Package: rmsBMA 0.2.0

rmsBMA: Reduced Model Space Bayesian Model Averaging

Implements Bayesian model averaging for settings with many candidate regressors relative to the available sample size, including cases where the number of regressors exceeds the number of observations. By restricting attention to models with at most M regressors, the package supports reduced model space inference, thereby preserving degrees of freedom for estimation. It provides posterior summaries, Extreme Bounds Analysis, model selection procedures, joint inclusion measures, and graphical tools for exploring model probabilities, model size distributions, and coefficient distributions. When the model space is too large to enumerate, it can be explored by Markov chain Monte Carlo model composition instead. The methodological approach follows Doppelhofer and Weeks (2009) <doi:10.1002/jae.1046> and Madigan and York (1995) <doi:10.2307/1403615>.

Authors:Krzysztof Beck [aut, cre]