The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

ssurvdr

Survival-informed SAVE dimension reduction with sample or maximum-entropy covariance stabilisation, reduced Cox modelling and information-criterion selection.

This is release candidate 0.1.0, prepared for initial CRAN submission.

Small reproducible example

library(ssurvdr)
dat <- simulate_ssurvdr(n = 100, p = 12, active = 1:3, seed = 2026)
fit <- fit_ssurvdr(dat$X, dat$time, dat$status, d = 2,
                  n_slices = 3, covariance = "mec", backend = "primal")
fit$criteria
path <- fit_ssurvdr_sparse_path(dat$X, dat$time, dat$status,
                               dense_fit = fit, support_grid = c(4, 8, 12))
path$path

Statistical interpretation

• fit_ssurvdr() estimates a dense basis; its name does not imply that it automatically selects a sparse support.
• fit_ssurvdr_sparse_path() ranks rows of a fitted basis and refits the reduced Cox model at candidate support sizes, including full support. This is post-estimation sparsification.
• sparse_projection() thresholds a dense primal SAVE kernel and does not support the compact dual kernel.
• compute_icomp() calculates C1 covariance complexity from stabilised eigenvalues and uses it in reduced Cox information criteria. No original-predictor support-size penalty is added.
• compute_c1f_weights() retains its historical name and c1f field. That diagnostic uses the implemented C1 arithmetic/geometric eigenvalue formula. The weights depend on estimated basis row norms and a separate eigenvalue-dispersion adjustment.
• evaluate_ssurvdr() reports the fitted training concordance. Independent predictive assessment requires a held-out sample or cross-validation with all preprocessing, screening and selection performed inside training folds.

The research scripts accompany the audit package outside the installable source. They are not run by package checks.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.