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  "Package": "FDboost",
  "Title": "Boosting Functional Regression Models",
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  "Date": "2026-03-24",
  "Authors@R": "c(\nperson(\"Sarah\", \"Brockhaus\", , \"Sarah.Brockhaus@stat.uni-muenchen.de\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-9484-7488\")),\nperson(\"David\", \"Ruegamer\", , \"david.ruegamer@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-8772-9202\")),\nperson(\"Almond\", \"Stoecker\", , \"almond.stoecker@hu-berlin.de\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-9160-2397\")),\nperson(\"Torsten\", \"Hothorn\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0001-8301-0471\")),\nperson(\"with contributions by many others\", \"(see inst/CONTRIBUTIONS)\", role = \"ctb\")\n)",
  "Description": "Regression models for functional data, i.e.,\nscalar-on-function, function-on-scalar and function-on-function\nregression models, are fitted by a component-wise gradient\nboosting algorithm.  For a manual on how to use 'FDboost', see\nBrockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.",
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  "Author": "Sarah Brockhaus [aut] (ORCID: <https://orcid.org/0000-0001-9484-7488>),\nDavid Ruegamer [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-8772-9202>),\nAlmond Stoecker [aut] (ORCID: <https://orcid.org/0000-0001-9160-2397>),\nTorsten Hothorn [ctb] (ORCID: <https://orcid.org/0000-0001-8301-0471>),\nwith contributions by many others (see inst/CONTRIBUTIONS) [ctb]",
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    "%A0%",
    "%Xa0%",
    "%Xc%",
    "applyFolds",
    "bbsc",
    "bconcurrent",
    "bfpc",
    "bhist",
    "bhistx",
    "bolsc",
    "bootstrapCI",
    "brandomc",
    "bsignal",
    "clr",
    "cvLong",
    "cvMa",
    "factorize",
    "FDboost",
    "FDboostLSS",
    "funMRD",
    "funMSE",
    "funplot",
    "funRsquared",
    "getArgvals",
    "getArgvalsLab",
    "getId",
    "getIdLab",
    "getTime",
    "getTimeLab",
    "getX",
    "getXLab",
    "hmatrix",
    "integrationWeights",
    "integrationWeightsLeft",
    "is.hmatrix",
    "o_control",
    "plotPredCoef",
    "plotPredicted",
    "plotResiduals",
    "reweightData",
    "subset_hmatrix",
    "truncateTime",
    "validateFDboost",
    "wide2long"
  ],
  "_datasets": [
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      "name": "birthDistribution",
      "title": "Densities of live births in Germany",
      "object": "birthDistribution",
      "file": "birthDistribution.RData",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "emotion",
      "title": "EEG and EMG recordings in a computerised gambling study",
      "object": "emotion",
      "file": "emotion.RData",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "fuelSubset",
      "title": "Spectral data of fossil fuels",
      "object": "fuelSubset",
      "file": "fuelSubset.RData",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "viscosity",
      "title": "Viscosity of resin over time",
      "object": "viscosity",
      "file": "viscosity.RData",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "FDboost-package",
      "title": "FDboost: Boosting Functional Regression Models",
      "topics": [
        "FDboost-package",
        "FDboost_package",
        "package-FDboost"
      ]
    },
    {
      "page": "sub-.hmatrix",
      "title": "Extract or replace parts of a hmatrix-object",
      "topics": [
        "[.hmatrix"
      ]
    },
    {
      "page": "grapes-Xc-grapes",
      "title": "Constrained row tensor product",
      "topics": [
        "%Xc%"
      ]
    },
    {
      "page": "anisotropic_Kronecker",
      "title": "Kronecker product or row tensor product of two base-learners with anisotropic penalty",
      "topics": [
        "%A%",
        "%A0%",
        "%Xa0%",
        "anisotropic_Kronecker"
