{
  "_id": "6a2530074b233be198395a72",
  "Type": "Package",
  "Package": "mt",
  "Title": "Metabolomics Data Analysis Toolbox",
  "Version": "2.0-1.21",
  "Authors@R": "person(given = \"Wanchang\", family = \"Lin\", role = c(\"aut\", \"cre\"),\nemail = \"wanchanglin@hotmail.com\")",
  "Description": "Functions for metabolomics data analysis: data\npreprocessing, orthogonal signal correction, PCA analysis,\nPCA-DA analysis, PLS-DA analysis, classification, feature\nselection, correlation analysis, data visualisation and\nre-sampling strategies.",
  "License": "GPL (>= 2)",
  "URL": "https://github.com/wanchanglin/mt",
  "BugReports": "https://github.com/wanchanglin/mt/issues",
  "Encoding": "UTF-8",
  "LazyLoad": "yes",
  "LazyData": "yes",
  "ZipData": "No",
  "NeedsCompilation": "no",
  "Config/pak/sysreqs": "libjpeg-dev libpng-dev",
  "Repository": "https://wanchanglin.r-universe.dev",
  "Date/Publication": "2025-08-11 10:51:07 UTC",
  "RemoteUrl": "https://github.com/wanchanglin/mt",
  "RemoteRef": "HEAD",
  "RemoteSha": "f87bb7ad81e9674a0a4533c1e34749b138c8b62a",
  "Packaged": {
    "Date": "2026-06-07 08:43:08 UTC",
    "User": "root"
  },
  "Author": "Wanchang Lin [aut, cre]",
  "Maintainer": "Wanchang Lin <wanchanglin@hotmail.com>",
  "MD5sum": "aafd5154469738bf9f88097fa5b0ae14",
  "_user": "wanchanglin",
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  "_created": "2026-06-07T08:43:08.000Z",
  "_published": "2026-06-07T08:47:03.641Z",
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  "_assets": [
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    "extra/citation.json",
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    "extra/contents.json",
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  "_homeurl": "https://github.com/wanchanglin/mt",
  "_realowner": "wanchanglin",
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  "_releases": [
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      "date": "2021-11-15"
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      "date": "2022-02-01"
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      "date": "2024-02-12"
    },
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      "version": "2.0-1.21",
      "date": "2025-08-19"
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  ],
  "_exports": [
    "aam.cl",
    "aam.mcl",
    "accest",
    "binest",
    "boot.err",
    "cl.auc",
    "cl.perf",
    "cl.rate",
    "cl.roc",
    "classifier",
    "combn.pw",
    "cor.cut",
    "cor.hcl",
    "cor.heat",
    "cor.heat.gram",
    "corrgram.circle",
    "corrgram.ellipse",
    "dat.sel",
    "df.summ",
    "feat.agg",
    "feat.freq",
    "feat.mfs",
    "feat.mfs.stab",
    "feat.mfs.stats",
    "feat.rank.re",
    "frank.err",
    "frankvali",
    "fs.anova",
    "fs.auc",
    "fs.bw",
    "fs.cl",
    "fs.cl.1",
    "fs.kruskal",
    "fs.pca",
    "fs.pls",
    "fs.plsvip",
    "fs.plsvip.1",
    "fs.plsvip.2",
    "fs.relief",
    "fs.rf",
    "fs.rf.1",
    "fs.rfe",
    "fs.snr",
    "fs.welch",
    "fs.wilcox",
    "get.fs.len",
    "grpplot",
    "hm.cols",
    "lda_plot_wrap",
    "lda_plot_wrap.1",
    "list2df",
    "maccest",
    "mbinest",
    "mc.anova",
    "mc.fried",
    "mc.norm",
    "mds_plot_wrap",
    "mdsplot",
    "mv.fill",
    "mv.stats",
    "mv.zene",
    "osc",
    "osc_sjoblom",
    "osc_wise",
    "osc_wold",
    "panel.elli",
    "panel.elli.1",
    "panel.outl",
    "panel.smooth.line",
    "pca_plot_wrap",
    "pca.comp",
    "pca.outlier",
    "pca.outlier.1",
    "pca.plot",
    "pcalda",
    "pcaplot",
    "pls_plot_wrap",
    "plsc",
    "plslda",
    "predict.osc",
    "preproc",
    "preproc.const",
    "preproc.sd",
    "pval.reject",
    "pval.test",
    "save.tab",
    "shrink.list",
    "stats.mat",
    "stats.vec",
    "trainind",
    "tune.pcalda",
    "tune.plsc",
