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  "Title": "Heteroscedastic Nearest Shrunken Centroid Classification",
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  "Authors@R": "c(\nperson(\"Haiyan\", \"Wang\", email = \"hwang@ksu.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Mohammad Omar\", \"Sahtout\", role = \"aut\"),\nperson(\"Trevor\", \"Hastie\", role = \"ctb\",\ncomment = \"Author of the 'pamr' code this package is derived from\"),\nperson(\"Robert\", \"Tibshirani\", role = \"ctb\",\ncomment = \"Author of the 'pamr' code this package is derived from\"),\nperson(\"Balasubramanian\", \"Narasimhan\", role = \"ctb\",\ncomment = \"Author of the 'pamr' code this package is derived from\"),\nperson(\"Gilbert\", \"Chu\", role = \"ctb\",\ncomment = \"Author of the 'pamr' code this package is derived from\"))",
  "Description": "Nearest shrunken centroid (NSC) classifiers for\nhigh-dimensional, low-sample-size data such as gene expression.\nImplements the original homoscedastic NSC with soft\nthresholding (Prediction Analysis of Microarrays; Tibshirani et\nal. 2002 <doi:10.1073/pnas.082099299>), its hard- and\norder-thresholding versions (Sahtout, Wang and Ghimire 2024\n<doi:10.1080/03610918.2022.2047201>), and the heteroscedastic\nextension of Sahtout and Wang that uses class-specific\nvariances in both the test statistic and the discriminant\nfunction (methods STh3, HTh3 and OTh3). The thresholding\nparameter is selected by a refined (\"deep\") cross-validation\nsearch. Also provides the simulation design, per-class\nperformance metrics and the relative-heterogeneity diagnostic\nused in the paper.",
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  "Author": "Haiyan Wang [aut, cre],\nMohammad Omar Sahtout [aut],\nTrevor Hastie [ctb] (Author of the 'pamr' code this package is derived\nfrom),\nRobert Tibshirani [ctb] (Author of the 'pamr' code this package is\nderived from),\nBalasubramanian Narasimhan [ctb] (Author of the 'pamr' code this\npackage is derived from),\nGilbert Chu [ctb] (Author of the 'pamr' code this package is derived\nfrom)",
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    "compute_rho2",
    "hnsc",
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    "hnsc_methods",
    "hnsc_select",
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    "per_class_recall",
    "recall_table",
    "simulate_hetero_data"
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    {
      "page": "balanced_folds",
      "title": "Class-balanced cross-validation folds",
      "topics": [
        "balanced_folds"
      ]
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    {
      "page": "compare_nsc",
      "title": "Compare homoscedastic and heteroscedastic NSC classifiers on one dataset",
      "topics": [
        "compare_nsc"
      ]
    },
    {
      "page": "compute_rho2",
      "title": "Empirical relative heterogeneity rho_k^2 and the crossover condition",
      "topics": [
        "compute_rho2"
      ]
    },
    {
      "page": "hnsc",
      "title": "Fit a nearest shrunken centroid classifier with the threshold chosen by cross-validation",
      "topics": [
        "hnsc"
      ]
    },
    {
      "page": "hnsc_cv",
      "title": "Cross-validation for a fitted NSC classifier",
      "topics": [
        "hnsc_cv"
      ]
    },
    {
      "page": "hnsc_methods",
      "title": "The six nearest shrunken centroid classifiers",
      "topics": [
        "hnsc_methods"
      ]
    },
    {
      "page": "hnsc_select",
      "title": "Choose the threshold by refined (\"deep\") cross-validation",
      "topics": [
        "hnsc_select"
      ]
    },
    {
      "page": "hnsc_train",
      "title": "Train a nearest shrunken centroid classifier over a grid of thresholds",
      "topics": [
        "hnsc_train"
      ]
    },
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      "title": "Confusion matrix, per-class recall and balanced accuracy",
      "topics": [
        "balanced_acc",
        "make_cm",
        "per_class_recall"
      ]
    },
    {
      "page": "predict.hnsc",
      "title": "Predict with a nearest shrunken centroid classifier",
      "topics": [
        "predict.hnsc"
      ]
    },
    {
      "page": "simulate_hetero_data",
      "title": "Simulate two-class data with correlated informative genes",
      "topics": [
        "simulate_hetero_data"
      ]
    },
    {
      "page": "summary.hnsc_comparison",
      "title": "Summaries of a classifier comparison",
      "topics": [
        "recall_table",
        "summary.hnsc_comparison"
      ]
    }
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