BMS-package: BMS: Bayesian Model Averaging Library

In BMS: Bayesian Model Averaging Library

BMS: Bayesian Model Averaging Library

Description

Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical 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. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods.

Details

The key function you need is bms.

Author(s)

Martin Feldkircher, Paul Hofmarcher, and Stefan Zeugner

References

http://bms.zeugner.eu: BMS package homepage with help and tutorials

Feldkircher, M. and S. Zeugner (2015): Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R, Journal of Statistical Software 68(4).

Feldkircher, M. and S. Zeugner (2009): Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging, IMF Working Paper 09/202.

See Also

coef.bma, plotModelsize and density.bma for some operations on the resulting 'bma' object, as well as predict.bma or gdensity, or zlm for individual Zellner regression models.

Check http://bms.zeugner.eu for additional help.

BMS documentation built on Aug. 9, 2022, 5:08 p.m.