{
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  "Package": "combi",
  "Type": "Package",
  "Title": "Compositional omics model based visual integration",
  "Version": "1.25.0",
  "Authors@R": "c(person(given = \"Stijn\", family = \"Hawinkel\",\nemail = \"stijn.hawinkel@psb.ugent.be\",\nrole=c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0002-4501-5180\")))",
  "Description": "This explorative ordination method combines\nquasi-likelihood estimation, compositional regression models\nand latent variable models for integrative visualization of\nseveral omics datasets. Both unconstrained and constrained\nintegration are available. The results are shown as\ninterpretable, compositional multiplots.",
  "License": "GPL-2",
  "Encoding": "UTF-8",
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  "VignetteBuilder": "knitr",
  "biocViews": "Metagenomics, DimensionReduction, Microbiome, Visualization,\nMetabolomics",
  "BugReports": "https://github.com/CenterForStatistics-UGent/combi/issues",
  "Config/pak/sysreqs": "libglpk-dev libicu-dev libxml2-dev zlib1g-dev",
  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:52:10 UTC",
  "RemoteUrl": "https://github.com/bioc/combi",
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  "NeedsCompilation": "no",
  "Packaged": {
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    "User": "root"
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  "Author": "Stijn Hawinkel [cre, aut] (ORCID:\n<https://orcid.org/0000-0002-4501-5180>)",
  "Maintainer": "Stijn Hawinkel <stijn.hawinkel@psb.ugent.be>",
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  "_expires": "2026-11-03T06:28:27.000Z",
  "_created": "2026-07-26T06:15:01.000Z",
  "_published": "2026-07-26T06:28:28.808Z",
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  "_realowner": "bioc",
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  "_exports": [
    "addLink",
    "checkMeanVarTrend",
    "combi",
    "convPlot",
    "extractCoords",
    "extractData",
    "inflPlot"
  ],
  "_datasets": [
    {
      "name": "zhangMetabo",
      "title": "Metabolomes of mice that underwent Pulsed Antibiotic Treatment (PAT) and controls",
      "object": "Zhang",
      "file": "Zhang.RData",
      "class": [
        "SummarizedExperiment"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
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      "object": "Zhang",
      "file": "Zhang.RData",
      "class": [
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      ],
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        "Time",
        "ABX",
        "Sample.Weight"
      ],
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    {
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      "title": "Microbiomes of mice that underwent Pulsed Antibiotic Treatment (PAT) and controls",
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      "file": "Zhang.RData",
      "class": [
        "phyloseq"
      ],
      "fields": [],
      "table": false,
      "tojson": false
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  "_help": [
    {
      "page": "addLink",
      "title": "Add a link on a compositional plot",
      "topics": [
        "addLink"
      ]
    },
    {
      "page": "arrayMult",
      "title": "Array multiplication",
      "topics": [
        "arrayMult"
      ]
    },
    {
      "page": "buildCentMat",
      "title": "A function to build a centering matrix based on a dataframe",
      "topics": [
        "buildCentMat"
      ]
    },
    {
      "page": "buildCompMat",
      "title": "Build the composition matrix for a certain dimension m dimensions",
      "topics": [
        "buildCompMat"
      ]
    },
    {
      "page": "buildConfMat",
      "title": "Build confounder design matrices with and without intercepts",
      "topics": [
        "buildConfMat"
      ]
    },
    {
      "page": "buildCovMat",
      "title": "A function to build the covariate matrix of the constraints",
      "topics": [
        "buildCovMat"
      ]
    },
    {
      "page": "buildEmptyJac",
      "title": "Prepare an empty Jacobian matrix, with useful entries prefilled. In case of distribution \"gaussian\", it returns the lhs matrix of the linear system for finding the feature paramters",
      "topics": [
        "buildEmptyJac"
      ]
    },
    {
      "page": "buildMarginalOffset",
      "title": "Build an offset matrix from an marginal model object",
      "topics": [
        "buildMarginalOffset"
      ]
    },
    {
      "page": "buildMu",
      "title": "A function to build the mu matrix",
      "topics": [
        "buildMu"
      ]
    },
    {
      "page": "buildMuMargins",
      "title": "Build the marginal mu matrix",
      "topics": [
        "buildMuMargins"
      ]
    },
    {
      "page": "buildOffsetModel",
      "title": "Build a marginal offset matrix given a model",
      "topics": [
