{ "_id": "6a156982acfb0bcc41d62b18", "Package": "makemyprior", "Title": "Intuitive Construction of Joint Priors for Variance Parameters", "Version": "1.2.2", "Authors@R": "c(\nperson(\"Ingeborg\", \"Hem\", email = \"ingeborg.hem@ntnu.no\", role = c(\"cre\", \"aut\"), comment = c(ORCID = \"0009-0008-3229-0184\")),\nperson(\"Geir-Arne\", \"Fuglstad\", role = \"aut\", comment = c(ORCID = \"0000-0003-4995-2152\")),\nperson(\"Andrea\", \"Riebler\", role = \"aut\", comment = c(ORCID = \"0009-0004-3998-4032\")))", "Description": "Tool for easy prior construction and visualization. It\nhelps to formulates joint prior distributions for variance\nparameters in latent Gaussian models. The resulting prior is\nrobust and can be created in an intuitive way. A graphical user\ninterface (GUI) can be used to choose the joint prior, where\nthe user can click through the model and select priors. An\nextensive guide is available in the GUI. The package allows for\ndirect inference with the specified model and prior. Using a\nhierarchical variance decomposition, we formulate a joint\nvariance prior that takes the whole model structure into\naccount. In this way, existing knowledge can intuitively be\nincorporated at the level it applies to. Alternatively, one can\nuse independent variance priors for each model components in\nthe latent Gaussian model. Details can be found in the\naccompanying scientific paper: Hem, Fuglstad, Riebler (2024,\nJournal of Statistical Software, ).", "License": "GPL (>= 2)", "URL": "https://github.com/ingebogh/makemyprior", "BugReports": "https://github.com/ingebogh/makemyprior/issues", "Encoding": "UTF-8", "LazyData": "true", "Additional_repositories": "https://inla.r-inla-download.org/R/stable/", "VignetteBuilder": "knitr", "RoxygenNote": "7.2.3", "Config/pak/sysreqs": "cmake make libuv1-dev zlib1g-dev", "Repository": "https://ingebogh.r-universe.dev", "Date/Publication": "2024-08-23 10:34:29 UTC", "RemoteUrl": "https://github.com/ingebogh/makemyprior", "RemoteRef": "HEAD", "RemoteSha": "ab64744f1ec514783d40bc37f2d98c78081c844b", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-26 09:30:55 UTC", "User": "root" }, "Author": "Ingeborg Hem [cre, aut] (ORCID:\n),\nGeir-Arne Fuglstad [aut] (ORCID:\n),\nAndrea Riebler [aut] (ORCID: )", "Maintainer": "Ingeborg Hem ", "MD5sum": 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"title": "Compile stan-model", "topics": [ "compile_stan" ] }, { "page": "create_stan_file", "title": "Create a \"skeleton\" for custom Stan code", "topics": [ "create_stan_file" ] }, { "page": "eval_joint_prior", "title": "Evaluate the joint variance prior", "topics": [ "eval_joint_prior", "make_eval_prior_data" ] }, { "page": "eval_pc_prior", "title": "Evaluate PC prior for variance proportion", "topics": [ "eval_pc_prior" ] }, { "page": "expit", "title": "expit", "topics": [ "expit" ] }, { "page": "extract_posterior_effect", "title": "Extract the posterior of a random effect", "topics": [ "extract_posterior_effect" ] }, { "page": "extract_posterior_parameter", "title": "Extract the posterior parameter estimate", "topics": [ "extract_posterior_parameter" ] }, { "page": "find_pc_prior_param", "title": "Find suitable PC prior parameters", "topics": [ "find_pc_prior_param" ] }, { "page": "get_parameter_order", "title": "Internal variance parameter order", "topics": [ 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"topics": [ "mc" ] }, { "page": "neonatal_data", "title": "Neonatal mortality data", "topics": [ "neonatal_data" ] }, { "page": "plot_marginal_prior", "title": "Plotting prior for a single parameter (weight or variance (not standard deviation))", "topics": [ "plot_marginal_prior" ] }, { "page": "plot_posterior_fixed", "title": "Plotting posterior distributions", "topics": [ "plot_posterior_fixed" ] }, { "page": "plot_posterior_stan", "title": "Plotting posterior distributions", "topics": [ "plot_posterior_stan" ] }, { "page": "plot_posterior_variance", "title": "Plotting posterior variances, standard deviations or precisions", "topics": [ "plot_posterior_precision", "plot_posterior_stdev", "plot_posterior_variance" ] }, { "page": "plot_prior", "title": "Plotting prior distributions", "topics": [ "plot_prior" ] }, { "page": "plot_several_posterior_stan", "title": "Plotting several posterior distributions", "topics": [ "plot_several_posterior_stan" ] }, { "page": "plot_tree_structure", 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