psbcGroup | R Documentation |
The package provides algorithms for fitting penalized parametric and semiparametric Bayesian survival models with elastic net, fused lasso, and group lasso priors.
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 |
aftGL_LT | The function to fit the parametric accelerated failure time model with group lasso for left-truncated and interval-censored data |
Package: | psbcGroup |
Type: | Package |
Version: | 1.7 |
Date: | 2024-1-9 |
License: | GPL (>= 2) |
LazyLoad: | yes |
Kyu Ha Lee, Sounak Chakraborty, Harrison Reeder, (Tony) Jianguo Sun
Maintainer: Kyu Ha Lee <klee@hsph.harvard.edu>
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. (2017).
Variable Selection for High-Dimensional Genomic Data with Censored Outcomes Using Group Lasso Prior. Computational Statistics and Data Analysis, Volume 112, pages 1-13.
Reeder, H., Haneuse, S., Lee, K. H. (2023+).
Group Lasso Priors for Bayesian Accelerated Failure Time Models with Left-Truncated and Interval-Censored Time-to-Event Data. under review
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