unsurv-package: unsurv: Unsupervised clustering of individualized survival...

In unsurv: Unsupervised Clustering of Individualized Survival Curves

unsurv: Unsupervised clustering of individualized survival curves

Description

The unsurv package provides tools for unsupervised clustering of individualized survival curves using medoid-based clustering (PAM).

Details

It is designed for settings where each individual is represented by a survival probability curve evaluated on a common time grid, such as predictions from:

• Kaplan–Meier estimates,

• Cox models,

• parametric survival models,

• deep learning survival models,

• or other individualized survival predictors.

Core features include:

• PAM clustering using weighted L1 or L2 distances,

• automatic cluster selection via silhouette width,

• optional monotonicity enforcement,

• optional median smoothing,

• prediction of cluster membership for new curves,

• stability assessment via resampling and Adjusted Rand Index,

• base R and ggplot2 visualization methods.

Main functions:

• unsurv — fit clustering model

• predict.unsurv — predict cluster membership

• plot.unsurv — plot medoid curves

• summary.unsurv — summarize clustering

• unsurv_stability — assess stability

Author(s)

Imad EL BADISY

References

Kaufman, L., & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley.

See Also

Useful links:

• https://CRAN.R-project.org/package=unsurv

• Report bugs at https://github.com/ielbadisy/unsurv/issues

Examples

if (requireNamespace("cluster", quietly = TRUE)) {
  set.seed(1)
  n <- 10
  times <- seq(0, 5, length.out = 40)
  rates <- sample(c(0.2, 0.6), n, TRUE)
  S <- sapply(times, function(t) exp(-rates * t))

  fit <- unsurv(S, times, K = 2)
  plot(fit)
}

unsurv documentation built on Sept. 1, 2026, 1:06 a.m.