The Ultimate Guide to the brms.mmrm Package in R

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Explore valuable documentation and insights to make the most of the brms.mmrm package in R. Get ready to unlock the full potential of the brms.mmrm package!

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What is the brms.mmrm package?

In this section, we’ll delve into the fundamental aspects and key features of the package.

brms.mmrm is an R package that extends the brms package to support mixed model repeated measures (MMRM) designs. MMRM designs are often used in clinical trials where measures are taken on the same subjects over time.

Title: Bayesian MMRMs using 'brms'
Description: The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and 'brms' is a powerful and versatile package for fitting Bayesian regression models. The 'brms.mmrm' R package leverages 'brms' to run MMRMs, and it supports a simplified interfaced to reduce difficulty and align with the best practices of the life sciences. References: Bürkner (2017) , Mallinckrodt (2008) .
Author: William Michael Landau [aut, cre] (), Kevin Kunzmann [aut] (), Yoni Sidi [aut], Christian Stock [aut] (), Eli Lilly and Company [cph, fnd], Boehringer Ingelheim Pharma GmbH & Co. KG [cph, fnd]
Maintainer: William Michael Landau

How to install the brms.mmrm package?

In this section, we’ll walk you through the process of installing and loading the brms.mmrm package. By following these steps, you can seamlessly add new functions, datasets, and other resources to your R environment for a more robust workflow.

# Install the {Package} package if it's not already installed
if (!require("{Package}")) {
    install.packages("{Package}")
}


# Load the {Package} package
library("{Package}")

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What package information should you know?

In this section, we’ll go over the technical aspects of the brms.mmrm package.

Key features

Datasets: Many R packages include built-in datasets that you can use to familiarize yourself with their functionalities. To identify built-in datasets. To identify the datasets for the brms.mmrm package, visit our database of R datasets.
Vignettes: R vignettes are documents that include examples for using a package. To view the list of available vignettes for the brms.mmrm package, you can visit our visit our database of R vignettes.
Citation: Citing R packages in your publications is important as it recognizes the contributions of the developers. To find citation information for the brms.mmrm package, visit our database of R package citations.

Technical details

License type: MIT + file LICENSE. For license details, visit the Open Source Initiative website.
Compilation requirements: Some R packages include internal code that must be compiled for them to function correctly. The brms.mmrm package does not have compilation requirements.
Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The brms.mmrm package has the following required dependencies: R (>= 4.0.0).
Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The brms.mmrm package has the following suggested dependencies: BH, knitr (>= 1.30), markdown (>= 1.1), Rcpp, RcppEigen, RcppParallel, rmarkdown (>= 2.4), rstan, StanHeaders, testthat (>= 3.0.0).
External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The brms.mmrm package does not use any external sources.
Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The brms.mmrm package has the following imported packages: brms, coda, dplyr, emmeans (>= 1.8.7), ggplot2, ggridges, MASS, posterior, purrr, rlang, stats, tibble, tidyr, tidyselect, trialr, utils, zoo.
Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The brms.mmrm package has no enhancements.

How to get help with the brms.mmrm package?

In this section, we’ll discuss a variety of available resources for getting help with the brms.mmrm package.

Key resources

The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("brms.mmrm") function to retrieve detailed information, examples, and usage instructions. Alternatively, you can use the ? operator as a shortcut.
Package website: The brms.mmrm package has a dedicated website. You can visit: https://openpharma.github.io/brms.mmrm/, https://github.com/openpharma/brms.mmrm.
Developer support: You can email William Michael Landau . For contact information, visit our R community directory.

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