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  "Package": "KSPM",
  "Title": "Kernel Semi-Parametric Models",
  "Version": "0.2.1",
  "Authors@R": "c(person(\"Catherine\", \"Schramm\", email = \"cath.schramm@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Aurelie\", \"Labbe\", role = \"ctb\"), person(\"Celia M. T.\", \"Greenwood\", role = \"ctb\"))",
  "Description": "To fit the kernel semi-parametric model and its\nextensions. It allows multiple kernels and unlimited\ninteractions in the same model. Coefficients are estimated by\nmaximizing a penalized log-likelihood; penalization terms and\nhyperparameters are estimated by minimizing leave-one-out\nerror. It includes predictions with confidence/prediction\nintervals, statistical tests for the significance of each\nkernel, a procedure for variable selection and graphical tools\nfor diagnostics and interpretation of covariate effects.\nCurrently it is implemented for continuous dependent variables.\nThe package is based on the paper of Liu et al. (2007),\n<doi:10.1111/j.1541-0420.2007.00799.x>.",
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  "Author": "Catherine Schramm [aut, cre], Aurelie Labbe [ctb], Celia M. T.\nGreenwood [ctb]",
  "Maintainer": "Catherine Schramm <cath.schramm@gmail.com>",
  "Repository": "https://catherineschramm.r-universe.dev",
  "Date/Publication": "2020-08-10 12:32:11 UTC",
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    "check.integer",
    "coef.kspm",
    "comb",
    "computes.Kernel",
    "computes.Kernel.interaction",
    "computes.KernelALL",
    "confint.kspm",
    "cooks.distance.kspm",
    "derivatives",
    "deviance.kspm",
    "extractAIC.kspm",
    "fitted.kspm",
    "flexible.summary",
    "get.parameters",
    "hypercoef",
    "info.kspm",
    "Kernel",
    "kernel.equality",
    "kernel.gaussian",
    "kernel.inverse.quadratic",
    "kernel.linear",
    "kernel.list",
    "kernel.matrix",
    "kernel.polynomial",
    "kernel.sigmoid",
    "kspm",
    "kspmControl",
    "logLik.kspm",
    "lossFunction.looe",
    "nobs.kspm",
    "objects.Kernel",
    "predict.kspm",
    "print.kspm",
    "print.summary.kspm",
    "renames.Kernel",
    "residuals.kspm",
    "rstandard.kspm",
    "search.parameters",
    "sigma.kspm",
    "splitFormula",
    "stepKSPM",
    "summary.kspm",
    "test.1.kernel",
    "test.global.kernel",
    "test.k.kernel",
    "variable.names.kspm"
  ],
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      "name": "csm",
      "title": "Conventional and Social media features of 187 movies.",
      "object": "csm",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Year",
        "Ratings",
        "Genre",
        "Gross",
        "Budget",
        "Screens",
        "Sequel",
        "Sentiment",
        "Views",
        "Likes",
        "Dislikes",
        "Comments",
        "Aggregate.Followers"
      ],
      "rows": 187,
      "table": true,
      "tojson": true
    },
    {
      "name": "energy",
      "title": "Energy consumption measuring hourly during 22 days",
      "object": "energy",
      "class": [
        "data.frame"
      ],
      "fields": [
        "power",
        "date",
        "Temperature",
        "P",
        "HR",
        "hour",
        "hour.num"
      ],
      "rows": 504,
      "table": true,
      "tojson": true
    }
  ],
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    {
      "page": "case.names.kspm",
      "title": "Case names of fitted models",
      "topics": [
        "case.names.kspm"
      ]
    },
    {
      "page": "coef.kspm",
      "title": "Extract Model Coefficients",
      "topics": [
        "coef.kspm"
      ]
    },
    {
      "page": "confint.kspm",
      "title": "Confidence interavls for linear part of model parameters",
      "topics": [
        "confint.kspm"
      ]
    },
    {
      "page": "cooks.distance.kspm",
      "title": "Cook's distance for a Kernel Semi Parametric Model Fit",
      "topics": [
        "cooks.distance.kspm"
      ]
    },
    {
      "page": "csm",
      "title": "Conventional and Social media features of 187 movies.",
      "topics": [
        "csm"
      ]
    },
    {
      "page": "derivatives",
      "title": "Computing kernel function derivatives",
      "topics": [
        "derivatives"
      ]
    },
    {
      "page": "deviance.kspm",
      "title": "Model deviance",
      "topics": [
        "deviance.kspm"
      ]
    },
    {
      "page": "energy",
      "title": "Energy consumption measuring hourly during 22 days",
      "topics": [
        "energy"
      ]
    },
    {
      "page": "extractAIC.kspm",
      "title": "Extract AIC from a Kernel Semi Parametric Model",
      "topics": [
        "extractAIC.kspm"
      ]
    },
    {
      "page": "fitted.kspm",
      "title": "Extract Model Fitted values",
      "topics": [
        "fitted.kspm"
      ]
    },
    {
      "page": "flexible.summary",
      "title": "Summarizing Kernel Semi parametric Model Fits with flexible parameters for Davies' approximation method",
      "topics": [
        "flexible.summary"
      ]
    },
    {
      "page": "get.parameters",
      "title": "compute Kernel Semi Parametric model parameters",
      "topics": [
        "get.parameters"
      ]
    },
    {
      "page": "hypercoef",
      "title": "Extract Model Hyper-parameter",
      "topics": [
        "hypercoef"
      ]
    },
    {
      "page": "info.kspm",
      "title": "Giving information about Kernel Semi parametric Model Fits",
      "topics": [
        "info.kspm"
      ]
    },
    {
      "page": "Kernel",
      "title": "Create a Kernel Object",
