bbPrior                 Priors on model space for variable selection
                        problems
bestBIC                 Model with best AIC, BIC, EBIC or other general
                        information criteria (getIC)
bfnormmix               Number of Normal mixture components under
                        Normal-IW and Non-local priors
cil                     Treatment effect estimation for linear models
                        via Confounder Importance Learning using
                        non-local priors.
dalapl                  Density and random draws from the asymmetric
                        Laplace distribution
ddir                    Dirichlet density
diwish                  Density for Inverse Wishart distribution
dmom                    Non-local prior density, cdf and quantile
                        functions.
dpostNIW                Posterior Normal-IWishart density
eprod                   Expectation of a product of powers of Normal or
                        T random variables
getBIC                  Obtain AIC, BIC, EBIC or other general
                        information criteria (getIC)
hald                    Hald Data
icfit-class             Class "icfit"
icov                    Extract estimated inverse covariance
localnulltest           Local variable selection
marginalNIW             Marginal likelihood under a multivariate Normal
                        likelihood and a conjugate Normal-inverse
                        Wishart prior.
mixturebf-class         Class "mixturebf"
modelSelection          Bayesian variable selection for linear models
                        via non-local priors.
modelSelectionGGM       Bayesian variable selection for linear models
                        via non-local priors.
mombf                   Moment and inverse moment Bayes factors for
                        linear models.
momknown                Bayes factors for moment and inverse moment
                        priors
msPriorSpec-class       Class "msPriorSpec"
msfit-class             Class "msfit"
msfit_ggm-class         Class "msfit_ggm"
nlpmarginals            Marginal density of the observed data for
                        linear regression with Normal, two-piece
                        Normal, Laplace or two-piece Laplace residuals
                        under non-local and Zellner priors
plotprior               Plot estimated marginal prior inclusion
                        probabilities
postModeOrtho           Bayesian model selection and averaging under
                        block-diagonal X'X for linear models.
postProb                Obtain posterior model probabilities
postSamples             Extract posterior samples from an object
priorp2g                Moment and inverse moment prior elicitation
rnlp                    Posterior sampling for regression parameters
