent across
lm and glm versions so that predict methods can handle both
cases.  (This may lead to errors in other external code that
expects object$ols or object$coefficients)
- fixed bug with initprobs that did not include an intercept for bas.lm

BAS 1.05

- added thinning option for MCMC method for bas.lm
- returned posterior expected shrinkage for bas.glm
- added option for initprobs = "marg-eplogp" for using marginal
SLR models to create starting probabilities or order variables
especially for p > n case
- added standalone function for hypergeometric1F1 using Cephes
library and a Laplace aproximation
-Added class "BAS" so that predict and fitted functions (S3
methods) are not masked by functions in the BVS package: to do
modify the rest of the S3 methods.

BAS 1.04

- added bas.glm for model averaging/section using mixture of g-priors for
GLMs.  Currently limited to Logistic Regression
- added Poisson family for

BAS 1.0

- cleaned up  MCMC method code

BAS 0.93

- removed internal print statements in bayesglm.c
- Bug fixes in AMCMC algorithm

BAS 0.92

- fixed glm-fit.R  so that hyperparameter for BIC is numeric

BAS 0.91

- added new AMCMC algorithm

BAS 0.91

- bug fix in bayes.glm

BAS 0.90

- added C routines for fitting glms

BAS 0.85

- fixed problem with duplicate models if n.models was > 2^(p-1) by

restricting n.models

- save original X as part of object so that fitted.bma gives the

correct fitted values (broken in version 0.80)

BAS 0.80

- Added `hypergeometric2F1` function that is callable by R
- centered X's in bas.lm so that the intercept has the correct

shrinkage - changed predict.bma to center newdata using the mean(X) - Added new Adaptive MCMC option (method = "AMCMC") (this is not stable at this point)

BAS 0.7

-Allowed pruning of model tree to eliminate rejected models

BAS 0.6

- Added MCMC option to create starting values for BAS (`method = "MCMC+BAS"`)

BAS 0.5

-Cleaned up all .Call routines so that all objects are duplicated or

allocated within code

BAS 0.45

- fixed ch2inv that prevented building on Windows in bayes glm_fit

BAS 0.4

- fixed fortran calls to use F77_NAME macro 
- changed  allocation of objects for .Call to prevent some objects from being overwritten.  

BAS 0.3

- fixed function to include prior probabilities on models
- fixed update function 

BAS 0.2

- fixed predict.bma to allow newdata to be a matrix or vector with the

column of ones for the intercept optionally included. - fixed help file for predict - added modelprior argument to bas.lm so that users may now use the beta-binomial prior distribution on model size in addition to the default uniform distribution - added functions uniform(), beta-binomial() and Bernoulli() to create model prior objects - added a vector of user specified initial probabilities as an option for argument initprobs in bas.lm and removed the separate argument user.prob

Reference manual

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1.4.4 by Merlise Clyde, 14 days ago,

Browse source code at

Authors: Merlise Clyde [aut, cre, cph], Michael Littman [ctb], Quanli Wang [ctb], Joyee Ghosh [ctb], Yingbo Li [ctb]

Documentation:   PDF Manual  

Task views: Bayesian Inference

GPL (>= 2) license

Depends on stats, graphics

Suggests MASS, knitr, rmarkdown, GGally

See at CRAN