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survex

Explainable Machine Learning in Survival Analysis

Installation

About

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.

Citation survex citation info
modeloriented.github.io/survex/
Bug report File report

Key Metrics

Version 1.2.0
R ≥ 3.5.0
Published 2023-10-24 178 days ago
Needs compilation? no
License GPL (≥ 3)
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Maintainer

Maintainer

Mikołaj Spytek

mikolajspytek@gmail.com

Authors

Mikołaj Spytek

aut / cre

Mateusz Krzyziński

aut

Sophie Langbein

aut

Hubert Baniecki

aut

Lorenz A. Kapsner

ctb

Przemyslaw Biecek

aut

Material

README
NEWS
Reference manual
Package source

In Views

Survival

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

survex archive

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