Title: Moment and Inverse Moment Bayes factors
Description: This package computes Moment and Inverse Moment Bayes factors for linear models, and approximate Bayes factors for GLM and situations having a statistic which is asymptotically normally distributed and sufficient. Routines to evaluate prior densities, modes and do prior parameter elicitation are included.
Packaged: Thu Apr  3 10:46:37 2008; davidrossell

Packaged: Fri Apr 11 14:42:30 2008; davidrossell

Description: This package computes Moment and Inverse Moment Bayes factors for linear models, and approximate Bayes factors for GLM and situations having a statistic which is asymptotically normally distributed and sufficient. Routines to evaluate prior densities, distribution functions, quantiles and modes are included.
Packaged: Thu Apr 24 12:01:03 2008; davidrossell

Packaged: Thu Jul  3 21:17:05 2008; davidrossell

Packaged: Mon Jan 26 09:56:43 2009; drossell

Description: This package computes Moment and Inverse Moment Bayes
factors for linear models, and approximate Bayes factors for
GLM and situations having a statistic which is asymptotically
normally distributed and sufficient. Routines to evaluate prior
densities, distribution functions, quantiles and modes are

Depends: R (>= 2.11.0), methods, mvtnorm, ncvreg, actuar
Description: This package performs model selection based on non-local
priors, including MOM, eMOM and iMOM priors. Routines are
provided to compute Bayes factors, marginal densities and to
perform variable selection in regression setups. Routines to
evaluate prior densities, distribution functions, quantiles and
Collate: ClassDefinitions.r imombf.r mode2g.r pimom.r
zellnerbf.lm.r dimom.r imomunknown.r priorp2g.r zellnerbf.r
margpimom.r mombf.lm.r qimom.r zellnerbf.unknownsig.r dmom.r
mombf.r qmom.r g2mode.r margpmom.r momknown.r zbfknown.r

Author: David Rossell, John D. Cook, Donatello Telesca
Depends: R (>= 2.11.0), methods, mvtnorm, ncvreg, actuar, mgcv
Description: This package performs model selection based on non-local priors, including MOM, eMOM and iMOM priors. Routines are provided to compute Bayes factors, marginal densities and to perform variable selection in regression setups. Routines to evaluate prior densities, distribution functions, quantiles and modes are included.
Collate: AllClasses.R imombf.R mode2g.R pimom.R zellnerbf.knownsig.R
imomknown.R modelSelection.R pmom.R zellnerbf.lm.R dimom.R
imomunknown.R priorp2g.R zellnerbf.R margpimom.R mombf.lm.R
qimom.R emom.R zellnerbf.unknownsig.R dmom.R mombf.R qmom.R
g2mode.R margpmom.R momknown.R zbfknown.R imombf.lm.R
momunknown.R zbfunknown.R pmomLM.R pmomPM.R emomLM.R postMode.R