{
  "_id": "6a13f8bdacfb0bcc41d3457a",
  "Package": "Coxmos",
  "Title": "Cox MultiBlock Survival",
  "Version": "1.2.0",
  "Authors@R": "c(person(\"Pedro\", \"Salguero\", , \"pedsalga@upv.edu.es\", role = c(\"aut\", \"cre\", \"rev\"),\ncomment = c(ORCID = \"0000-0002-1879-3374\")),\nperson(\"Sonia\", \"Tarazona Campos\", , \"sotacam@eio.upv.es\", role = c(\"ths\")),\nperson(\"Kassu\", \"Mehari Beyene\", , \"kasu.beyene@uclouvain.be\", role = c(\"ctb\")),\nperson(\"Luis\", \"Meira Machado\", , \"\", role = c(\"ctb\")),\nperson(\"Marta\", \"Sestelo\", , \"\", role = c(\"ctb\")),\nperson(\"Artur\", \"Araújo\", , \"\", role = c(\"ctb\"))\n)",
  "Maintainer": "Pedro Salguero <pedsalga@upv.edu.es>",
  "Description": "This software package provides Cox survival analysis for\nhigh-dimensional and multiblock datasets. It encompasses a\nsuite of functions dedicated from the classical Cox regression\nto newest analysis, including Cox proportional hazards model,\nStepwise Cox regression, and Elastic-Net Cox regression, Sparse\nPartial Least Squares Cox regression (sPLS-COX) incorporating\nthree distinct strategies, and two Multiblock-PLS Cox\nregression (MB-sPLS-COX) methods. This tool is designed to\nadeptly handle high-dimensional data, and provides tools for\ncross-validation, plot generation, and additional resources for\ninterpreting results. While references are available within the\ncorresponding functions, key literature is mentioned below.\nTerry M Therneau (2024)\n<https://CRAN.R-project.org/package=survival>, Noah Simon et\nal. (2011) <doi:10.18637/jss.v039.i05>, Philippe Bastien et al.\n(2005) <doi:10.1016/j.csda.2004.02.005>, Philippe Bastien\n(2008) <doi:10.1016/j.chemolab.2007.09.009>, Philippe Bastien\net al. (2014) <doi:10.1093/bioinformatics/btu660>, Kassu Mehari\nBeyene and Anouar El Ghouch (2020) <doi:10.1002/sim.8671>,\nFlorian Rohart et al. (2017)\n<doi:10.1371/journal.pcbi.1005752>.",
  "URL": "https://github.com/BiostatOmics/Coxmos",
  "BugReports": "https://github.com/BiostatOmics/Coxmos/issues",
  "License": "GPL (>= 3) + file LICENSE",
  "Encoding": "UTF-8",
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  "Repository": "https://biostatomics.r-universe.dev",
  "Date/Publication": "2026-02-24 18:57:38 UTC",
  "RemoteUrl": "https://github.com/biostatomics/coxmos",
  "RemoteRef": "HEAD",
  "RemoteSha": "0c3bcb3778179c7a466a4e0e7f35c53961f0f9dc",
  "Packaged": {
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    "User": "root"
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  "Author": "Pedro Salguero [aut, cre, rev] (ORCID:\n<https://orcid.org/0000-0002-1879-3374>),\nSonia Tarazona Campos [ths],\nKassu Mehari Beyene [ctb],\nLuis Meira Machado [ctb],\nMarta Sestelo [ctb],\nArtur Araújo [ctb]",
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  "_vignettes": [
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      "source": "Coxmos-pipeline.Rmd",
      "filename": "Coxmos-pipeline.html",
      "title": "Step-by-step guide to the High-Dimensional Coxmos pipeline",
      "author": "Pedro Salguero García",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Installation",
        "Getting ready",
        "Input data",
        "Exploratory Analysis and Data Processing",
        "Event Distribution",
        "Event Per Variable (EVP)",
        "Preprocessing Categorical Variables for Dimensionality Reduction",
        "Training/Test Split for Survival Model Validation",
        "Survival Models",
        "Classical approaches",
        "Cox",
        "Cox SW",
        "HD methods for Survival Analyses",
        "Cox Elastic Net",
        "sPLS-ICOX",
        "sPLS-DRCOX",
        "sPLS-DACOX",
        "Model Validation",
        "Survival Models' Comparison",
        "Computing Time Comparison (Cross-Validation Model's time)",
        "Evaluating the Proportional Hazards Assumption",
        "Model Interpretation",
        "Forest Plots for Cox Model Interpretation",
        "Predicted Value Distribution by Event Status",
        "Variable or Component Contributions in Coxmos Models",
        "Visualizing sPLS Models for Survival Analysis",
        "Interpreting original variables in sPLS Components: Pseudobeta Coefficients",
        "Kaplan-Meier Curves for Visualizing Survival Differences",
        "Full Model-Based Stratification",
        "Component-Level Kaplan-Meier Model-Based Stratification",
        "Variable-Level Kaplan-Meier Curves",
        "Prediction and Evaluation on New Observations",
        "Single Patient Analysis",
        "Predicted Value Distribution for new observations",
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