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mlr3proba: an R package for machine learning in survival analysis

SUMMARY: As machine learning has become increasingly popular over the last few decades, so too has the number of machine-learning interfaces for implementing these models. Whilst many R libraries exist for machine learning, very few offer extended support for survival analysis. This is problematic c...

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Detalles Bibliográficos
Autores principales: Sonabend, Raphael, Király, Franz J., Bender, Andreas, Bischl, Bernd, Lang, Michel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8428574/
https://www.ncbi.nlm.nih.gov/pubmed/33523131
http://dx.doi.org/10.1093/bioinformatics/btab039
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author Sonabend, Raphael
Király, Franz J.
Bender, Andreas
Bischl, Bernd
Lang, Michel
author_facet Sonabend, Raphael
Király, Franz J.
Bender, Andreas
Bischl, Bernd
Lang, Michel
author_sort Sonabend, Raphael
collection PubMed
description SUMMARY: As machine learning has become increasingly popular over the last few decades, so too has the number of machine-learning interfaces for implementing these models. Whilst many R libraries exist for machine learning, very few offer extended support for survival analysis. This is problematic considering its importance in fields like medicine, bioinformatics, economics, engineering and more. mlr3proba provides a comprehensive machine-learning interface for survival analysis and connects with mlr3’s general model tuning and benchmarking facilities to provide a systematic infrastructure for survival modelling and evaluation. AVAILABILITY AND IMPLEMENTATION: mlr3proba is available under an LGPL-3 licence on CRAN and at https://github.com/mlr-org/mlr3proba, with further documentation at https://mlr3book.mlr-org.com/survival.html.
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spelling pubmed-84285742021-09-10 mlr3proba: an R package for machine learning in survival analysis Sonabend, Raphael Király, Franz J. Bender, Andreas Bischl, Bernd Lang, Michel Bioinformatics Applications Notes SUMMARY: As machine learning has become increasingly popular over the last few decades, so too has the number of machine-learning interfaces for implementing these models. Whilst many R libraries exist for machine learning, very few offer extended support for survival analysis. This is problematic considering its importance in fields like medicine, bioinformatics, economics, engineering and more. mlr3proba provides a comprehensive machine-learning interface for survival analysis and connects with mlr3’s general model tuning and benchmarking facilities to provide a systematic infrastructure for survival modelling and evaluation. AVAILABILITY AND IMPLEMENTATION: mlr3proba is available under an LGPL-3 licence on CRAN and at https://github.com/mlr-org/mlr3proba, with further documentation at https://mlr3book.mlr-org.com/survival.html. Oxford University Press 2021-02-01 /pmc/articles/PMC8428574/ /pubmed/33523131 http://dx.doi.org/10.1093/bioinformatics/btab039 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Sonabend, Raphael
Király, Franz J.
Bender, Andreas
Bischl, Bernd
Lang, Michel
mlr3proba: an R package for machine learning in survival analysis
title mlr3proba: an R package for machine learning in survival analysis
title_full mlr3proba: an R package for machine learning in survival analysis
title_fullStr mlr3proba: an R package for machine learning in survival analysis
title_full_unstemmed mlr3proba: an R package for machine learning in survival analysis
title_short mlr3proba: an R package for machine learning in survival analysis
title_sort mlr3proba: an r package for machine learning in survival analysis
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8428574/
https://www.ncbi.nlm.nih.gov/pubmed/33523131
http://dx.doi.org/10.1093/bioinformatics/btab039
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