Package: BVSNLP
Type: Package
Title: Bayesian Variable Selection in High Dimensional Settings using
Non-Local Prior
Version: 0.9.10
Author: Amir Nikooienejad [aut, cre], Valen E. Johnson [ths]
Maintainer: Amir Nikooienejad <amir@stat.tamu.edu>
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.
It 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.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.1.0), doParallel (>= 1.0.9)
Imports: Rcpp, foreach, parallel
LinkingTo: Rcpp, RcppArmadillo, RcppEigen, RcppNumerical
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-02-07 19:54:56 UTC; AmirNik
Repository: CRAN
Date/Publication: 2018-02-07 20:43:43 UTC

Package: BVSNLP
Type: Package
Title: Bayesian Variable Selection in High Dimensional Settings using
Non-Local Prior
Version: 0.9.7
Author: Amir Nikooienejad [aut, cre], Valen E. Johnson [ths]
Maintainer: Amir Nikooienejad <amir@stat.tamu.edu>
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.
It 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.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.1.0), doParallel (>= 1.0.9)
Imports: Rcpp, foreach, parallel
LinkingTo: Rcpp, RcppArmadillo, RcppEigen, RcppNumerical
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-01-08 14:30:45 UTC; AmirNik
Repository: CRAN
Date/Publication: 2018-01-08 14:52:20 UTC