survival.svb: Fit High-Dimensional Proportional Hazards Models

Implementation of methodology designed to perform: (i) variable selection, (ii) effect estimation, and (iii) uncertainty quantification, for high-dimensional survival data. Our method uses a spike-and-slab prior with Laplace slab and Dirac spike and approximates the corresponding posterior using variational inference, a popular method in machine learning for scalable conditional inference. Although approximate, the variational posterior provides excellent point estimates and good control of the false discovery rate. For more information see Komodromos et al. (2021) <doi:10.48550/arXiv.2112.10270>.

Version: 0.0-2
Depends: R (≥ 4.0.0)
Imports: Rcpp (≥ 1.0.6), glmnet, survival
LinkingTo: Rcpp, RcppEigen
Published: 2022-01-17
Author: Michael Komodromos
Maintainer: Michael Komodromos <mk1019 at ic.ac.uk>
BugReports: https://github.com/mkomod/survival.svb/issues
License: GPL-3
URL: https://github.com/mkomod/survival.svb
NeedsCompilation: yes
Materials: README
CRAN checks: survival.svb results

Documentation:

Reference manual: survival.svb.pdf

Downloads:

Package source: survival.svb_0.0-2.tar.gz
Windows binaries: r-devel: survival.svb_0.0-2.zip, r-release: survival.svb_0.0-2.zip, r-oldrel: survival.svb_0.0-2.zip
macOS binaries: r-release (arm64): survival.svb_0.0-2.tgz, r-oldrel (arm64): survival.svb_0.0-2.tgz, r-release (x86_64): survival.svb_0.0-2.tgz
Old sources: survival.svb archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=survival.svb to link to this page.