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survBootOutliers: Concordance Based Bootstrap Methods for Outlier Detection in Survival Analysis

Three new 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 two following publications: Pinto J., Carvalho A. and Vinga S. (2015) <doi:10.5220/0005225300750082>; Pinto J.D., Carvalho A.M., Vinga S. (2015) <doi:10.1007/978-3-319-27926-8_22>.

Version: 1.0
Depends: R (≥ 3.4.0), survival, stats
Imports: methods, utils
Suggests: BiocParallel
Published: 2018-05-28
DOI: 10.32614/CRAN.package.survBootOutliers
Author: Joao Pinto, Andre Verissimo, Alexandra Carvalho, Susana Vinga
Maintainer: Joao Pinto <joao.pinto at tecnico.ulisboa.pt>
License: GPL-2
URL: https://github.com/jonydog/survBootOutliers
NeedsCompilation: no
Materials: NEWS
CRAN checks: survBootOutliers results

Documentation:

Reference manual: survBootOutliers.pdf
Vignettes: Introduction

Downloads:

Package source: survBootOutliers_1.0.tar.gz
Windows binaries: r-devel: survBootOutliers_1.0.zip, r-release: survBootOutliers_1.0.zip, r-oldrel: survBootOutliers_1.0.zip
macOS binaries: r-release (arm64): survBootOutliers_1.0.tgz, r-oldrel (arm64): survBootOutliers_1.0.tgz, r-release (x86_64): survBootOutliers_1.0.tgz, r-oldrel (x86_64): survBootOutliers_1.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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