      "topics": [
        "Kernel"
      ]
    },
    {
      "page": "kernel.function",
      "title": "Kernel Functions",
      "topics": [
        "kernel.equality",
        "kernel.function",
        "kernel.gaussian",
        "kernel.inverse.quadratic",
        "kernel.linear",
        "kernel.polynomial",
        "kernel.sigmoid"
      ]
    },
    {
      "page": "kernel.list",
      "title": "List of kernel parts included in the kernel semi parametric model",
      "topics": [
        "kernel.list"
      ]
    },
    {
      "page": "kernel.matrix",
      "title": "Kernel matrix",
      "topics": [
        "kernel.matrix"
      ]
    },
    {
      "page": "kernel.method",
      "title": "some internal methods in computation of kernel semi parametric model",
      "topics": [
        "asOneSidedFormula",
        "check.integer",
        "comb",
        "computes.Kernel",
        "computes.Kernel.interaction",
        "computes.KernelALL",
        "kernel.method",
        "objects.Kernel",
        "renames.Kernel",
        "splitFormula"
      ]
    },
    {
      "page": "kspm",
      "title": "Fitting Kernel Semi Parametric model",
      "topics": [
        "kspm"
      ]
    },
    {
      "page": "kspmControl",
      "title": "Control various aspects of the optimisation problem",
      "topics": [
        "kspmControl"
      ]
    },
    {
      "page": "logLik.kspm",
      "title": "Log Likelihood of a kspm Object",
      "topics": [
        "logLik.kspm"
      ]
    },
    {
      "page": "lossFunction.looe",
      "title": "Computation of the leave one out error (LOOE) in kernel semi parametric model",
      "topics": [
        "lossFunction.looe"
      ]
    },
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      "page": "nobs.kspm",
      "title": "Extract the number of observations from a Kernel Semi parametric Model Fit",
      "topics": [
        "nobs.kspm"
      ]
    },
    {
      "page": "plot.derivatives",
      "title": "Plot derivatives of a kspm object",
      "topics": [
        "plot.derivatives"
      ]
    },
    {
      "page": "plot.kspm",
      "title": "Plot Diagnostics for a kspm Object",
      "topics": [
        "plot.kspm"
      ]
    },
    {
      "page": "predict.kspm",
      "title": "Predicting Kernel Semi parametric Model Fits",
      "topics": [
        "predict.kspm"
      ]
    },
    {
      "page": "print.kspm",
      "title": "Print results from a Kernel Semi parametric Model Fit",
      "topics": [
        "print.kspm",
        "print.summary.kspm"
      ]
    },
    {
      "page": "residuals.kspm",
      "title": "Extract residuals from a Kernel Semi Parametric Model",
      "topics": [
        "residuals.kspm"
      ]
    },
    {
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      "title": "Standardized residuals for Kernel Semi parametric Model Fits",
      "topics": [
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      ]
    },
    {
      "page": "search.parameters",
      "title": "Optimisation to cumpute hyperparameter in Kernel Semi Parametric model",
      "topics": [
        "search.parameters"
      ]
    },
    {
      "page": "sigma.kspm",
      "title": "Extract residuals standard deviation",
      "topics": [
        "sigma.kspm"
      ]
    },
    {
      "page": "stepKSPM",
      "title": "Choose a model by AIC or BIC in a Stepwise Algorithm",
      "topics": [
        "stepKSPM"
      ]
    },
    {
      "page": "summary.kspm",
      "title": "Summarizing Kernel Semi parametric Model Fits",
      "topics": [
        "summary.kspm"
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    },
    {
      "page": "test.function",
      "title": "Score Tests for kernel part in kernel semi parametric model",
      "topics": [
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        "test.function",
        "test.global.kernel",
        "test.k.kernel"
      ]
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      "title": "Variable names of fitted models",
      "topics": [
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      ]
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    "Matrix"
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      "source": "vignetteKSPM.Rmd",
      "filename": "vignetteKSPM.html",
      "title": "KSPM: an R package for Kernel Semi-Prametric Models",
      "author": "Catherine Schramm, Aurelie Labbe, Celia Greenwood",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Key theoretical details",
        "The model",
        "Single kernel semi-parametric model",
        "Multiple kernel semi-parametric model",
        "Kernel interactions",
        "Kernel functions",
        "Estimation of parameters",
        "Tests of hypotheses in KSPM",
        "Interpretation of variable effects through derivatives",
        "Example: movie ratings dataset",
        "Data importation",
        "Predicting ratings using the set of conventional features",
        "Fitting the model",
        "Diagnostic plots",
        "Interpretation of coefficients through pointwise derivatives plots",
        "Comparing choices of kernel function",
        "Predicting ratings using both conventional and social media features",
        "Prediction for new data",
        "Prediction for current data",
        "An example of variables selection procedure",
        "Illustration 2: consumption of energy",
        "A first model",
        "Impact of the tuning parameter $\\rho$ on predictions and derivatives",
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      "modified": "2020-08-10 12:32:11",
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