High-Dimensional Model Selection
Priors on model space for variable selection problems
Model with best AIC, BIC, EBIC or other general information criteria (...
Number of Normal mixture components under Normal-IW and Non-local prio...
Treatment effect estimation for linear models via Confounder Importanc...
Density and random draws from the asymmetric Laplace distribution
Dirichlet density
Density for Inverse Wishart distribution
Non-local prior density, cdf and quantile functions.
Posterior Normal-IWishart density
Expectation of a product of powers of Normal or T random variables
Class "icfit"
Extract estimated inverse covariance
Local variable selection
Marginal (or integrated) likelihood density of the observed data for a...
Marginal likelihood under a multivariate Normal likelihood and a conju...
Class "mixturebf"
Moment and inverse moment prior elicitation
Bayesian variable selection for generalized linear and generalized add...
Bayesian variable selection for linear models via non-local priors.
Class "msfit_ggm"
Class "msfit"
Class "msPriorSpec"
Plot estimated marginal prior inclusion probabilities
Obtain posterior model probabilities
Extract posterior samples from an object
Posterior sampling for regression parameters
Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025) <doi:10.5281/zenodo.17119597> (see the URL field below for its URL) for a hands-on book describing the methods, examples and suggested citations if you use the package.
Useful links