{ "_id": "6a26722b794753ddbc3eadfb", "Package": "funGp", "Type": "Package", "Title": "Gaussian Process Models for Scalar and Functional Inputs", "Version": "1.0.0", "Authors@R": "c(\nperson(\"Jose\", \"Betancourt\", email = \"fungp.rpack@gmail.com\", role = c(\"cre\", \"aut\")),\nperson(\"François\", \"Bachoc\", email = \"francois.bachoc@math.univ-toulouse.fr\", role = \"aut\"),\nperson(\"Thierry\", \"Klein\", email = \"thierry.klein@math.univ-toulouse.fr\", role = \"aut\"),\nperson(\"Jeremy\", \"Rohmer\", email = \"J.Rohmer@brgm.fr\", role = \"aut\"),\nperson(\"Yves\", \"Deville\", email = \"deville.yves@alpestat.com\", role = \"ctb\"),\nperson(\"Deborah\", \"Idier\", email = \"D.Idier@brgm.fr\", role = \"ctb\"))", "Maintainer": "Jose Betancourt ", "Description": "Construction and smart selection of Gaussian process\nmodels for analysis of computer experiments with emphasis on\ntreatment of functional inputs that are regularly sampled. This\npackage offers: (i) flexible modeling of functional-input\nregression problems through the fairly general Gaussian process\nmodel; (ii) built-in dimension reduction for functional inputs;\n(iii) heuristic optimization of the structural parameters of\nthe model (e.g., active inputs, kernel function, type of\ndistance). An in-depth tutorial in the use of funGp is provided\nin Betancourt et al. (2024) and\nMetamodeling background is provided in Betancourt et al. (2020)\n. The algorithm for structural\nparameter optimization is described in\n.", "Note": "research product of the RISCOPE project (ANR, project\nNo.16CE04-0011) .", "License": "GPL-3", "URL": "https://djbetancourt-gh.github.io/funGp/,\nhttps://github.com/djbetancourt-gh/funGp", "BugReports": "https://github.com/djbetancourt-gh/funGp/issues", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.1", "Collate": "'0_funGp_Doc.R' '0_show_Doc.R' '0_summary_Doc.R'\n'8_outilsCode.R' '3_ant_admin.R' '2_fgpKern_Class.R'\n'2_fgpProj_Class.R' '1_fgpm_Class.R' '1_Xfgpm_Class.R'\n'3_ant_search.R' '3_training_F.R' '3_training_S.R'\n'3_training_SF.R' '4_prediction_F.R' '4_prediction_S.R'\n'4_prediction_SF.R' '5_simulation_F.R' '5_simulation_S.R'\n'5_simulation_SF.R' '6_updating.R' '7_blackBoxFunctions.R'\n'7_checkingFunctions.R' '7_correlFunctions.R'\n'7_dimRedFunctions.R' '7_distanceFunctions.R'\n'7_plottingFunctions.R' '7_plottingFunctionsStandard.R'\n'8_outilsStats.R' 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