{ "_id": "6ae326243d0f299392dd0ff5", "Package": "lgpr", "Title": "Longitudinal Gaussian Process Regression", "Version": "1.2.5", "Authors@R": "c(person(given = \"Juho\",\nfamily = \"Timonen\",\nrole = c(\"aut\", \"cre\"),\nemail = \"juho.timonen@iki.fi\",\ncomment = c(ORCID = \"0000-0003-2341-6765\")\n),\nperson(given = \"Andrew\",\nfamily = \"Johnson\",\nrole = c(\"ctb\")\n)\n)", "Description": "Interpretable nonparametric modeling of longitudinal data\nusing additive Gaussian process regression. Contains\nfunctionality for inferring covariate effects and assessing\ncovariate relevances. Models are specified using a convenient\nformula syntax, and can include shared, group-specific,\nnon-stationary, heterogeneous and temporally uncertain effects.\nBayesian inference for model parameters is performed using\n'Stan'. The modeling approach and methods are described in\ndetail in Timonen et al. (2021)\n.", "License": "GPL (>= 3)", "Encoding": "UTF-8", "LazyData": "true", "Biarch": "true", "SystemRequirements": "GNU make", "NeedsCompilation": "yes", "RoxygenNote": "7.3.2", "URL": "https://github.com/jtimonen/lgpr", "BugReports": "https://github.com/jtimonen/lgpr/issues", "VignetteBuilder": "knitr", "Packaged": { "Date": "2026-10-29 10:42:03 UTC", "User": "root" }, "Author": "Juho Timonen [aut, cre] (ORCID:\n), Andrew Johnson [ctb]", "Maintainer": "Juho Timonen ", "Config/pak/sysreqs": "make", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-10-31 02:15:14 UTC", "RemoteUrl": "https://github.com/cran/lgpr", "RemoteRef": "HEAD", "RemoteSha": "00c84cd224f88e704aa143098497668f5e8e137c", "_user": "cran", "_type": "src", "_file": "lgpr_1.2.5.tar.gz", "_fileid": "https://r2.ropensci.org/3c07d904b820ac23a9481db7012ad6cc006ad0110bbd19cd44d14d88dacc2fd5", "_filesize": 488717, "_sha256": "3c07d904b820ac23a9481db7012ad6cc006ad0110bbd19cd44d14d88dacc2fd5", "_expires": 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useful for debugging", "topics": [ "example_fit" ] }, { "page": "fit_summary", "title": "Print a fit summary.", "topics": [ "fit_summary" ] }, { "page": "GaussianPrediction-class", "title": "An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model", "topics": [ "component_names,GaussianPrediction-method", "GaussianPrediction", "GaussianPrediction-class", "num_components,GaussianPrediction-method", "num_evalpoints,GaussianPrediction-method", "num_paramsets,GaussianPrediction-method", "show,GaussianPrediction-method" ] }, { "page": "get_draws", "title": "Extract parameter draws from lgpfit or stanfit", "concept": [ "main functions" ], "topics": [ "get_draws" ] }, { "page": "get_pred", "title": "Extract model predictions and function posteriors", "topics": [ "get_pred" ] }, { "page": "kernel", "title": "Compute a kernel matrix (covariance matrix)", "topics": [ "kernel", "kernel_beta", "kernel_bin", "kernel_cat", 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"plot_f", "plot_pred" ] }, { "page": "plot_sim", "title": "Visualize an lgpsim object (simulated data)", "topics": [ "plot_sim" ] }, { "page": "ppc", "title": "Graphical posterior predictive checks", "topics": [ "ppc" ] }, { "page": "pred", "title": "Posterior predictions and function posteriors", "concept": [ "main functions" ], "topics": [ "pred" ] }, { "page": "Prediction-class", "title": "An S4 class to represent prior or posterior draws from an additive function distribution.", "topics": [ "component_names,Prediction-method", "num_components,Prediction-method", "num_evalpoints,Prediction-method", "num_paramsets,Prediction-method", "Prediction", "Prediction-class", "show,Prediction-method" ] }, { "page": "prior_pred", "title": "Prior (predictive) sampling", "concept": [ "main functions" ], "topics": [ "prior_pred", "sample_param_prior" ] }, { "page": "prior_to_num", "title": "Convert given prior to numeric format", "topics": [ "prior_to_num" ] }, { "page": "priors", "title": "Prior 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