Analytics Engineering — Open-Source Solutions

To truly drive change and push the clinical research industry forward, we all need to come to the table. That’s why open-source contributions and solutions are a large part of our mission at Atorus. In support of these aims, Atorus has developed and contributed to multiple open-source analytics packages intended to broaden the reach of R statistical software users in the clinical field.

admiral

A Modular ADaM Programming Framework

admiral is a flexible, modular toolkit for building ADaM datasets in R. Its clear structure and comprehensive documentation support scalable, submission-ready programming.

Read more about admiral on GitHub.

Part of the pharmaverse.

admiralpeds

Pediatric Extensions to the admiral Framework

admiralpeds extends admiral with pre-built functions and examples for pediatric trials. It simplifies dataset creation for younger populations.

Read more on GitHub and find it on CRAN.

Part of the pharmaverse.

cardx

Create Tables From ARD Data in Cards

cardx streamlines the creation of dynamic tables and plots from ARD (Analysis Results Data) within the cards framework, supporting reproducible reporting.

Read more about cardx on GitHub.

Part of the InsightsEngineering ecosystem.

clindata

Standardized Functions for Clinical Data Processing

clindata provides reusable functions for transforming, summarizing, and visualizing clinical trial data. The package promotes consistency and code sharing across teams and studies.

Read more about clindata on GitHub.

Co-developed with Gilead.

clinify

Regulatory-Ready Tables, Made Simple

The clinify package helps you create polished, professional clinical tables for submission and reporting. Built to extend the functionality of powerful existing tools like flextable and officer, clinify simplifies complicated formatting demands. Straightforward functions for adding titles, footnotes, and consistent organizational styling help clinical teams deliver tables that are visually aligned, well-structured, and compliant with industry standards. Furthermore, helpful functions for complex types of pagination are available to help with tricky situations.

Intended as the successor to pharmaRTF, clinify modernizes table generation workflows with a more flexible and robust approach.

Read more about clinify on GitHub and find it on CRAN.

Co-developed with Incyte.

datasetjson

Utilities for Working With CDISC Dataset JSON

datasetjson enables reading, writing, and validating CDISC Dataset JSON files within R. It supports modern submission workflows and integration with metadata-driven pipelines.

Read more about datasetjson on GitHub.

Developed by Atorus and Johnson & Johnson.

envsetup

Project-Based Environment Initialization

envsetup helps teams set up consistent R environments by automatically loading packages, setting file paths, and applying project-specific options.

Read more about envsetup on GitHub.

Part of the pharmaverse.

gsm

Tools for Statistical Modeling in Clinical Workflows

The gsm package simplifies creation, evaluation, and reuse of statistical models in clinical trial analysis. Designed to support reproducibility and efficiency, it provides standardized modeling workflows for biostatistics teams.

Read more about gsm on GitHub.

Co-developed with Gilead.

logrx (formerly timber)

A Logging Utility Focused on Clinical Trial Programming Workflows

Where do you get your log file when you’re programming in R? logrx offers you just the solution. logrx is built on the paradigm of letting SAS^® be SAS^® and letting R be R while making sure that we provide the right information to support an audit trail of program execution. With an organized collection of session information, packages accessed, and collection of messages, warnings, and errors, logrx plugs the gap of an R execution log and helps unify the SAS^® and R programming processes.

Read more about logrx on GitHub and find it on CRAN.

Co-developed with GSK.

metacore

A Centralized Metadata Object Focused on Clinical Trial Data Programming Workflows

Utilizing metadata is essential to support efficiency and automation within clinical data workflows. Key to enabling this support is simplifying access to that metadata. metacore provides a single, in-memory interface for CDISC metadata that can be used by separate tools and packages within clinical programming workflows, so metadata can be read once and accessed as many times as needed thereafter.

Read more about metacore on GitHub and find it on CRAN.

Co-developed with GSK.

metatools

Enable the Use of “metacore” to Help Create and Check Data Sets

With packages like admiral to support ADaM programming workflows — and SDTM packages soon to follow — some programming is relevant to the life cycle of SDTM through tables, figures, and listings (TFL) as a whole. metatools is meant to be a home of metadata access functions that might be universally useful when working with CDISC data. Whether you’re doing compliance checks of data with your metadata, converting CT controlled variables to factors, or joining a SUPP back to a parent, metatools leverages metacore to automate these activities for you.

Read more about metatools on GitHub and find it on CRAN.

Co-developed with GSK and Roche.

pharmaRTF

An Enhanced RTF Wrapper for Use With Existing Table Packages

pharmaRTF allows users to leverage powerful R table packages to create industry standard format Rich Text Format (RTF) files. Using this platform, you can control document properties, repeat column headers across packages, include multilevel titles and footnotes, and more to create ICH-compliant formatted outputs.

Read more about pharmaRTF on GitHub and find it on CRAN.

Developed by Atorus Research.

pharmaverse

Atorus is just one part of the open-source analytics community supporting clinical programming efforts. Our packages and solutions are part of the larger pharmaverse, a curated, opinionated pharma stack of open-source R packages to enable clinical reporting from CRF to eSubmission, backed by a community of passionate individuals and organizations committed to co-creating efficiency in our mission to improve health.

Learn more at pharmarverse.org.

riskassessment

Risk-Based Assessment for R Packages

The riskassessment package supports the evaluation of R package risk in regulated workflows. It includes scoring logic, visualizations, and output formats to support package selection and documentation.

Read more about riskassessment on GitHub.

Developed by the R Validation Hub.

sdtm.oak

Rule-Based SDTM Dataset Creation

sdtm.oak transforms EDC raw data into SDTM datasets using reusable, rule-based logic. It reduces manual effort and increases transparency in SDTM conversion.

Read more about sdtm.oak on GitHub.

Part of the pharmaverse.

teal

Interactive Clinical Applications in R

teal provides a framework for building Shiny-based apps for interactive data review, subgroup analysis, and clinical reporting. Highly modular and extensible.

Read more about teal on GitHub.

Part of the InsightsEngineering ecosystem.

tidytlg

tidyverse-Friendly TLG Creation

tidytlg enables the creation of tables, listings, and graphs using tidyverse syntax. It standardizes the structure of TLGs while giving programmers flexibility and control.

Read more about tidytlg on GitHub.

Part of the pharmaverse.

Tplyr

The Grammar of Clinical Data Summaries

Tplyr is a package dedicated to simplifying the data manipulation necessary to create static clinical reports for the pharmaceutical industry. A commitment to ease of use and flexibility guided every step of the development process for Tplyr, in addition to the vision of building a tool that did more than just summarize your data. Tplyr ultimately provides a framework for not just creating presentation-ready summary data, but also the traceability to allow you to drill down to much more.

Read more about Tplyr on GitHub and find it on CRAN.