        "buildOffsetModel"
      ]
    },
    {
      "page": "checkAlias",
      "title": "Check for alias structures in a dataframe, and throw an error when one is found",
      "topics": [
        "checkAlias"
      ]
    },
    {
      "page": "checkMeanVarTrend",
      "title": "Quickly check if the mean variance trend provides a good fit",
      "topics": [
        "checkMeanVarTrend"
      ]
    },
    {
      "page": "checkMonotonicity",
      "title": "Check for monotonicity in compositional datasets fro given dimensions",
      "topics": [
        "checkMonotonicity"
      ]
    },
    {
      "page": "combi",
      "title": "Perform model-based data integration",
      "topics": [
        "combi"
      ]
    },
    {
      "page": "convPlot",
      "title": "Plot the convegrence of the different parameter estimates in a line plot",
      "topics": [
        "convPlot"
      ]
    },
    {
      "page": "deriv2LagrangianFeatures",
      "title": "The score function to estimate the latent variables",
      "topics": [
        "deriv2LagrangianFeatures"
      ]
    },
    {
      "page": "deriv2LagrangianLatentVars",
      "title": "The jacobian function to estimate the latent variables",
      "topics": [
        "deriv2LagrangianLatentVars"
      ]
    },
    {
      "page": "deriv2LagrangianLatentVarsConstr",
      "title": "The score function to estimate the latent variables",
      "topics": [
        "deriv2LagrangianLatentVarsConstr"
      ]
    },
    {
      "page": "derivLagrangianFeatures",
      "title": "The score function to estimate the feature parameters",
      "topics": [
        "derivLagrangianFeatures"
      ]
    },
    {
      "page": "derivLagrangianLatentVars",
      "title": "The score function to estimate the latent variables",
      "topics": [
        "derivLagrangianLatentVars"
      ]
    },
    {
      "page": "derivLagrangianLatentVarsConstr",
      "title": "The score function to estimate the latent variables",
      "topics": [
        "derivLagrangianLatentVarsConstr"
      ]
    },
    {
      "page": "estFeatureParameters",
      "title": "Estimate the feature parameters",
      "topics": [
        "estFeatureParameters"
      ]
    },
    {
      "page": "estIndepModel",
      "title": "Estimate the independence model belonging to one view",
      "topics": [
        "estIndepModel"
      ]
    },
    {
      "page": "estLatentVars",
      "title": "Estimate the latent variables",
      "topics": [
        "estLatentVars"
      ]
    },
    {
      "page": "estMeanVarTrend",
      "title": "Estimate a column-wise mean-variance trend",
      "topics": [
        "estMeanVarTrend"
      ]
    },
    {
      "page": "estOff",
      "title": "Estimate the row/column parameters of the independence model",
      "topics": [
        "estOff"
      ]
    },
    {
      "page": "extractCoords",
      "title": "Extract coordinates from fitted object",
      "topics": [
        "extractCoords"
      ]
    },
    {
      "page": "extractData",
      "title": "Helper function to extract data matrix from phyloseq, expressionset objects etc. Also filers out all zero rows",
      "topics": [
        "extractData"
      ]
    },
    {
      "page": "extractMat",
      "title": "A function to extract a data matrix from a number of objects",
      "topics": [
        "extractMat",
        "extractMat,ExpressionSet-method",
        "extractMat,matrix-method",
        "extractMat,SummarizedExperiment-method"
      ]
    },
    {
      "page": "filterConfounders",
      "title": "Filter out the effect of known confounders",
      "topics": [
        "filterConfounders"
      ]
    },
    {
      "page": "getInflLatentVar",
      "title": "Extract the influence on the estimation of the latent variable",
      "topics": [
        "getInflLatentVar"
      ]
    },
    {
      "page": "gramSchmidtOrth",
      "title": "Gram schimdt orhtogonalize a with respect to b, and normalize",
      "topics": [
        "gramSchmidtOrth"
      ]
    },
    {
      "page": "indentPlot",
      "title": "Functions to indent the plot to include the entire labels",
      "topics": [
        "indentPlot"
      ]
    },
    {
      "page": "inflPlot",
      "title": "A ggplot line plot showing the influences",
      "topics": [
        "inflPlot"
      ]
    },
    {
      "page": "influence.combi",
      "title": "Evaluate the influence function",
      "topics": [
        "influence.combi"
      ]
    },
    {
      "page": "jacConfounders",
      "title": "Jacobian when estimating confounder variables",
      "topics": [
        "jacConfounders"
      ]
    },
    {
      "page": "jacConfoundersComp",
      "title": "Jacobian for conditioning under compositionality",
      "topics": [
        "jacConfoundersComp"
      ]
    },
    {
      "page": "jacFeatures",
      "title": "Evaluate the jacobian for estimating the feature parameters for one view",
