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  "Title": "Geometrically Designed Spline Regression",
  "Version": "0.3.6",
  "Date": "2026-09-16",
  "Authors@R": "c(\nperson(c(\"Dimitrina\", \"S.\"), \"Dimitrova\", , \"D.Dimitrova@citystgeorges.ac.uk\", role = \"aut\"),\nperson(c(\"Vladimir\", \"K.\"), \"Kaishev\", , \"Vladimir.Kaishev.1@citystgeorges.ac.uk\", role = \"aut\"),\nperson(\"Andrea\", \"Lattuada\", , \"Andrea.Lattuada@hotmail.com\", role = \"aut\"),\nperson(c(\"Emilio\", \"L.\"), \"Sáenz Guillén\", , \"emilioluissaenzguillen@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(c(\"Richard\", \"J.\"), \"Verrall\", , \"R.J.Verrall@citystgeorges.ac.uk\", role = \"aut\")\n)",
  "Maintainer": "Emilio L. Sáenz Guillén <emilioluissaenzguillen@gmail.com>",
  "Description": "Spline regression, generalized additive models and\ncomponent-wise gradient boosting utilizing geometrically\ndesigned (GeD) splines. GeDS regression is a non-parametric\nmethod inspired by geometric principles, for fitting spline\nregression models with variable knots. Joint tensor-product\nspline components may involve more than two independent\nvariables in Normal, generalized, additive, and boosting\nmodels. It efficiently estimates the number of knots and their\npositions, as well as the spline order, assuming the response\nvariable follows a distribution from the exponential family.\nGeDS models integrate the broader category of generalized\n(non-)linear models, offering a flexible approach to model\ncomplex relationships. A description of the method can be found\nin Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and\nDimitrova et al. (2023) <doi:10.1016/j.amc.2022.127493>.\nFurther extending its capabilities, GeDS's implementation\nincludes generalized additive models (GAM) and functional\ngradient boosting (FGB), enabling versatile multivariate\npredictor modeling, as discussed in the forthcoming work of\nDimitrova et al. (2026).",
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  "Date/Publication": "2026-09-27 14:19:07 UTC",
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  "Author": "Dimitrina S. Dimitrova [aut],\nVladimir K. Kaishev [aut],\nAndrea Lattuada [aut],\nEmilio L. Sáenz Guillén [aut, cre],\nRichard J. Verrall [aut]",
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    "shapeConstrain",
    "SplineReg_GLM",
    "SplineReg_LM",
    "UnivariateFitter",
    "visualize_boosting"
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    {
      "name": "BaFe2As2",
      "title": "Barium-Ferrum-Arsenide Powder Diffraction Data",
      "object": "BaFe2As2",
      "class": [
        "data.frame"
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        "intensity"
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      "tojson": true
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      "table": true,
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        "F_Q"
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        "Deaths",
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  "_help": [
    {
      "page": "GeDS-package",
      "title": "Geometrically Designed Spline Regression",
      "topics": [
        "GeDS-package",
        "GeDS"
      ]
    },
    {
      "page": "BaFe2As2",
      "title": "Barium-Ferrum-Arsenide Powder Diffraction Data",
      "topics": [
        "BaFe2As2"
      ]
    },
    {
      "page": "BivariateFitters",
      "title": "Fitter Function for GeD Spline Regression for Bivariate Data",
      "topics": [
        "BivariateFitter",
        "BivariateFitters",
        "GenBivariateFitter"
      ]
    },
    {
      "page": "bl_imp",
      "title": "Base Learner Importance for GeDSboost Objects",
      "topics": [
        "bl_imp",
        "bl_imp.GeDSboost"
      ]
    },
    {
      "page": "coalMining",
      "title": "Coal Mining Disasters Data",
      "topics": [
        "coalMining"
      ]
    },
    {
      "page": "coef",
      "title": "Coef Method for GeDS and GeDSfitND Objects",
      "topics": [
        "coef.GeDS",
        "coef.GeDSfitND"
      ]
    },
    {
      "page": "coef.GeDSgam_GeDSboost",
      "title": "Coef Method for GeDSgam, GeDSboost",
      "topics": [
        "coef.GeDSboost",
        "coef.GeDSgam",
        "coef.GeDSgam,",
        "coef.GeDSgam,boost"
      ]
    },
    {
      "page": "confint.GeDS",
      "title": "Confidence Intervals for GeDS Models Coefficients",
      "topics": [
        "confint.GeDS",
        "confint.GeDSboost",
        "confint.GeDSgam"
      ]
    },
    {
      "page": "crossv_GeDS",
      "title": "K-Fold Cross-Validation",
