The `BAS`

R package is designed to provide
an easy to use package and fast code for implementing Bayesian Model
Averaging and Model Selection in `R`

using state of the art prior
distributions for linear and generalized linear models. The prior
distributions in `BAS`

are based on Zellner’s g-prior or mixtures of
g-priors for linear and generalized linear models. These have been shown
to be consistent asymptotically for model selection and inference and
have a number of computational advantages. `BAS`

implements three main
algorithms for sampling from the space of potential models: a
deterministic algorithm for efficient enumeration, adaptive sampling
without replacement algorithm for modest problems, and a MCMC algorithm
that utilizes swapping to escape from local modes with standard
Metropolis-Hastings proposals.

The stable version
can be installed easily in the `R`

console like any other package:

```
install.packages('BAS')
```

On the other hand, I welcome everyone to use the most recent version of
the package with quick-fixes, new features and probably new bugs. It’s
currently hosted on GitHub. To
get the latest development version from
GitHub, use the `devtools`

package
from CRAN and enter in
`R`

:

```
devtools::install_github('merliseclyde/BAS')
```

You can check out the current build and test coverage status courtesy Travis CI: before installing.

Installing the package from source does require compilation of C and FORTRAN code as the library makes use of BLAS and LAPACK for efficient model fitting. See CRAN manuals for installing packages from source under different operating systems.

To begin load the package:

```
library(BAS)
```

The two main function in `BAS`

are `bas.lm`

and `bas.glm`

for
implementing Bayesian Model Averaging and Variable Selection using
Zellner’s g-prior and mixtures of g priors. Both functions have a syntax
similar to the `lm`

and `glm`

functions respectively. We illustrate
using `BAS`

on a simple example with the famous Hald data set using the
Zellner-Siow Cauchy prior via

```
data(Hald)
hald.ZS = bas.lm(Y ~ ., data=Hald, prior="ZS-null", modelprior=uniform(), method="BAS")
```

`BAS`

has `summary`

, `plot`

`coef`

, `predict`

and `fitted`

functions
like the `lm`

/`glm`

functions. Images of the model space highlighting
which variable are important may be obtained via

```
image(hald.ZS)
```

Run `demo("BAS.hald")`

or `demo("BAS.USCrime")`

or see the package
vignette for more examples and options such as using MCMC for model
spaces that cannot be enumerated.

`BAS`

now includes for support for binomial and binary regression and
Poisson regression using Laplace approximations to obtain Bayes Factors
used in calculating posterior probabilities of models or sampling of
models. Here is an example using the Pima diabetes data set with the
hyper-g/n prior:

```
library(MASS)
data(Pima.tr)
Pima.hgn = bas.glm(type ~ ., data=Pima.tr, method="BAS", family=binomial(),
betaprior=hyper.g.n(), modelprior=uniform())
```

Note, the syntax for specifying priors on the coefficients in `bas.glm`

uses a function with arguments to specify the hyper-parameters, rather
than a text string to specify the prior name and a separate argument for
the hyper-parameters. `bas.lm`

will be moving to this format sometime in
the future.

Feel free to report any issues or request features to be added via the github issues page.

For current documentation and vignettes see the BAS website

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.

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