Survival analysis models are commonly used in medicine and other areas. Many of them
are too complex to be interpreted by human. Exploration and explanation is needed, but
standard methods do not give a broad enough picture. 'survex' provides easy-to-apply
methods for explaining survival models, both complex black-boxes and simpler statistical models.
They include methods specific to survival analysis such as SurvSHAP(t) introduced in Krzyzinski et al., (2023)
<doi:10.1016/j.knosys.2022.110234>, SurvLIME described in Kovalev et al., (2020) <doi:10.1016/j.knosys.2020.106164> as well as
extensions of existing ones described in Biecek et al., (2021) <doi:10.1201/9780429027192>.
| Version: |
1.2.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
DALEX (≥ 2.2.1), ggplot2 (≥ 3.4.0), kernelshap, pec, survival, patchwork |
| Suggests: |
censored (≥ 0.2.0), covr, flexsurv, gbm, generics, glmnet, ingredients, knitr (≥ 1.42), mboost, parsnip, progressr, randomForestSRC, ranger, reticulate, rmarkdown, rms, testthat (≥ 3.0.0), treeshap (≥ 0.3.0), withr, xgboost |
| Published: |
2023-10-24 |
| Author: |
Mikołaj Spytek 
[aut, cre],
Mateusz Krzyziński
[aut],
Sophie Langbein [aut],
Hubert Baniecki
[aut],
Lorenz A. Kapsner
[ctb],
Przemyslaw Biecek
[aut] |
| Maintainer: |
Mikołaj Spytek <mikolajspytek at gmail.com> |
| BugReports: |
https://github.com/ModelOriented/survex/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://modeloriented.github.io/survex/ |
| NeedsCompilation: |
no |
| Citation: |
survex citation info |
| Materials: |
README NEWS |
| In views: |
Survival |
| CRAN checks: |
survex results |