Intuitive Construction of Joint Priors for Variance Parameters
c(
person("Ingeborg Gullikstad", "Hem", email = "ingeborg.hem@ntnu.no", role = c("cre", "aut")),
person("Geir-Arne", "Fuglstad", role = "aut"),
person("Andrea", "Riebler", role = "aut") )
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.
ggplot2, Matrix, knitr, shiny, shinyjs, shinyBS, visNetwork,
rmarkdown, rlang, MASS, testthat (>= 2.1.0)
Ingeborg Gullikstad Hem [cre, aut],
Geir-Arne Fuglstad [aut],
Andrea Riebler [aut]

ggplot2, Matrix, methods, shiny, shinyjs, shinyBS, visNetwork,
rlang, MASS

person(given = "Kinto",
family = "Behr",
role = c("aut", "cre", "cph"),
email = "kinto.behr@gmail.com")
A suite of tools for transforming an existing workflow into a
self-documenting pipeline with very minimal upfront costs. Segments of
the pipeline are specified in much the same way a 'Make' rule is, by
declaring an executable recipe (which might be an R script), along
with the corresponding targets and dependencies. When the entire
pipeline is run through, only those recipes that need to be executed
will be. Meanwhile, execution metadata is captured behind the scenes
for later inspection.

knitr, covr, testthat (>= 3.0.0), withr, rmarkdown

knitr, covr, testthat (>= 3.0.0), withr, rmarkdown, webshot,
visNetwork