NEWS.md

In PEPBVS: Bayesian Variable Selection using Power-Expected-Posterior Prior

PEPBVS 2.0

• Added function peptest for hypothesis testing

• Added function estimation.pep which can be used for estimation under Bayesian model averaging

• Added function posteriorpredictive.pep which simulates values from the posterior predictive distribution under Bayesian model averaging

• Added function comparepriors.lm which returns selected models under different choices of prior on the model parameters and the model space

• The argument xnew of S3 method predict has to be a data frame instead of a matrix.

• The older functions full_enumeration_pep and mc3_pep were unified and replaced by the new function pep.lm. The main differences are the following: a) pep.lm takes as arguments a formula and a data frame instead of a matrix (input data matrix) and a vector (response vector) and b) with the new argument algorithmic.choice="automatic", the choice of the model selection algorithm (full enumeration or MC3) can also be done automatically and does not necessarily have to be chosen by the user. Finally, the new function returns few additional list elements: fullmodel representing the full model that was used, mapp containing the mapping between Xi's and the original explanatory variable names and allvisitedmodsM containing all visited' models after the burnin period (relevant for MC3). The older functionsfull_enumeration_pepandmc3_pep` return a warning message that are deprecated.

PEPBVS documentation built on April 3, 2025, 6:12 p.m.