ssurvdr: Sparse Survival Dimension Reduction for High-Dimensional Censored Data

Implements sparse survival sufficient dimension reduction methods for high-dimensional censored data, including covariance-stabilised SAVE, Maximum Entropy Covariance estimation, information-complexity-guided variable weighting, dual-space high-dimensional SAVE computation, row-wise post-estimation sparsification paths, and Cox proportional hazards model selection.

Version: 0.1.0
Depends: R (≥ 4.1.0)
Imports: stats, MASS, survival
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown
Published: 2026-10-21
DOI: 10.32614/CRAN.package.ssurvdr (may not be active yet)
Author: Kabir Opeyemi Olorede [aut, cre]
Maintainer: Kabir Opeyemi Olorede <kabir.olorede at kwasu.edu.ng>
BugReports: https://github.com/ilovemaths/ssurvdr/issues
License: MIT + file LICENSE
URL: https://github.com/ilovemaths/ssurvdr
NeedsCompilation: no
Citation: ssurvdr citation info
Materials: README
CRAN checks: ssurvdr results

Documentation:

Reference manual: ssurvdr.html , ssurvdr.pdf
Vignettes: Survival dimension reduction and row-support selection (source, R code)

Downloads:

Package source: ssurvdr_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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