Package: makemyprior 1.2.2
makemyprior: Intuitive Construction of Joint Priors for Variance Parameters
Tool for easy prior construction and visualization. It helps to formulates joint prior distributions for variance parameters in latent Gaussian models. The resulting prior is robust and can be created in an intuitive way. A graphical user interface (GUI) can be used to choose the joint prior, where the user can click through the model and select priors. An extensive guide is available in the GUI. The package allows for direct inference with the specified model and prior. Using a hierarchical variance decomposition, we formulate a joint variance prior that takes the whole model structure into account. In this way, existing knowledge can intuitively be incorporated at the level it applies to. Alternatively, one can use independent variance priors for each model components in the latent Gaussian model. Details can be found in the accompanying scientific paper: Hem, Fuglstad, Riebler (2024, Journal of Statistical Software, <doi:10.18637/jss.v110.i03>).
Authors:
makemyprior_1.2.2.tar.gz
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makemyprior.pdf |makemyprior.html✨
makemyprior/json (API)
NEWS
# Install 'makemyprior' in R: |
install.packages('makemyprior', repos = c('https://ingebogh.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ingebogh/makemyprior/issues
- latin_data - Latin square experiment data
- neonatal_data - Neonatal mortality data
- wheat_data - Genomic wheat breeding model data
Last updated 3 months agofrom:ab64744f1e. Checks:OK: 6 NOTE: 1. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 21 2024 |
R-4.5-win | OK | Nov 21 2024 |
R-4.5-linux | OK | Nov 21 2024 |
R-4.4-win | OK | Nov 21 2024 |
R-4.4-mac | OK | Nov 21 2024 |
R-4.3-win | NOTE | Nov 21 2024 |
R-4.3-mac | OK | Nov 21 2024 |
Exports:compile_stancreate_stan_fileeval_joint_prioreval_pc_priorexpitextract_posterior_effectextract_posterior_parameterfind_pc_prior_paramget_parameter_orderinference_inlainference_stanlogitmake_eval_prior_datamake_priormakemyprior_example_modelmakemyprior_guimakemyprior_modelsmakemyprior_plottingmcplot_marginal_priorplot_posterior_fixedplot_posterior_precisionplot_posterior_stanplot_posterior_stdevplot_posterior_varianceplot_priorplot_several_posterior_stanplot_tree_structurescale_precmattypical_variance
Dependencies:base64encbslibcachemclicolorspacecommonmarkcrayondigestevaluatefansifarverfastmapfontawesomefsggplot2gluegtablehighrhtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigpromisesR6rappdirsRColorBrewerRcpprlangrmarkdownsassscalesshinyshinyBSshinyjssourcetoolstibbletinytexutf8vctrsviridisLitevisNetworkwithrxfunxtableyaml
Latin square experiment
Rendered fromlatin_square.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-02-20
Started: 2021-05-18
Example i.i.d. model
Rendered frommake_prior.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-02-20
Started: 2021-05-18
Neonatal mortality
Rendered fromneonatal_mortality.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-02-20
Started: 2021-05-18
Wheat breeding
Rendered fromwheat_breeding.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2023-11-07
Started: 2021-05-18