Title: Creates an empty package framework for the LCFD format
Description: This package creates an empty framework of files and
directories for the "Load, Clean, Func, Do" structure described

Title: Package for managing projects with multiple versions derived
Description: An R package to help manage R projects (e.g. Sweave
reports) where multiple versions are created based upon a
single source repository. For example, a monthly report where
each versions is identitcal with the exception of easily
definable parameters (e.g. date ranges for data extraction,
Collate: 'Build.R' 'Version.R' 'buildVersion.R' 'releaseVersion.R'

Title: Create Dummy Variables from Categorical Data
Description: Create dummy variables from categorical data.
This package can convert categorical data (factor and ordered) into
dummy variables and handle multiple columns simultaneously.
This package enables to select whether a dummy variable for base group
is included (for principal component analysis/factor analysis) or
excluded (for regression analysis) by an option.

'makedummies' function accepts 'data.frame', 'matrix', and
'tbl' (tibble) class (by 'tibble' package).
'matrix' class data is automatically converted to 'data.frame' class.

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