Packaged: 2019-05-17 22:08:02 UTC; stevescott

Packaged: 2019-06-06 18:43:27 UTC; stevescott

Package: BoomSpikeSlab
Date: 2019-09-01
Title: MCMC for Spike and Slab Regression
Author: Steven L. Scott <steve.the.bayesian@gmail.com>
Maintainer: Steven L. Scott <steve.the.bayesian@gmail.com>
Description: Spike and slab regression with a variety of residual error
distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a
few others.  Spike and slab regression is Bayesian regression with prior
distributions containing a point mass at zero.  The posterior updates the
amount of mass on this point, leading to a posterior distribution that is
actually sparse, in the sense that if you sample from it many coefficients are
actually zeros.  Sampling from this posterior distribution is an elegant way
to handle Bayesian variable selection and model averaging.  See
<DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.
Depends: Boom (>= 0.9.2) , R (>= 3.1.0)
LinkingTo: Boom(>= 0.9.2)
Suggests: MASS, testthat, mlbench
Imports: igraph
Version: 1.2.1
License: LGPL-2.1 | file LICENSE
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2019-09-04 01:49:39 UTC; stevescott
Repository: CRAN
Date/Publication: 2019-09-04 05:10:25 UTC

Package: BoomSpikeSlab
Version: 1.2.2
Date: 2020-03-26
Title: MCMC for Spike and Slab Regression
Author: Steven L. Scott <steve.the.bayesian@gmail.com>
Maintainer: Steven L. Scott <steve.the.bayesian@gmail.com>
Description: Spike and slab regression with a variety of residual error
distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a
few others.  Spike and slab regression is Bayesian regression with prior
distributions containing a point mass at zero.  The posterior updates the
amount of mass on this point, leading to a posterior distribution that is
actually sparse, in the sense that if you sample from it many coefficients are
actually zeros.  Sampling from this posterior distribution is an elegant way
to handle Bayesian variable selection and model averaging.  See
<DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.
License: LGPL-2.1 | file LICENSE
Depends: Boom (>= 0.9.4) , R (>= 3.5.0)
LinkingTo: Boom(>= 0.9.4)
Suggests: MASS, testthat, mlbench, igraph
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2020-03-31 15:33:13 UTC; steve
Repository: CRAN
Date/Publication: 2020-03-31 16:50:02 UTC