Bayesian Variable Selection in High Dimensional Settings using
Nonlocal Priors
Description
Variable/Feature selection in high or ultra-high dimensional
settings has gained a lot of attention recently specially in cancer genomic
studies. This package provides a Bayesian approach to tackle this problem,
where it exploits mixture of point masses at zero and nonlocal priors to
improve the performance of variable selection and coefficient estimation.
product moment (pMOM) and product inverse moment (piMOM) nonlocal priors
are implemented and can be used for the analyses. This package performs
variable selection for binary response and survival time response datasets
which are widely used in biostatistic and bioinformatics community.
Benefiting from parallel computing ability, it reports necessary outcomes
of Bayesian variable selection such as Highest Posterior Probability Model
(HPPM), Median Probability Model (MPM) and posterior inclusion probability
for each of the covariates in the model. The option to use Bayesian Model
Averaging (BMA) is also part of this package that can be exploited for
predictive power measurements in real datasets.