    "tune.plslda",
    "un.list",
    "valipars",
    "vec.summ",
    "vec.summ.1"
  ],
  "_datasets": [
    {
      "name": "abr1",
      "title": "abr1 Data",
      "object": "abr1",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "abr1",
      "title": "abr1 Data",
      "topics": [
        "abr1"
      ]
    },
    {
      "page": "accest",
      "title": "Estimate Classification Accuracy By Resampling Method",
      "topics": [
        "aam.cl",
        "aam.mcl",
        "accest",
        "accest.default",
        "accest.formula",
        "print.accest",
        "print.summary.accest",
        "summary.accest"
      ]
    },
    {
      "page": "binest",
      "title": "Binary Classification",
      "topics": [
        "binest"
      ]
    },
    {
      "page": "boot.err",
      "title": "Calculate .632 and .632+ Bootstrap Error Rate",
      "topics": [
        "boot.err"
      ]
    },
    {
      "page": "boxplot.frankvali",
      "title": "Boxplot Method for Class 'frankvali'",
      "topics": [
        "boxplot.frankvali"
      ]
    },
    {
      "page": "boxplot.maccest",
      "title": "Boxplot Method for Class 'maccest'",
      "topics": [
        "boxplot.maccest"
      ]
    },
    {
      "page": "cl.perf",
      "title": "Assess Classification Performances",
      "topics": [
        "cl.auc",
        "cl.perf",
        "cl.rate",
        "cl.roc"
      ]
    },
    {
      "page": "classifier",
      "title": "Wrapper Function for Classifiers",
      "topics": [
        "classifier"
      ]
    },
    {
      "page": "cor.util",
      "title": "Correlation Analysis Utilities",
      "topics": [
        "cor.cut",
        "cor.hcl",
        "cor.heat",
        "cor.heat.gram",
        "corrgram.circle",
        "corrgram.ellipse",
        "hm.cols"
      ]
    },
    {
      "page": "dat.sel",
      "title": "Generate Pairwise Data Set",
      "topics": [
        "combn.pw",
        "dat.sel"
      ]
    },
    {
      "page": "data.visualisation",
      "title": "Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA",
      "topics": [
        "lda_plot_wrap",
        "lda_plot_wrap.1",
        "mds_plot_wrap",
        "pca_plot_wrap",
        "pls_plot_wrap"
      ]
    },
    {
      "page": "df.util",
      "title": "Summary Utilities",
      "topics": [
        "df.summ",
        "vec.summ",
        "vec.summ.1"
      ]
    },
    {
      "page": "feat.agg",
      "title": "Rank aggregation by Borda count algorithm",
      "topics": [
        "feat.agg"
      ]
    },
    {
      "page": "feat.freq",
      "title": "Frequency and Stability of Feature Selection",
      "topics": [
        "feat.freq"
      ]
    },
    {
      "page": "feat.mfs",
      "title": "Multiple Feature Selection",
      "topics": [
        "feat.mfs",
        "feat.mfs.stab",
        "feat.mfs.stats"
      ]
    },
    {
      "page": "feat.rank.re",
      "title": "Feature Ranking with Resampling Method",
      "topics": [
        "feat.rank.re"
      ]
    },
    {
      "page": "frank.err",
      "title": "Feature Ranking and Validation on Feature Subset",
      "topics": [
        "frank.err"
      ]
    },
    {
      "page": "frankvali",
      "title": "Estimates Feature Ranking Error Rate with Resampling",
      "topics": [
        "frankvali",
        "frankvali.default",
        "frankvali.formula",
        "fs.cl",
        "fs.cl.1",
        "print.frankvali",
        "print.summary.frankvali",
        "summary.frankvali"
      ]
    },
    {
      "page": "fs.anova",
      "title": "Feature Selection Using ANOVA",
      "topics": [
        "fs.anova"
      ]
    },
    {
      "page": "fs.auc",
      "title": "Feature Selection Using Area under Receiver Operating Curve (AUC)",
      "topics": [
        "fs.auc"
      ]
    },
    {
      "page": "fs.bw",
