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  "Title": "QSAR Modeling with Multiple Algorithms: MLR, PLS, and Random\nForest",
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  "Authors@R": "person(\"Oche Ambrose\", \"George\", email = \"ocheab1@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-3979-6232\"))",
  "Description": "Quantitative Structure-Activity Relationship (QSAR)\nmodeling is a valuable tool in computational chemistry and drug\ndesign, where it aims to predict the activity or property of\nchemical compounds based on their molecular structure. In this\nvignette, we present the 'rQSAR' package, which provides\nfunctions for variable selection and QSAR modeling using\nMultiple Linear Regression (MLR), Partial Least Squares (PLS),\nand Random Forest algorithms.",
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  "Encoding": "UTF-8",
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    "Date": "2026-06-04 10:28:35 UTC",
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      "title": "Build QSAR models with k-fold cross-validation",
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    },
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      "title": "Perform variable selection using regression subsets",
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    {
      "page": "residual_plots",
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      "author": "George Oche Ambrose",
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      "headings": [
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        "Background",
        "Basic Usage",
        "Variable Selection",
        "Visualization",
        "QSAR Modeling",
        "Conclusion"
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      "modified": "2024-04-03 07:14:11",
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