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Coxmos: Cox MultiBlock Survival
This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) <<a href="https://CRAN.R-project.org/package=survival" target="_top">https://CRAN.R-project.org/package=survival</a>>, Noah Simon et al. (2011) <<a href="https://doi.org/10.18637%2Fjss.v039.i05" target="_top">doi:10.18637/jss.v039.i05</a>>, Philippe Bastien et al. (2005) <<a href="https://doi.org/10.1016%2Fj.csda.2004.02.005" target="_top">doi:10.1016/j.csda.2004.02.005</a>>, Philippe Bastien (2008) <<a href="https://doi.org/10.1016%2Fj.chemolab.2007.09.009" target="_top">doi:10.1016/j.chemolab.2007.09.009</a>>, Philippe Bastien et al. (2014) <<a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtu660" target="_top">doi:10.1093/bioinformatics/btu660</a>>, Kassu Mehari Beyene and Anouar El Ghouch (2020) <<a href="https://doi.org/10.1002%2Fsim.8671" target="_top">doi:10.1002/sim.8671</a>>, Florian Rohart et al. (2017) <<a href="https://doi.org/10.1371%2Fjournal.pcbi.1005752" target="_top">doi:10.1371/journal.pcbi.1005752</a>>.
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Coxmos: Cox MultiBlock Survival
This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) <<a href="https://CRAN.R-project.org/package=survival" target="_top">https://CRAN.R-project.org/package=survival</a>>, Noah Simon et al. (2011) <<a href="https://doi.org/10.18637%2Fjss.v039.i05" target="_top">doi:10.18637/jss.v039.i05</a>>, Philippe Bastien et al. (2005) <<a href="https://doi.org/10.1016%2Fj.csda.2004.02.005" target="_top">doi:10.1016/j.csda.2004.02.005</a>>, Philippe Bastien (2008) <<a href="https://doi.org/10.1016%2Fj.chemolab.2007.09.009" target="_top">doi:10.1016/j.chemolab.2007.09.009</a>>, Philippe Bastien et al. (2014) <<a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtu660" target="_top">doi:10.1093/bioinformatics/btu660</a>>, Kassu Mehari Beyene and Anouar El Ghouch (2020) <<a href="https://doi.org/10.1002%2Fsim.8671" target="_top">doi:10.1002/sim.8671</a>>, Florian Rohart et al. (2017) <<a href="https://doi.org/10.1371%2Fjournal.pcbi.1005752" target="_top">doi:10.1371/journal.pcbi.1005752</a>>.
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Coxmos: Cox MultiBlock Survival
This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) <<a href="https://CRAN.R-project.org/package=survival" target="_top">https://CRAN.R-project.org/package=survival</a>>, Noah Simon et al. (2011) <<a href="https://doi.org/10.18637%2Fjss.v039.i05" target="_top">doi:10.18637/jss.v039.i05</a>>, Philippe Bastien et al. (2005) <<a href="https://doi.org/10.1016%2Fj.csda.2004.02.005" target="_top">doi:10.1016/j.csda.2004.02.005</a>>, Philippe Bastien (2008) <<a href="https://doi.org/10.1016%2Fj.chemolab.2007.09.009" target="_top">doi:10.1016/j.chemolab.2007.09.009</a>>, Philippe Bastien et al. (2014) <<a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtu660" target="_top">doi:10.1093/bioinformatics/btu660</a>>, Kassu Mehari Beyene and Anouar El Ghouch (2020) <<a href="https://doi.org/10.1002%2Fsim.8671" target="_top">doi:10.1002/sim.8671</a>>, Florian Rohart et al. (2017) <<a href="https://doi.org/10.1371%2Fjournal.pcbi.1005752" target="_top">doi:10.1371/journal.pcbi.1005752</a>>.
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9- titleCRAN: Package Coxmos
- Content-Typetext/html; charset=utf-8
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- citation_titleCox MultiBlock Survival [R package Coxmos version 1.1.3]
- citation_authorPedro Salguero García
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5- og:titleCoxmos: Cox MultiBlock Survival
- og:descriptionThis software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) <<a href="https://CRAN.R-project.org/package=survival" target="_top">https://CRAN.R-project.org/package=survival</a>>, Noah Simon et al. (2011) <<a href="https://doi.org/10.18637%2Fjss.v039.i05" target="_top">doi:10.18637/jss.v039.i05</a>>, Philippe Bastien et al. (2005) <<a href="https://doi.org/10.1016%2Fj.csda.2004.02.005" target="_top">doi:10.1016/j.csda.2004.02.005</a>>, Philippe Bastien (2008) <<a href="https://doi.org/10.1016%2Fj.chemolab.2007.09.009" target="_top">doi:10.1016/j.chemolab.2007.09.009</a>>, Philippe Bastien et al. (2014) <<a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtu660" target="_top">doi:10.1093/bioinformatics/btu660</a>>, Kassu Mehari Beyene and Anouar El Ghouch (2020) <<a href="https://doi.org/10.1002%2Fsim.8671" target="_top">doi:10.1002/sim.8671</a>>, Florian Rohart et al. (2017) <<a href="https://doi.org/10.1371%2Fjournal.pcbi.1005752" target="_top">doi:10.1371/journal.pcbi.1005752</a>>.
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- og:typewebsite
- og:urlhttps://CRAN.R-project.org/package=Coxmos
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16- https://CRAN.R-project.org/package=Coxmos
- https://CRAN.R-project.org/package=survival
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- https://doi.org/10.1002%2Fsim.8671