      "title": "Feature Selection Using Between-Group to Within-Group (BW) Ratio",
      "topics": [
        "fs.bw"
      ]
    },
    {
      "page": "fs.kruskal",
      "title": "Feature Selection Using Kruskal-Wallis Test",
      "topics": [
        "fs.kruskal"
      ]
    },
    {
      "page": "fs.pca",
      "title": "Feature Selection by PCA",
      "topics": [
        "fs.pca"
      ]
    },
    {
      "page": "fs.pls",
      "title": "Feature Selection Using PLS",
      "topics": [
        "fs.pls",
        "fs.plsvip",
        "fs.plsvip.1",
        "fs.plsvip.2"
      ]
    },
    {
      "page": "fs.relief",
      "title": "Feature Selection Using RELIEF Method",
      "topics": [
        "fs.relief"
      ]
    },
    {
      "page": "fs.rf",
      "title": "Feature Selection Using Random Forests (RF)",
      "topics": [
        "fs.rf",
        "fs.rf.1"
      ]
    },
    {
      "page": "fs.rfe",
      "title": "Feature Selection Using SVM-RFE",
      "topics": [
        "fs.rfe"
      ]
    },
    {
      "page": "fs.snr",
      "title": "Feature Selection Using Signal-to-Noise Ratio (SNR)",
      "topics": [
        "fs.snr"
      ]
    },
    {
      "page": "fs.welch",
      "title": "Feature Selection Using Welch Test",
      "topics": [
        "fs.welch"
      ]
    },
    {
      "page": "fs.wilcox",
      "title": "Feature Selection Using Wilcoxon Test",
      "topics": [
        "fs.wilcox"
      ]
    },
    {
      "page": "get.fs.len",
      "title": "Get Length of Feature Subset for Validation",
      "topics": [
        "get.fs.len"
      ]
    },
    {
      "page": "grpplot",
      "title": "Plot Matrix-Like Object by Group",
      "topics": [
        "grpplot"
      ]
    },
    {
      "page": "list.util",
      "title": "List Manipulation Utilities",
      "topics": [
        "list2df",
        "shrink.list",
        "un.list"
      ]
    },
    {
      "page": "maccest",
      "title": "Estimation of Multiple Classification Accuracy",
      "topics": [
        "maccest",
        "maccest.default",
        "maccest.formula",
        "print.maccest",
        "print.summary.maccest",
        "summary.maccest"
      ]
    },
    {
      "page": "mbinest",
      "title": "Binary Classification by Multiple Classifier",
      "topics": [
        "mbinest"
      ]
    },
    {
      "page": "mc.anova",
      "title": "Multiple Comparison by 'ANOVA' and Pairwise Comparison by 'HSDTukey Test'",
      "topics": [
        "mc.anova"
      ]
    },
    {
      "page": "mc.fried",
      "title": "Multiple Comparison by 'Friedman Test' and Pairwise Comparison by 'Wilcoxon Test'",
      "topics": [
        "mc.fried"
      ]
    },
    {
      "page": "mc.norm",
      "title": "Normality Test by Shapiro-Wilk Test",
      "topics": [
        "mc.norm"
      ]
    },
    {
      "page": "mdsplot",
      "title": "Plot Classical Multidimensional Scaling",
      "topics": [
        "mdsplot"
      ]
    },
    {
      "page": "mv.util",
      "title": "Missing Value Utilities",
      "topics": [
        "mv.fill",
        "mv.stats",
        "mv.zene"
      ]
    },
    {
      "page": "osc",
      "title": "Orthogonal Signal Correction (OSC)",
      "topics": [
        "osc",
        "osc.default",
        "osc.formula",
        "print.osc",
        "print.summary.osc",
        "summary.osc"
      ]
    },
    {
      "page": "osc_sjoblom",
      "title": "Orthogonal Signal Correction (OSC) Approach by Sjoblom et al.",
      "topics": [
        "osc_sjoblom"
      ]
    },
    {
      "page": "osc_wise",
      "title": "Orthogonal Signal Correction (OSC) Approach by Wise and Gallagher.",
      "topics": [
        "osc_wise"
      ]
    },
    {
      "page": "osc_wold",
      "title": "Orthogonal Signal Correction (OSC) Approach by Wold et al.",
      "topics": [
        "osc_wold"
      ]
    },
    {
