Package: ssurvdr 0.1.0

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.

Authors:Kabir Opeyemi Olorede [aut, cre]

ssurvdr_0.1.0.tar.gz
ssurvdr_0.1.0.zip(r-4.7-any)ssurvdr_0.1.0.zip(r-4.6-any)ssurvdr_0.1.0.zip(r-4.5-any)
ssurvdr_0.1.0.tgz(r-4.6-any)ssurvdr_0.1.0.tgz(r-4.5-any)
ssurvdr_0.1.0.tar.gz(r-4.7-any)ssurvdr_0.1.0.tar.gz(r-4.6-any)
ssurvdr_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ssurvdr/json (API)

# Install 'ssurvdr' in R:

install.packages('ssurvdr', repos = c('https://ilovemaths.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ilovemaths/ssurvdr/issues

On CRAN:

3.70 score 18 exports 4 dependencies

Last updated from:bc2d82a71e. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	113
source / vignettes	OK	383
linux-release	OK	141
macos-release	OK	188
macos-oldrel	OK	193
windows-devel	OK	63
windows-release	OK	156
windows-oldrel	OK	78
wasm-release	OK	139

Exports:compute_c1f_weights compute_icomp evaluate_ssurvdr extract_sdr_basis fit_reduced_cox fit_ssurvdr fit_ssurvdr_grid fit_ssurvdr_sparse_path mec_covariance run_ssurvdr_simulation save_kernel screen_survival_variables select_variables simulate_ssurvdr sparse_projection sparsify_sdr_basis summarise_ssurvdr survival_slicing

Dependencies:lattice MASS Matrix survival

Survival dimension reduction and row-support selection

Basis-adaptive weights | Validation

Rendered fromintroduction-to-ssurvdr.Rmdusingknitr::rmarkdown

Last update: 2026-10-11
Started: 2026-10-11

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Compute basis-adaptive variable weights	compute_c1f_weights
Compute information criteria for reduced Cox model	compute_icomp
Evaluate ssurvdr fitted model against known simulation truth	evaluate_ssurvdr
Extract sufficient dimension reduction basis	extract_sdr_basis
Fit reduced Cox proportional hazards model	fit_reduced_cox
Fit sparse survival dimension reduction model	fit_ssurvdr
Fit ssurvdr model over a tuning grid	fit_ssurvdr_grid
Fit an ICOMP-guided row-sparsity path	fit_ssurvdr_sparse_path
Maximum Entropy Covariance Estimator	mec_covariance
Run Monte Carlo simulation for ssurvdr	run_ssurvdr_simulation
Compute survival SAVE kernel matrix	save_kernel
Screen variables using univariate Cox models	screen_survival_variables
Select variables from SDR basis	select_variables
Simulate high-dimensional censored survival data for ssurvdr	simulate_ssurvdr
Sparse projection from SAVE kernel	sparse_projection
Row-sparsify an SDR basis	sparsify_sdr_basis
Summarise ssurvdr model output	summarise_ssurvdr
Construct survival-informed slices for censored survival data	survival_slicing