## Survival Support Vector Analysis Cesaire
J. K. Fouodo

### Introduction

This package performs support vectors analysis for data sets with
survival outcome. Three approaches are available in the package: The
regression approach takes censoring into account when formulating the
inequality constraints of the support vector problem. In the ranking
approach, the inequality constraints set the objective to maximize the
concordance index for comparable pairs of observations. The hybrid
approach combines the regression and ranking constraints in the same
model.

### Installation

Installation from Github:

`devtools::install_github("imbs-hl/survivalsvm")`

CRAN release coming soon.

### Usage

For usage in R, see ?survivalsvm in R. Most importantly, see the
Examples section. As a first example you could try

`survivalsvm(Surv(time, status) ~ ., veteran, gamma.mu = 0.1)`

### References

- Van Belle, V., Pelcmans, K., Van Huffel S. and Suykens J. A.K.
(2011a). Improved performance on high-dimensional survival data by
application of Survival-SVM. Bioinformatics (Oxford, England) 27,
87-94.
- Van Belle, V., Pelcmans, K., Van Huffel S. and Suykens J. A.K.
(2011b). Support vector methods for survival analysis: a comparaison
between ranking and regression approaches. Artificial Intelligence in
medecine 53, 107-118.