Conceived to calculate Bayes factors in linear models and then to provide a formal Bayesian answer to testing and variable selection problems. From a theoretical side, the emphasis in this package is placed on the prior distributions and it allows a wide range of them: Jeffreys (1961); Zellner and Siow(1980)<DOI:10.1007/bf02888369>; Zellner and Siow(1984); Zellner (1986)<DOI:10.2307/2233941>; Fernandez et al. (2001)<DOI:10.1016/s0304-4076(00)00076-2>; Liang et al. (2008)<DOI:10.1198/016214507000001337> and Bayarri et al. (2012)<DOI:10.1214/12-aos1013>. The interaction with the package is through a friendly interface that syntactically mimics the well-known lm() command of R. The resulting objects can be easily explored providing the user very valuable information (like marginal, joint and conditional inclusion probabilities of potential variables; the highest posterior probability model, HPM; the median probability model, MPM) about the structure of the true -data generating- model. Additionally, this package incorporates abilities to handle problems with a large number of potential explanatory variables through parallel and heuristic versions of the main commands, Garcia-Donato and Martinez-Beneito (2013)<DOI:10.1080/01621459.2012.742443>.
|Author||Gonzalo Garcia-Donato and Anabel Forte|
|Date of publication||2016-11-18 12:47:45|
|Maintainer||Anabel Forte <firstname.lastname@example.org>|
BMAcoeff: Bayesian Model Averaged estimations of regression...
Btest: Bayes factors and posterior probabilities for linear...
Bvs: Bayesian Variable Selection for linear regression models
GibbsBvs: Bayesian Variable Selection for linear regression models...
Hald: Hald data
Jointness: Computation of Jointness measurements.
Ozone35: Ozone35 dataset
PBvs: Bayesian Variable Selection for linear regression models...
predictBvs: Bayesian Model Averaged predictions