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>.">

survex: Explainable Machine Learning in Survival Analysis (original) (raw)

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
DOI: 10.32614/CRAN.package.survex
Author: Mikołaj Spytek ORCID iD [aut, cre], Mateusz KrzyzińskiORCID iD [aut], Sophie Langbein [aut], Hubert Baniecki ORCID iD [aut], Lorenz A. Kapsner ORCID iD [ctb], Przemyslaw Biecek ORCID iD [aut]
Maintainer: Mikołaj Spytek
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

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