Package: survBootOutliers 1.0

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>.

Authors:Joao Pinto <[email protected]>, Andre Verissimo <[email protected]>, Alexandra Carvalho <[email protected]>, Susana Vinga <[email protected]>

survBootOutliers_1.0.tar.gz
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survBootOutliers_1.0.tgz(r-4.4-any)survBootOutliers_1.0.tgz(r-4.3-any)
survBootOutliers_1.0.tar.gz(r-4.5-noble)survBootOutliers_1.0.tar.gz(r-4.4-noble)
survBootOutliers_1.0.tgz(r-4.4-emscripten)survBootOutliers_1.0.tgz(r-4.3-emscripten)
survBootOutliers.pdf |survBootOutliers.html✨
survBootOutliers/json (API)
NEWS

# Install 'survBootOutliers' in R:
install.packages('survBootOutliers', repos = c('https://jonydog.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jonydog/survbootoutliers/issues

On CRAN:

survival

3.70 score 5 scripts 122 downloads 3 exports 3 dependencies

Last updated 6 years agofrom:a6ccd6084e. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-winOKNov 01 2024
R-4.5-linuxOKNov 01 2024
R-4.4-winOKNov 01 2024
R-4.4-macOKNov 01 2024
R-4.3-winOKNov 01 2024
R-4.3-macOKNov 01 2024

Exports:display.obs.histogramget.whas100.datasetsurvBootOutliers

Dependencies:latticeMatrixsurvival

Introduction

Rendered fromsurvBootOutliers.Rnwusingutils::Sweaveon Nov 01 2024.

Last update: 2018-05-25
Started: 2017-06-21