      "topics": [
        "crossv_GeDS"
      ]
    },
    {
      "page": "CrystalData",
      "title": "Crystallographic Scattering Data",
      "topics": [
        "CrystalData",
        "CrystalData10k",
        "CrystalData300k"
      ]
    },
    {
      "page": "Derive",
      "title": "Derivative of GeDS Objects",
      "topics": [
        "Derive"
      ]
    },
    {
      "page": "deviance.GeDS",
      "title": "Deviance Method for GeDS, GeDSfitND, GeDSgam, GeDSboost",
      "topics": [
        "deviance.GeDS",
        "deviance.GeDSboost",
        "deviance.GeDSfitND",
        "deviance.GeDSgam"
      ]
    },
    {
      "page": "EWmortality",
      "title": "Death Counts in England and Wales",
      "topics": [
        "EWmortality"
      ]
    },
    {
      "page": "f",
      "title": "Defining the Covariates for the Spline Component in a GeDS Formula",
      "topics": [
        "f"
      ]
    },
    {
      "page": "family.GeDS",
      "title": "Extract Family from a GeDS, GeDSgam, GeDSboost Object",
      "topics": [
        "family.GeDS",
        "family.GeDSboost",
        "family.GeDSgam"
      ]
    },
    {
      "page": "formula.GeDS",
      "title": "Formula for the Predictor Model",
      "topics": [
        "formula.GeDS",
        "formula.GeDSboost",
        "formula.GeDSfitND",
        "formula.GeDSgam"
      ]
    },
    {
      "page": "GGeDS",
      "title": "Generalized Geometrically Designed Spline Regression Estimation",
      "topics": [
        "GGeDS"
      ]
    },
    {
      "page": "Integrate",
      "title": "Defined Integral of GeDS Objects",
      "topics": [
        "Integrate"
      ]
    },
    {
      "page": "IRLSfit",
      "title": "IRLS Estimation",
      "topics": [
        "IRLSfit"
      ]
    },
    {
      "page": "knots",
      "title": "Knots Method for GeDS, GeDSfitND, GeDSgam, GeDSboost",
      "topics": [
        "knots.GeDS",
        "knots.GeDSboost",
        "knots.GeDSfitND",
        "knots.GeDSgam"
      ]
    },
    {
      "page": "lines.GeDS",
      "title": "Lines Method for GeDS Objects",
      "topics": [
        "lines.GeDS"
      ]
    },
    {
      "page": "logLik.GeDS",
      "title": "Extract Log-Likelihood from a GeDS Object",
      "topics": [
        "logLik.GeDS",
        "logLik.GeDSboost",
        "logLik.GeDSgam"
      ]
    },
    {
      "page": "n.boost.iter",
      "title": "Extract Number of Boosting Iterations from a GeDSboost Object",
      "topics": [
        "N.boost.iter",
        "N.boost.iter.GeDSboost"
      ]
    },
    {
      "page": "NGeDS",
      "title": "Geometrically Designed Spline Regression Estimation",
      "topics": [
        "NGeDS"
      ]
    },
    {
      "page": "NGeDSboost",
      "title": "Component-Wise Gradient Boosting with NGeDS Base-Learners",
      "topics": [
        "NGeDSboost"
      ]
    },
    {
      "page": "NGeDSgam",
      "title": "NGeDSgam: Local Scoring Algorithm with GeD Splines in Backfitting",
      "topics": [
        "NGeDSgam"
      ]
    },
    {
      "page": "plot.GeDS",
      "title": "Plot Method for GeDS Objects",
      "topics": [
        "plot.GeDS"
      ]
    },
    {
      "page": "plot.GeDSboost",
      "title": "Plot Method for GeDSboost Objects",
      "topics": [
        "plot.GeDSboost"
      ]
    },
    {
      "page": "plot.GeDSgam",
      "title": "Plot Method for GeDSgam Objects",
      "topics": [
        "plot.GeDSgam"
      ]
    },
    {
      "page": "PPolyRep",
      "title": "Piecewise Polynomial Spline Representation",
      "topics": [
        "PPolyRep"
      ]
    },
    {
      "page": "predict.GeDS",
      "title": "Predict Method for GeDS Objects",
      "topics": [
        "predict.GeDS"
      ]
    },
    {
      "page": "predict.GeDSfitND",
      "title": "Predict from an experimental multivariate tensor GeDS fit",
      "topics": [
        "predict.GeDSfitND"
      ]
    },
    {
      "page": "predict.GeDSgam_GeDSboost",
      "title": "Predict Method for GeDSgam, GeDSboost",
      "topics": [
        "predict.GeDSboost",
        "predict.GeDSboost,",
        "predict.GeDSgam",
        "predict.GeDSgam,boost"
      ]
    },
    {
      "page": "print.GeDS",
      "title": "Print Method for GeDS, GeDSgam, GeDSboost",
      "topics": [
        "print.GeDS",
        "print.GeDSboost",
        "print.GeDSgam"
      ]
    },
    {
      "page": "print.GeDSfitND",
      "title": "Print an Experimental Multivariate GeDS Fit",
      "topics": [
        "print.GeDSfitND"
      ]
    },
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