      ]
    },
    {
      "page": "applyFolds",
      "title": "Cross-Validation and Bootstrapping over Curves",
      "topics": [
        "applyFolds",
        "cvLong",
        "cvMa",
        "cvrisk.FDboost"
      ]
    },
    {
      "page": "bbsc",
      "title": "Constrained Base-learners for Scalar Covariates",
      "topics": [
        "bbsc",
        "bolsc",
        "brandomc"
      ]
    },
    {
      "page": "bhistx",
      "title": "Base-learners for Functional Covariates",
      "topics": [
        "bhistx"
      ]
    },
    {
      "page": "birthDistribution",
      "title": "Densities of live births in Germany",
      "topics": [
        "birthDistribution"
      ]
    },
    {
      "page": "bootstrapCI",
      "title": "Function to compute bootstrap confidence intervals",
      "topics": [
        "bootstrapCI"
      ]
    },
    {
      "page": "bsignal",
      "title": "Base-learners for Functional Covariates",
      "topics": [
        "bconcurrent",
        "bfpc",
        "bhist",
        "bsignal"
      ]
    },
    {
      "page": "clr",
      "title": "Clr and inverse clr transformation",
      "topics": [
        "clr"
      ]
    },
    {
      "page": "coef.FDboost",
      "title": "Coefficients of boosted functional regression model",
      "topics": [
        "coef.FDboost"
      ]
    },
    {
      "page": "cvrisk.FDboostLSS",
      "title": "Cross-validation for FDboostLSS",
      "topics": [
        "cvrisk.FDboostLSS"
      ]
    },
    {
      "page": "emotion",
      "title": "EEG and EMG recordings in a computerised gambling study",
      "topics": [
        "emotion"
      ]
    },
    {
      "page": "extract.blg",
      "title": "Extract information of a base-learner",
      "topics": [
        "extract.blg"
      ]
    },
    {
      "page": "factorize",
      "title": "Factorize tensor product model",
      "topics": [
        "factorise",
        "factorize",
        "factorize.FDboost"
      ]
    },
    {
      "page": "FDboost",
      "title": "Model-based Gradient Boosting for Functional Response",
      "topics": [
        "FDboost"
      ]
    },
    {
      "page": "FDboost_fac-class",
      "title": "`FDboost_fac` S3 class for factorized FDboost model components",
      "topics": [
        "FDboost_fac-class"
      ]
    },
    {
      "page": "FDboostLSS",
      "title": "Model-based Gradient Boosting for Functional GAMLSS",
      "topics": [
        "FDboostLSS"
      ]
    },
    {
      "page": "fitted.FDboost",
      "title": "Fitted values of a boosted functional regression model",
      "topics": [
        "fitted.FDboost"
      ]
    },
    {
      "page": "fuelSubset",
      "title": "Spectral data of fossil fuels",
      "topics": [
        "fuelSubset"
      ]
    },
    {
      "page": "funMRD",
      "title": "Functional MRD",
      "topics": [
        "funMRD"
      ]
    },
    {
      "page": "funMSE",
      "title": "Functional MSE",
      "topics": [
        "funMSE"
      ]
    },
    {
      "page": "funplot",
      "title": "Plot functional data with linear interpolation of missing values",
      "topics": [
        "funplot"
      ]
    },
    {
      "page": "funRsquared",
      "title": "Functional R-squared",
      "topics": [
        "funRsquared"
      ]
    },
    {
      "page": "getTime",
      "title": "Generic functions to asses attributes of functional data objects",
      "topics": [
        "getArgvals",
        "getArgvalsLab",
        "getId",
        "getIdLab",
        "getTime",
        "getTimeLab",
        "getX",
        "getXLab"
      ]
    },
    {
      "page": "getTime.hmatrix",
      "title": "Extract attributes of hmatrix",
      "topics": [
        "getArgvals.hmatrix",
        "getArgvalsLab.hmatrix",
        "getId.hmatrix",
        "getIdLab.hmatrix",
        "getTime.hmatrix",
        "getTimeLab.hmatrix",
        "getX.hmatrix",
