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survxai

Survival analysis models are used primarily in medicine and churn analysis. Due to many applications, we are witnessing a fast development of a wide range of black-box survival models. Their lack of interpretability makes them unusable for analyzes that require an understanding of the model behavior.

An R package survxai is a tool for creating explanations of survival models. For both, complex and simple survival models. It also enables to compare them. Currently, four explanation methods are implemented. We can divide them into 2 groups: local and global.

The read more about the surxvai package see paper survxai: an R package for structure-agnostic explanations of survival modelsin The Journal of Open Source Software.

Install

devtools::install_github("MI2DataLab/survxai")

CheatSheet

News

Informations about changes in survxai relases can be found in NEWS.

How to contribute

Informations about creating issues and pull requests in survxai can be found in CONTRIBUTING.

Acknowledgments

Work on this package is financially supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.

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Version

Install

install.packages('survxai')

Monthly Downloads

2

Version

0.2.2

License

GPL

Maintainer

Aleksandra Grudziaz

Last Published

August 28th, 2020

Functions in survxai (0.2.2)

plot.surv_model_performance_explainer

Plot for surv_model_performance object
plot.surv_variable_response_explainer

Plot for surv_variable_response object
ceteris_paribus

Ceteris Paribus
pbcTest

pbcTest
pbcTrain

pbcTrain
plot.surv_prediction_breakdown_explainer

Plot for surv_breakdown object
plot.surv_explainer

Plot for surv_explainer object
model_performance

Model performance for survival models
explain

Create Survival Model Explainer
theme_mi2

MI^2 plot theme
plot.surv_ceteris_paribus_explainer

Plot for ceteris_paribus object
print.surv_ceteris_paribus_explainer

Ceteris Paribus Print
prediction_breakdown

BreakDown for survival models
print.surv_prediction_breakdown_explainer

Prediction Breakdown Print
print.surv_variable_response_explainer

Variable Response Print
variable_response

Variable response for survival models
print.surv_model_performance_explainer

Print Survival Model Performance
print.surv_explainer

Print Survival Explainer Summary