dmom
(Package: mombf) :
Moment prior and inverse moment prior.
dmom, dimom and demom return the density for the moment, inverse moment and exponential moment priors. pmom, pimom and pemom return the distribution function for the univariate moment, inverse moment and exponential moment priors (respectively). qmom and qimom return the quantiles for the univariate moment and inverse moment priors.
pmomLM
(Package: mombf) :
Bayesian variable selection and model averaging for linear
Variable selection for linear and probit models, providing a sample from the joint posterior of the model and regression coefficients. pmomLM and pmomPM implement product Normal MOM and heavy-tailed product MOM as prior distribution for linear and probit model coefficients (respectively). emomLM and emomPM set an eMOM prior.
modelSelection
(Package: mombf) :
Bayesian variable selection for linear models via non-local priors.
Bayesian model selection for linear models using non-local priors. The algorithm uses a Gibbs scheme and can handle p>>n cases. See rnlp to obtain posterior samples for the coefficients.
Stores the output of Bayesian variable selection, as produced by function modelSelection. The class extends a list, so all usual methods for lists also work for msfit objects, e.g. accessing elements, retrieving names etc.
mombf
(Package: mombf) :
Moment and inverse moment Bayes factors for linear models.
mombf computes moment Bayes factors to test whether a subset of regression coefficients are equal to some user-specified value. imombf computes inverse moment Bayes factors. zellnerbf computes Bayes factors based on the Zellner-Siow prior (used to build the moment prior).
bbPrior
(Package: mombf) :
Priors on model space for variable selection problems
unifPrior implements a uniform prior (equal a priori probability for all models). binomPrior implements a Binomial prior. bbPrior implements a Beta-Binomial prior.
● Data Source:
CranContrib
● Keywords: distribution
● Alias: bbPrior, binomPrior, unifPrior
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eprod
(Package: mombf) :
Expectation of a product of powers of Normal or T random
Compute the mean of prod(x)^power when x follows T_dof(mu,sigma) distribution (dof= -1 for multivariate Normal).
Stores the prior distributions to be used for Bayesian variable selection in normal regression models. This class can be used to specify the prior on non-zero regression coefficients, the model indicator or the nuisance parameters.
providing 
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