jonydog/survBootOutliers: Concordance based methods for outlier detection in survival analysis
Version 0.99.4

This packages provides three methods to perform outlier detection in a survival context. In total there are six methods provided, the first three methods are traditional residual-based outlier detection methods, the second three are the concordance-based. Package developed during the work on the following publications: Pinto J., Carvalho A. and Vinga S. (2015). Outlier Detection in Survival Analysis based on the Concordance C-index.In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015) ISBN 978-989-758-070-3, pages 75-82. DOI: 10.5220/0005225300750082 Pinto J.D., Carvalho A.M., Vinga S. (2015) Outlier Detection in Cox Proportional Hazards Models Based on the Concordance c-Index. In: Pardalos P., Pavone M., Farinella G., Cutello V. (eds) Machine Learning, Optimization, and Big Data. Lecture Notes in Computer Science, vol 9432. Springer, Chams.

Getting started

Package details

AuthorJoao Pinto <[email protected]>, Andre Verissimo <[email protected]>, Alexandra Carvalho <[email protected]>, Susana Vinga <[email protected]>
Bioconductor views Survival
MaintainerJoao Pinto <[email protected]>
LicenseGPL-2
Version0.99.4
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("jonydog/survBootOutliers")
jonydog/survBootOutliers documentation built on Sept. 26, 2017, 10:20 p.m.