CRAN status Test Coverage

ADaM in R Asset Library


To provide an open source, modularized toolbox that enables the pharmaceutical programming community to develop ADaM datasets in R.


The package is available from CRAN and can be installed by running install.packages("admiral").

To install the latest development version of the package directly from GitHub use the following code:

if (!requireNamespace("remotes", quietly = TRUE)) {

remotes::install_github("pharmaverse/pharmaversesdtm") # This is a required dependency of {admiral}
remotes::install_github("pharmaverse/admiraldev") # This is a required dependency of {admiral}

Release Schedule

{admiral}` releases are targeted for the first Monday of the last month of each quarter. Pull Requests will be frozen the week before a release. The {admiral} family has several downstream and upstream dependencies and so this release shall be done in three Phases:

Release Schedule Phase 1- Date and Packages Phase 2- Date and Packages
Q4-2023 December 4th December 11th
{pharmaversesdtm} {admiralonco}
{admiraldev} {admiralophtha}

The {admiral} Q4-2023 release will officially be {admiral}’s version 1.0.0 release, where we commit to increased package maturity and pivot towards focusing on maintenance rather than new content. This does not mean that there will never be any new content in {admiral}, rather it means we will be more mindful about introducing new functionality and/or breaking changes. The release schedule in 2024 and onward will also shift to twice-yearly, rather than quarterly, so that our users have ample time to react to any new content and changes that do make it onto {admiral}.

Main Goal

Provide users with an open source, modularized toolbox with which to create ADaM datasets in R. As opposed to a “run 1 line and an ADaM appears” black-box solution or an attempt to automate ADaM.

One of the key aspects of {admiral} is its development by the users for the users. It gives an entry point for all to collaborate, co-create and contribute to a harmonized approach of developing ADaMs in R across the pharmaceutical industry.


To set expectations: It is not our target that {admiral} will ever provide all possible solutions for all ADaM datasets outside of study specific needs. It depends on the user’s collaboration and contribution to help grow over time to an asset library that is robust, easy to use and has an across-industry focus. We do not see a coverage of 100% of all ADaM derivations as ever achievable—ADaM is endless.

We will provide:

Types of Packages

There will be 3 foreseeable types of {admiral} packages:

Admiral Manifesto

For {admiral} and all extension packages, we prioritize providing our users with a simple to adopt toolkit that enables them to produce readable and easily constructible ADaM programs. The following explains our philosophy, which we try to adhere to across the {admiral} family of packages. There isn’t always a clear single, straightforward rule, but there are guiding principles we adhere to for {admiral}. This manifesto helps show the considerations of our developers when making decisions.

We have four design principles to achieve the main goal:


All {admiral} functions should be easy to use.


All {admiral} functions have a clear purpose.


All {admiral} functions are easily findable.


All {admiral} functions follow the Programming Strategy that all our developers and contributors must follow, so that all our code has a high degree of consistency and readability.

References and Documentation

Pharmaverse Blog

If you are interested in R and Clinical Reporting, then visit the pharmaverse blog. This contains regular, bite-sized posts showcasing how {admiral} and other packages in the pharmaverse can be used to realize the vision of full end-to-end Clinical Reporting in R.

We are also always looking for keen {admiral} users to publish their own blog posts about how they use the package. If this could be you, feel free make an issue in the GitHub repo and get started!

Conference Presentations


We use the following for support and communications between user and developer community:


Along with the authors and contributors, thanks to the following people for their work on the package: Jaxon Abercrombie, Mahdi About, Teckla Akinyi, James Black, Claudia Carlucci, Bill Denney, Kamila Duniec, Alice Ehmann, Ania Golab, Alana Harris, Declan Hodges, Anthony Howard, Shimeng Huang, Samia Kabi, James Kim, John Kirkpatrick, Leena Khatri, Robin Koeger, Konstantina Koukourikou, Pavan Kumar, Pooja Kumari, Shan Lee, Wenyi Liu, Jack McGavigan, Jordanna Morrish, Syed Mubasheer, Yohann Omnes, Barbara O’Reilly, Hamza Rahal, Nick Ramirez, Tom Ratford, Tamara Senior, Sophie Shapcott, Ondrej Slama, Andrew Smith, Daniil Stefonishin, Vignesh Thanikachalam, Michael Thorpe, Annie Yang, Ojesh Upadhyay and Franciszek Walkowiak.