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Most (but not all) of the libraries that comprise the ready4 framework are currently only available as development releases. For these libraries, you will need to use a tool like devtools to install the library directly from our GitHub organisation. If you do not have devtools on your machine you can install it with the following command.
utils::install.packages("devtools")If you plan to use ready4 for any purpose, you will need to install the ready4 foundation library.
As all software in the ready4 suite depends on the ready4 library, in most cases you do not need to install this library directly (it will come bundled with whatever other ready4 suite software you install).
If you can run the following command without producing an error message, then you already have it.
find.package("ready4")You can install the ready4 library from CRAN.
utils::install.packages("ready4")Alternatively, you can install the latest development release of ready4 directly from its GitHub repository.
utils::install.packages("devtools")
devtools::install_github("ready4-dev/ready4")Before you apply ready4 tools to your own project, you should make sure you can run some or all of the example code included in the package vignettes.
If you are a coder planning on using ready4 to author computational models, then you may wish to install the ready4class, ready4fun and ready4pack libraries.
However, please note that none of these libraries are yet available as a production release. You should therefore understand the limitations of using ready4 software development releases before you make the decision to install this software. Although we use these authoring tools intensively to help us write highly standardised model modules, we feel that these tools are only likely to be helpful to others once much more comprehensive documentation and training resources become available. Without this training and support, these packages are unlikely to appear to be very user-friendly. Furthermore, the initial burden of complying with house-style, file-naming and directory structure requirements of these packages is only likely to be worthwhile if you plan on developing multiple ready4 module libraries. If you still think these tools could be useful to you, consider contacting us first to discuss what additional information may be most helpful to you.
As ready4class and ready4fun are bundled as dependencies of ready4pack, you can install all three from our GitHub organisation using one command.
devtools::install_github("ready4-dev/ready4pack")To use these computational model authoring tools, you will need to have set-up and appropriately configured your own accounts in:
The machine onto which you install ready4pack will also need to be securely storing your GitHub credentials (i.e. the value for the GITHUB_PAT token).
It should be noted that the development workflow supported by our computational model authoring tools is not yet well documented. We don’t recommend undertaking R package development with these tools until this has been rectified. However, if you still want to try these tools out, the best place to start is review the examples in the ready4class, ready4fun and ready4pack vignettes.
If you are a coder or modeller planning to create, share and access model datasets with ready4, then you will need the ready4use library.
However, please note that ready4use is not yet available as a production release. You should therefore understand the limitations of using ready4 software development releases before you make the decision to install this software.
You may already have ready4use installed on your machine (e.g. if you have previously installed other ready4 framework and module libraries that include ready4use as a dependency). If you can run the following command without producing an error message, then you already have it.
find.package("ready4use")You can install ready4use directly from its GitHub repository.
devtools::install_github("ready4-dev/ready4use")If one of your intended uses of ready4use is to share outputs in online datasets, you will need to have set up an account on a Dataverse installation (we recommend using the Harvard Dataverse). Some of the key terms and concepts relating to using a Dataverse installation in conjunction with ready4use are described in this tutorial.
You need to ensure that you have write permissions to any Dataverse Datasets that you plan to use to post files to. Furthermore, the machine on which you install ready4use should also securely store your Dataverse account credentials (specifically, values for the DATAVERSE_KEY and DATAVERSE_SERVER tokens). Details of how to do this are described in documentation for the dataverse R package, an important third party dependency package for ready4use.
You should now be able to run the example code included in the package vignettes. To run all of this code you will need to replace the details of the Dataverse Dataset to which files are being written to those of your own Dataverse Dataset.
If you are a coder or modeller planning to implement a reproducible analysis with ready4, you will need to install the ready4show library.
However, please note that ready4show is not yet available as a production release. You should therefore understand the limitations of using ready4 software development releases before you make the decision to install this software.
If you have installed other ready4 libraries, then ready4show may have already been installed as a dependency. If you can run the following command without producing an error message, then you already have it.
find.package("ready4show")The ready4show library can be installed directly from its GitHub repository.
devtools::install_github("ready4-dev/ready4show")Before you apply ready4show tools to your own project, you should make sure you can run some or all of the example code included in the package vignettes.
If you plan on using existing ready4 modules for a modelling project, you can review currently available module libraries, to identify which libraries are relevant to your project.
However, please note that no ready4 module library is yet available as a [production release](/docs/software/status/production-releases/. You should therefore understand the limitations of using ready4 software development releases before you make the decision to install this software.
The command to install each ready4 module takes the following format.
devtools::install_github("ready4-dev/PACKAGE_NAME")For example, if you are planning to predict health utility using some of the mapping algorithms that we have previously developed, you can install the youthu library with the following command.
devtools::install_github("ready4-dev/youthu")A small number of ready4 modules require that you configure some of the dependencies installed with them before they can be used. In particular:
if you are using modules from the TTU package to undertake a utility mapping study, you will need to have both installed and configured the cmdstanr R package as per the instructions on that package’s documentation website; and
if you are using the mychoice package to undertake a discrete choice experiment study and are using a Mac, you need to ensure that you have a Fortran compiler installed. Some relevant advice on this: https://mac.r-project.org/tools/ .
Before you apply ready4 modules to your own project, you should make sure you can run some or all of the example code included in relevant library vignette articles. The package website URL takes the form of https://ready4-dev.github.io/PACKAGE_NAME/articles/ (e.g. the vignettes for the youthvars package are available at https://ready4-dev.github.io/youthvars/articles/).