        "getXLab.hmatrix"
      ]
    },
    {
      "page": "hmatrix",
      "title": "A S3 class for univariate functional data on a common grid",
      "topics": [
        "hmatrix"
      ]
    },
    {
      "page": "integrationWeights",
      "title": "Functions to compute integration weights",
      "topics": [
        "integrationWeights",
        "integrationWeightsLeft"
      ]
    },
    {
      "page": "is.hmatrix",
      "title": "Test to class of hmatrix",
      "topics": [
        "is.hmatrix"
      ]
    },
    {
      "page": "plot.validateFDboost",
      "title": "Methods for objects of class validateFDboost",
      "topics": [
        "mstop.validateFDboost",
        "plot.validateFDboost",
        "plotPredCoef",
        "print.validateFDboost"
      ]
    },
    {
      "page": "o_control",
      "title": "Function to control estimation of smooth offset",
      "topics": [
        "o_control"
      ]
    },
    {
      "page": "plot.bootstrapCI",
      "title": "Methods for objects of class bootstrapCI",
      "topics": [
        "plot.bootstrapCI",
        "print.bootstrapCI"
      ]
    },
    {
      "page": "plot.FDboost",
      "title": "Plot the fit or the coefficients of a boosted functional regression model",
      "topics": [
        "plot.FDboost",
        "plotPredicted",
        "plotResiduals"
      ]
    },
    {
      "page": "predict.FDboost",
      "title": "Prediction for boosted functional regression model",
      "topics": [
        "predict.FDboost"
      ]
    },
    {
      "page": "predict.FDboost_fac",
      "title": "Prediction and plotting for factorized FDboost model components",
      "topics": [
        "plot.FDboost_fac",
        "predict.FDboost_fac"
      ]
    },
    {
      "page": "residuals.FDboost",
      "title": "Residual values of a boosted functional regression model",
      "topics": [
        "residuals.FDboost"
      ]
    },
    {
      "page": "reweightData",
      "title": "Function to Reweight Data",
      "topics": [
        "reweightData"
      ]
    },
    {
      "page": "stabsel.FDboost",
      "title": "Stability Selection",
      "topics": [
        "stabsel.FDboost"
      ]
    },
    {
      "page": "subset_hmatrix",
      "title": "Subsets hmatrix according to an index",
      "topics": [
        "subset_hmatrix"
      ]
    },
    {
      "page": "summary.FDboost",
      "title": "Print and summary of a boosted functional regression model",
      "topics": [
        "print.FDboost",
        "summary.FDboost"
      ]
    },
    {
      "page": "truncateTime",
      "title": "Function to truncate time in functional data",
      "topics": [
        "truncateTime"
      ]
    },
    {
      "page": "update.FDboost",
      "title": "Function to update FDboost objects",
      "topics": [
        "update.FDboost"
      ]
    },
    {
      "page": "validateFDboost",
      "title": "Cross-Validation and Bootstrapping over Curves",
      "topics": [
        "validateFDboost"
      ]
    },
    {
      "page": "viscosity",
      "title": "Viscosity of resin over time",
      "topics": [
        "viscosity"
      ]
    },
    {
      "page": "wide2long",
      "title": "Transform id and time of wide format into long format",
      "topics": [
        "wide2long"
      ]
    }
  ],
  "_readme": "https://github.com/boost-r/fdboost/raw/HEAD/README.md",
  "_rundeps": [
    "Formula",
    "gamboostLSS",
    "gamlss.dist",
    "inum",
    "lattice",
    "libcoin",
    "MASS",
    "Matrix",
    "mboost",
    "mgcv",
    "mvtnorm",
    "nlme",
    "nnls",
    "partykit",
    "quadprog",
    "rpart",
    "stabs",
    "survival",
    "zoo"
  ],
  "_vignettes": [
    {
      "source": "density-on-scalar_birth.Rnw",
      "filename": "density-on-scalar_birth.pdf",
      "title": "FDboost density-on-scalar births",
      "engine": "knitr::knitr",
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