{
  "_id": "6ae75897818c9e3ef6dbbf07",
  "Package": "clippda",
  "Title": "A package for the clinical proteomic profiling data analysis",
  "Version": "1.63.0",
  "Date": "2011-09-16",
  "Author": "Stephen Nyangoma",
  "Description": "Methods for the nalysis of data from clinical proteomic\nprofiling studies. The focus is on the studies of human\nsubjects, which are often observational case-control by design\nand have technical replicates. A method for sample size\ndetermination for planning these studies is proposed. It\nincorporates routines for adjusting for the expected\nheterogeneities and imbalances in the data and the\nwithin-sample replicate correlations.",
  "Maintainer": "Stephen Nyangoma <s.o.nyangoma@bham.ac.uk>",
  "License": "GPL (>=2)",
  "Collate": "checkNo.replicates.R sampleClusteredData.R\nsampleSize3DscatterPlots.R sampleSizeContourPlots.R\nmostSimilarTwo.R negativeIntensitiesCorrection.R\nphenoDataFrame.R preProcRepeatedPeakData.R spectrumFilter.R f.R\nztwo.R ZvaluescasesVcontrolsPlots.R ZvaluesfrommultinomPlots.R\nbetweensampleVariance.Generic.R fisherInformation.Generic.R\nproteomicsExprsData.Generic.R proteomicspData.Generic.R\nreplicateCorrelations.Generic.R sampleSizeGeneric.R\nsampleSizeParameters.Generic.R\nsample_technicalVaraiance.Generic.R\naclinicalProteomicData.class.R aclinicalProteomicData.methods.R\nbetweensampleVariance.Method.R fisherInformation.Method.R\nproteomicsExprsData.Method.R proteomicspData.Method.R\nreplicateCorrelations.Method.R sampleSizeMethod.R\nsampleSizeParameters.Method.R sample_technicalVariance.Method.R",
  "LazyLoad": "yes",
  "ZipData": "yes",
  "URL": "http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml",
  "Packaged": {
    "Date": "2026-11-01 13:24:43 UTC",
    "User": "root"
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  "biocViews": "Proteomics, OneChannel, Preprocessing,\nDifferentialExpression, MultipleComparison",
  "Config/pak/sysreqs": "cmake libfreetype6-dev libglu1-mesa-dev make\ntexlive libpng-dev libuv1-dev libgl1-mesa-dev zlib1g-dev",
  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:34:12 UTC",
  "RemoteUrl": "https://github.com/bioc/clippda",
  "RemoteRef": "HEAD",
  "RemoteSha": "4bbcccb83f102a62dab06db8e1d024da30cc0bd9",
  "NeedsCompilation": "no",
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  "_expires": "2027-02-09T15:19:48.000Z",
  "_created": "2026-11-01T13:24:43.000Z",
  "_published": "2026-11-01T15:19:50.901Z",
  "_upstream": "https://github.com/bioc/clippda",
  "_status": "success",
  "_maintainer": {
    "name": "Stephen Nyangoma",
    "email": "s.o.nyangoma@bham.ac.uk"
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    "sampleClusteredData",
    "sampleSize",
    "sampleSize3DscatterPlots",
    "sampleSizeContourPlots",
    "sampleSizeParameters",
    "show",
    "spectrumFilter",
    "ztwo",
    "ZvaluescasesVcontrolsPlots",
    "ZvaluesfrommultinomPlots"
  ],
  "_datasets": [
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      "name": "liver_pheno",
      "title": "A dataframe of phenotypic information",
      "object": "liver_pheno",
      "file": "liver_pheno.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "SampleTag",
        "tumor",
        "sex"
      ],
      "rows": 131,
      "table": true,
      "tojson": true
    },
    {
      "name": "liverdata",
      "title": "A dataframe of the protein expression data, peak information, and sample information",
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      "file": "liverdata.rda",
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      ],
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      ],
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      "object": "pheno_urine",
      "file": "pheno_urine.rda",
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        "Sex",
        "stage",
        "Protein_conc"
      ],
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      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "clippda-package",
      "title": "A package for clinical proteomics profiling data analysis",
      "topics": [
        "clippda-package",
        "clippda"
      ]
    },
    {
      "page": "aclinicalProteomicsData-class",
      "title": "Class \"aclinicalProteomicsData\"",
      "topics": [
        "aclinicalProteomicsData",
        "aclinicalProteomicsData-class"
      ]
    },
    {
      "page": "aclinicalProteomicsData-methods",
      "title": "S4 method for the aclinicalProteomicsData class",
      "topics": [
        "aclinicalProteomicsData-methods"
      ]
    },
    {
      "page": "betweensampleVariance",
      "title": "A generic function for computing the biological variance and mean differences between cases and controls",
      "topics": [
        "betweensampleVariance",
        "biologicalVariance"
      ]
    },
    {
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      "title": "Methods for Function betweensampleVariance",
      "topics": [
        "betweensampleVariance,aclinicalProteomicsData-method",
        "betweensampleVariance-methods"
      ]
    },
    {
      "page": "checkNo.replicates",
      "title": "A function to detect disparity in the number of replicates across assays",
      "topics": [
        "checkNo.replicates"
      ]
    },
    {
      "page": "f",
      "title": "A function to compute adjustments for the effct of covariates (Z values) for an experiment with a binary exposure and a binary confounder",
      "topics": [
        "f"
      ]
    },
    {
      "page": "fisherInformation",
      "title": "A generic function to compute the heterogeneity correction factor in sample size calculations",
      "topics": [
        "fisherInformation"
      ]
    },
    {
      "page": "fisherInformation-methods",
      "title": "Methods for Function fisherInformation",
      "topics": [
        "fisherInformation,aclinicalProteomicsData-method",
        "fisherInformation-methods"
      ]
    },
    {
