Description: Provides two Bayesian models for detecting the association between rare genetic variants and a trait that can be continuous, ordinal or binary. Bayesian latent variable collapsing model (BLVCM) detects interaction effect and is dedicated to twin design while it can also be applied to independent samples. Hierarchical Bayesian multiple regression model (HBMR) incorporates genotype uncertainty information and can be applied to either independent or family samples. Furthermore, it deals with continuous, binary and ordinal traits.

Author: Martin Feldkircher and Stefan Zeugner

Description: Bayesian Model Averaging for univariate linear models
under Zellner's g prior. Options include: fixed (BRIC, UIP,
...) and flexible g priors (Empirical Bayes, hyper-g), 5 kinds
of model prior concepts, and model sampling via model
enumeration or MCMC samplers (Metropolis-Hastings plain or
reversible jump). Post-processing allows for inference
according to different concepts (likelihood vs MCMC-based) and
for plotting (posterior model size and coefficient densities,
best models, model convergence, BMA comparison).

Description: Bayesian Model Averaging for linear models with a wide
choice of (customizable) priors. Built-in priorss include
coefficient priors (fixed, flexible and hyper-g priors), 5
kinds of model priors, moreover model sampling by enumeration
or various MCMC approaches. Post-processing functions allow for
inferring posterior inclusion and model probabilitites, various
moments, coefficient and predictive densities. Plotting
functions available for posterior model size, MCMC convergence,
predictive and coefficient densities, best models
Packaged: Thu May 5 12:36:52 2011; zeugner

Description: Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, flexible and hyper-g priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison.