Penalized Parametric and Semiparametric Bayesian Survival Models with Shrinkage and Grouping Priors

Description

The package provides algorithms for fitting penalized parametric and semiparametric Bayesian survival models with elastic net, fused lasso, and group lasso priors.

Details

The package includes following functions:

psbcEN The function to fit the PSBC model with elastic net prior
psbcFL The function to fit the PSBC model with fused lasso prior
psbcGL The function to fit the PSBC model with group lasso or Bayesian lasso prior
aftGL The function to fit the parametric accelerated failure time model with group lasso
Package: psbcGroup
Type: Package
Version: 1.3
Date: 2016-03-08
License: GPL (>= 2)
LazyLoad: yes

Author(s)

Kyu Ha Lee, Sounak Chakraborty, (Tony) Jianguo Sun
Maintainer: Kyu Ha Lee <klee@hsph.harvard.edu>

References

Lee, K. H., Chakraborty, S., and Sun, J. (2011). Bayesian Variable Selection in Semiparametric Proportional Hazards Model for High Dimensional Survival Data. The International Journal of Biostatistics, Volume 7, Issue 1, Pages 1-32.

Lee, K. H., Chakraborty, S., and Sun, J. (2015). Survival Prediction and Variable Selection with Simultaneous Shrinkage and Grouping Priors. Statistical Analysis and Data Mining, Volume 8, Issue 2, pages 114-127.

Lee, K. H., Chakraborty, S., and Sun, J. Variable Selection for High-Dimensional Genomic Data with Censored Outcomes Using Group Lasso Prior. submitted.