      "page": "panel.elli",
      "title": "Panel Function for Plotting Ellipse and outlier",
      "topics": [
        "panel.elli",
        "panel.elli.1",
        "panel.outl"
      ]
    },
    {
      "page": "panel.smooth.line",
      "title": "Panel Function for Plotting Regression Line",
      "topics": [
        "panel.smooth.line"
      ]
    },
    {
      "page": "pca.outlier",
      "title": "Outlier detection by PCA",
      "topics": [
        "pca.outlier",
        "pca.outlier.1"
      ]
    },
    {
      "page": "pcalda",
      "title": "Classification with PCADA",
      "topics": [
        "pcalda",
        "pcalda.default",
        "pcalda.formula",
        "print.pcalda",
        "print.summary.pcalda",
        "summary.pcalda"
      ]
    },
    {
      "page": "pcaplot",
      "title": "Plot Function for PCA with Grouped Values",
      "topics": [
        "pca.comp",
        "pca.plot",
        "pcaplot"
      ]
    },
    {
      "page": "plot.accest",
      "title": "Plot Method for Class 'accest'",
      "topics": [
        "plot.accest"
      ]
    },
    {
      "page": "plot.maccest",
      "title": "Plot Method for Class 'maccest'",
      "topics": [
        "plot.maccest"
      ]
    },
    {
      "page": "plot.pcalda",
      "title": "Plot Method for Class 'pcalda'",
      "topics": [
        "plot.pcalda"
      ]
    },
    {
      "page": "plot.plsc",
      "title": "Plot Method for Class 'plsc' or 'plslda'",
      "topics": [
        "plot.plsc",
        "plot.plslda"
      ]
    },
    {
      "page": "plsc",
      "title": "Classification with PLSDA",
      "topics": [
        "plsc",
        "plsc.default",
        "plsc.formula",
        "plslda",
        "plslda.default",
        "plslda.formula",
        "print.plsc",
        "print.plslda",
        "print.summary.plsc",
        "print.summary.plslda",
        "summary.plsc",
        "summary.plslda"
      ]
    },
    {
      "page": "predict.osc",
      "title": "Predict Method for Class 'osc'",
      "topics": [
        "predict.osc"
      ]
    },
    {
      "page": "predict.pcalda",
      "title": "Predict Method for Class 'pcalda'",
      "topics": [
        "predict.pcalda"
      ]
    },
    {
      "page": "predict.plsc",
      "title": "Predict Method for Class 'plsc' or 'plslda'",
      "topics": [
        "predict.plsc",
        "predict.plslda"
      ]
    },
    {
      "page": "preproc",
      "title": "Pre-process Data Set",
      "topics": [
        "preproc",
        "preproc.const",
        "preproc.sd"
      ]
    },
    {
      "page": "pval.util",
      "title": "P-values Utilities",
      "topics": [
        "pval.reject",
        "pval.test"
      ]
    },
    {
      "page": "save.tab",
      "title": "Save List of Data Frame or Matrix into CSV File",
      "topics": [
        "save.tab"
      ]
    },
    {
      "page": "stats.util",
      "title": "Statistical Summary Utilities for Two-Classes Data",
      "topics": [
        "stats.mat",
        "stats.vec"
      ]
    },
    {
      "page": "trainind",
      "title": "Generate Index of Training Samples",
      "topics": [
        "trainind"
      ]
    },
    {
      "page": "tune.func",
      "title": "Functions for Tuning Appropriate Number of Components",
      "topics": [
        "tune.func",
        "tune.pcalda",
        "tune.plsc",
        "tune.plslda"
      ]
    },
    {
      "page": "valipars",
      "title": "Generate Control Parameters for Resampling",
      "topics": [
        "valipars"
      ]
    }
  ],
  "_readme": "https://github.com/wanchanglin/mt/raw/HEAD/README.md",
  "_rundeps": [
    "class",
    "deldir",
    "e1071",
    "ellipse",
    "interp",
    "jpeg",
    "lattice",
    "latticeExtra",
    "MASS",
    "pls",
    "png",
    "proxy",
    "randomForest",
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