Simulate and analyze Semi-competing Risks Data using
copula-based models. The Semi-competing Risks Data consist of a
terminal event time and single or multiple intermediate event times.
The marginal survival functions of these event times are estimated
without parametric assumptions. The association parameters measuring dependency
among these event times involving the copula model are yielded from
solving a concordance estimating equations or maximizing a
pseudo-likelihood function. Details can be found in the article by
Tonghui Yu and Liming Xiang (2026) <doi:10.1093/biomtc/ujag087>.
| Version: |
1.0.1 |
| Depends: |
foreach, R (≥ 4.3.0), survival |
| Imports: |
acopula, copula (≥ 1.1-3), cowplot, doParallel (≥ 1.0.17), graph (≥ 1.80.0), graphics, parallel, pracma, prodlim (≥
2023.08.28), quantreg (≥ 5.97), Rcpp, Rgraphviz (≥ 2.46.0), stats, survminer, utils |
| LinkingTo: |
Rcpp |
| Published: |
2026-06-12 |
| DOI: |
10.32614/CRAN.package.CopulaSCR (may not be active yet) |
| Author: |
Tonghui Yu [aut, cre],
Binhui Zhang [aut] |
| Maintainer: |
Tonghui Yu <tonghui_yu at 126.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
CopulaSCR results |