{
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  "Package": "viralx",
  "Title": "Explainers for Regression Models in HIV Research",
  "Version": "1.3.1",
  "Authors@R": "person(\"Juan Pablo\", \"Acuña González\", , \"acua6307@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0003-6029-6560\"))",
  "Maintainer": "Juan Pablo Acuña González <acua6307@gmail.com>",
  "Description": "A dedicated viral-explainer model tool designed to empower\nresearchers in the field of HIV research, particularly in viral\nload and CD4 (Cluster of Differentiation 4) lymphocytes\nregression modeling. Drawing inspiration from the 'tidymodels'\nframework for rigorous model building of Max Kuhn and Hadley\nWickham (2020) <https://www.tidymodels.org>, and the 'DALEXtra'\ntool for explainability by Przemyslaw Biecek (2020)\n<doi:10.48550/arXiv.2009.13248>. It aims to facilitate\ninterpretable and reproducible research in biostatistics and\ncomputational biology for the benefit of understanding HIV\ndynamics.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
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  "URL": "https://github.com/juanv66x/viralx",
  "BugReports": "https://github.com/juanv66x/viralx/issues",
  "Config/pak/sysreqs": "libicu-dev",
  "Repository": "https://juanv66x.r-universe.dev",
  "Date/Publication": "2024-11-04 03:23:23 UTC",
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    "User": "root"
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    "viralx_knn_shap",
    "viralx_knn_vis",
    "viralx_mars",
    "viralx_mars_shap",
    "viralx_mars_vis",
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    "viralx_nn_glob",
    "viralx_nn_shap",
    "viralx_nn_vis"
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      "title": "Training Data for Explainability of Models",
      "object": "train2",
      "class": [
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      "fields": [
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        "vl_2019",
        "cd_2021",
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        "vl_2022"
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      "rows": 25,
      "table": true,
      "tojson": true
    }
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      "page": "glob_cr_vis",
      "title": "Global Visualization of SHAP Values for Cubist Rules Model",
      "topics": [
        "glob_cr_vis"
      ]
    },
    {
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      "title": "Global Visualization of SHAP Values for K-Nearest Neighbor Model",
      "topics": [
        "glob_knn_vis"
      ]
    },
    {
      "page": "glob_nn_vis",
      "title": "Global Visualization of SHAP Values for Neural Network Model",
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        "glob_nn_vis"
      ]
    },
    {
      "page": "train2",
      "title": "Training Data for Explainability of Models",
      "topics": [
        "train2"
      ]
    },
    {
      "page": "viralx_knn",
      "title": "Explain K-Nearest Neighbors Model",
      "topics": [
        "viralx_knn"
      ]
    },
    {
      "page": "viralx_knn_glob",
      "title": "Global Explainers for K-Nearest Neighbor Models",
      "topics": [
        "viralx_knn_glob"
      ]
    },
    {
      "page": "viralx_knn_shap",
      "title": "Explain K Nearest Neighbor Model using SHAP values",
      "topics": [
        "viralx_knn_shap"
      ]
    },
    {
      "page": "viralx_knn_vis",
      "title": "Visualize SHAP Values for K-Nearest Neighbor Model",
      "topics": [
        "viralx_knn_vis"
      ]
    },
    {
      "page": "viralx_mars",
      "title": "Explain Multivariate Adaptive Regression Splines Model",
      "topics": [
        "viralx_mars"
      ]
    },
    {
      "page": "viralx_mars_shap",
      "title": "Explain Multivariate Adaptive Regression Splines Using SHAP Values",
      "topics": [
        "viralx_mars_shap"
      ]
    },
    {
      "page": "viralx_mars_vis",
      "title": "Visualize SHAP Values for Multivariate Adaptive Regression Splines Model",
      "topics": [
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      ]
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      ]
    },
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      ]
    },
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      ]
    },
    {
      "page": "viralx_nn_vis",
      "title": "Visualize SHAP Values for Neural Network Model",
      "topics": [
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      ]
    }
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