      "topics": [
        "jacFeatures"
      ]
    },
    {
      "page": "jacLatentVars",
      "title": "Evaluate the jacobian for estimating the latent variable for one view",
      "topics": [
        "jacLatentVars"
      ]
    },
    {
      "page": "jacLatentVarsConstr",
      "title": "Evaluate the jacobian for estimating the latent variable for one view for constrained ordination",
      "topics": [
        "jacLatentVarsConstr"
      ]
    },
    {
      "page": "plot.combi",
      "title": "Make multiplots of the data integration object",
      "topics": [
        "plot.combi"
      ]
    },
    {
      "page": "polyHorner",
      "title": "Horner's method to evaluate a polynomial, copied from the polynom package. the most efficient way",
      "topics": [
        "polyHorner"
      ]
    },
    {
      "page": "predictSpline",
      "title": "A custom spline prediction function, extending linearly with a slope such that prediction never drops below first bisectant",
      "topics": [
        "predictSpline"
      ]
    },
    {
      "page": "prepareJacMat",
      "title": "prepare the jacobian matrix",
      "topics": [
        "prepareJacMat"
      ]
    },
    {
      "page": "prepareJacMatComp",
      "title": "prepare the jacobian for the latent variabels compostional",
      "topics": [
        "prepareJacMatComp"
      ]
    },
    {
      "page": "prepareScoreMat",
      "title": "Prepare a helper matrix for score function evaluation under quasi-likelihood",
      "topics": [
        "prepareScoreMat"
      ]
    },
    {
      "page": "print.combi",
      "title": "Print an overview of a fitted combi x",
      "topics": [
        "print.combi"
      ]
    },
    {
      "page": "quasiJacIndep",
      "title": "The jacobian for column offset estimation",
      "topics": [
        "quasiJacIndep"
      ]
    },
    {
      "page": "quasiScoreIndep",
      "title": "Quasi score equations for column offset parameters of sequence count data",
      "topics": [
        "quasiScoreIndep"
      ]
    },
    {
      "page": "rowMultiply",
      "title": "A function to efficiently row multiply a matrix and a vector",
      "topics": [
        "rowMultiply"
      ]
    },
    {
      "page": "scaleCoords",
      "title": "A helper function to rescale coordinates",
      "topics": [
        "scaleCoords"
      ]
    },
    {
      "page": "scoreConfounders",
      "title": "Score functions for confounder variables",
      "topics": [
        "scoreConfounders"
      ]
    },
    {
      "page": "scoreConfoundersComp",
      "title": "Score equations for conditioning under compositionality",
      "topics": [
        "scoreConfoundersComp"
      ]
    },
    {
      "page": "scoreFeatureParams",
      "title": "Evaluate the score functions for the estimation of the feature parameters for a single dataset",
      "topics": [
        "scoreFeatureParams"
      ]
    },
    {
      "page": "scoreLatentVars",
      "title": "Evaluate the score functions for the estimation of the latent variables for a single dataset",
      "topics": [
        "scoreLatentVars"
      ]
    },
    {
      "page": "seqM",
      "title": "A small auxiliary function for the indices of the lagrange multipliers",
      "topics": [
        "seqM"
      ]
    },
    {
      "page": "trimOnConfounders",
      "title": "Trim based on confounders to avoid taxa with only zero counts",
      "topics": [
        "trimOnConfounders"
      ]
    },
    {
      "page": "zhangMetabo",
      "title": "Metabolomes of mice that underwent Pulsed Antibiotic Treatment (PAT) and controls",
      "topics": [
        "zhangMetabo"
      ]
    },
    {
      "page": "zhangMetavars",
      "title": "Baseline sample variables of PAT and control mice",
      "topics": [
        "zhangMetavars"
      ]
    },
    {
      "page": "zhangMicrobio",
      "title": "Microbiomes of mice that underwent Pulsed Antibiotic Treatment (PAT) and controls",
      "topics": [
        "zhangMicrobio"
      ]
    }
  ],
  "_readme": "https://github.com/bioc/combi/raw/HEAD/README.md",
  "_rundeps": [
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    "vegan",
    "viridisLite",
    "withr",
    "XVector"
  ],
  "_vignettes": [
    {
      "source": "combi.Rmd",
      "filename": "combi.html",
      "title": "Manual for the combi pacakage",
      "engine": "knitr::rmarkdown",
      "headings": [
        "combi package: vignette",
        "Introduction",
        "Installation",
        "Unconstrained integration",
        "Adding projections",
        "Coordinates",
        "Constrained integration",
        "Diagnostics",
        "FAQ",
        "Why are not all my samples shown in the constrained ordination?",
        "The combi function crashes, what should I do",
        "Session info"
      ],
      "created": "2020-03-18 08:44:18",
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