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      "title": "A dataframe of phenotypic information",
      "topics": [
        "liver_pheno"
      ]
    },
    {
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      "title": "A dataframe of the protein expression data, peak information, and sample information",
      "topics": [
        "liverdata"
      ]
    },
    {
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      "title": "A dataframe of the protein expression data, peak information and sample information",
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      "title": "A function which indentifies two columns of a matrix, or dataframe, with the highest pairwise positive correlations",
      "topics": [
        "mostSimilarTwo"
      ]
    },
    {
      "page": "negativeIntensitiesCorrection",
      "title": "A function to correct the data for the negative intensities caused by the normalization and background correction procedures of mass spectrometry data",
      "topics": [
        "negativeIntensitiesCorrection"
      ]
    },
    {
      "page": "pheno_urine",
      "title": "A dataframe of phenotypic information",
      "topics": [
        "pheno_urine"
      ]
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    {
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      "title": "A generic function to set classes for the variables in the dataframe of phenotypic information.",
      "topics": [
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    },
    {
      "page": "preProcRepeatedPeakData",
      "title": "A function to pre-process repeated raw peak data",
      "topics": [
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      ]
    },
    {
      "page": "proteomicsExprsData",
      "title": "A generic fuction to extract duplicate SELDI data from an object of aclinicalProteomicsData class in the same format as the data from Biomarkers wizard",
      "topics": [
        "proteomicsExprsData"
      ]
    },
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      "page": "proteomicsExprsData-methods",
      "title": "Methods for Function proteomicsExprsData",
      "topics": [
        "proteomicsExprsData,aclinicalProteomicsData-method",
        "proteomicsExprsData-methods"
      ]
    },
    {
      "page": "proteomicspData",
      "title": "A function to extract a dataframe of phenotypic information from an object of aclinicalProteomicsData class",
      "topics": [
        "proteomicspData"
      ]
    },
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      "title": "Methods for Function proteomicspData",
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        "proteomicspData,aclinicalProteomicsData-method",
        "proteomicspData-methods"
      ]
    },
    {
      "page": "replicateCorrelations",
      "title": "A generic function to compute intraclass correlations",
      "topics": [
        "intraclassCorrelations",
        "replicateCorrelations"
      ]
    },
    {
      "page": "replicateCorrelations-methods",
      "title": "Methods for Function replicateCorrelations",
      "topics": [
        "replicateCorrelations,aclinicalProteomicsData-method",
        "replicateCorrelations-methods"
      ]
    },
    {
      "page": "sample_technicalVariance",
      "title": "A generic function for computing the technical variance",
      "topics": [
        "sample_technicalVariance",
        "withinsampleVariance"
      ]
    },
    {
      "page": "sample_technicalVariance-methods",
      "title": "Methods for Function sample_technicalVariance",
      "topics": [
        "sample_technicalVariance,aclinicalProteomicsData-method",
        "sample_technicalVariance-methods"
      ]
    },
    {
      "page": "sampleClusteredData",
      "title": "A function to arrange the data in sample-wise pairs",
      "topics": [
        "sampleClusteredData"
      ]
    },
    {
      "page": "sampleSize",
      "title": "A function for sample size calculations",
      "topics": [
        "sampleSize"
      ]
    },
    {
      "page": "sampleSize-methods",
      "title": "~~ Methods for Function sampleSize",
      "topics": [
        "sampleSize,aclinicalProteomicsData,numeric,numeric-method",
        "sampleSize-methods"
      ]
    },
    {
      "page": "sampleSize3DscatterPlots",
      "title": "A function for 3D display of sample size in a multi parameter space",
      "topics": [
        "sampleSize3DscatterPlots"
      ]
    },
    {
      "page": "sampleSizeContourPlots",
      "title": "A function to construct a grid with contours for calculating sample size in multi dimensional parameter space",
      "topics": [
        "sampleSizeContourPlots"
      ]
    },
    {
      "page": "sampleSizeParameters",
      "title": "A generic function to calculate sample size parameters",
      "topics": [
        "sampleSizeParameters"
      ]
    },
    {
      "page": "sampleSizeParameters-methods",
      "title": "~~ Methods for Function sampleSizeParameters",
      "topics": [
        "sampleSizeParameters,aclinicalProteomicsData,numeric,numeric-method",
        "sampleSizeParameters-methods"
      ]
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      "page": "show-methods",
      "title": "Methods for Function show in Package `methods'",
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      "page": "spectrumFilter",
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      "topics": [
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      "page": "ztwo",
      "title": "A function to compute Z values when there are no covariates other than the cancer class",
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      "page": "ZvaluesfrommultinomPlots",
      "title": "A generic functon to plot Density of Z values from a simulation from a multinomial population using the balanced and unbalanced studies and a 3D representaion of the Z values",
      "topics": [
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