mixOmics

What the repository contains

This repository holds the source for mixOmics, an R package that is published on Bioconductor and mirrored through a GitHub development version. The project page at mixomics.org points users to documentation and usage notes, while the package itself is the unit that analysts install and load into an R session. The maintainers note that the README is generated from an R Markdown source and should not be edited by hand, which keeps the rendered documentation in step with the package build. Because the package targets statistical work inside R, the distribution model matters as much as the code: most users get a tested release through Bioconductor twice a year, and the GitHub copy is offered for those who want changes between those releases. The README is explicit that the GitHub version is under development and may be unstable, so it directs users to tagged releases that have passed package testing when stability is a concern. This split between a curated release channel and a rolling development channel is a common pattern for Bioconductor packages, and mixOmics follows it directly. The repository also carries a code of conduct for contributors and welcomes community contributions that fit within it, so the development process is documented as well as the install steps. For someone new to the package, the first encounter is therefore not a code download but a choice between a stable Bioconductor release and a newer GitHub build, and the README makes that choice clear before any command is run.

Three ways to install

The recommended path is Bioconductor. The README shows installing BiocManager if needed and then running BiocManager::install('mixOmics'), followed by library(mixOmics) to load it. This pulls the release build that matches the current Bioconductor cycle. A second path uses GitHub through devtools, with devtools::install_github('mixOmicsTeam/mixOmics') fetching the latest development source between Bioconductor updates. The third path is a Docker container that wraps the latest stable GitHub build, which the README presents as an option for users who prefer a reproducible environment. macOS users get a specific caveat in every path: they must install XQuartz first, since the package relies on system components that Apple no longer bundles. Each install method is described with copy-paste command blocks, so the friction of getting the package running is kept low regardless of whether someone works in a plain R session or a container. The README also notes that Bioconductor versions are updated twice a year, which is why the GitHub channel exists in the gaps between those updates. For teaching and for shared compute, the Docker image is the path of least resistance because it fixes the R version and system libraries. The net effect is three clearly separated routes, one stable, one bleeding edge, and one containerized, each with its own tradeoff between freshness and reproducibility that the documentation spells out.

Running the Docker image

The Docker route is documented in detail because it gives a self contained workspace. After installing Docker, the user pulls mixomicsteam/mixomics and starts it with a chosen password and a port mapping of 8787:8787. The container then serves an RStudio instance that is reached by opening http://localhost:8787 in a browser and logging in with the username rstudio and the password set at launch. The README lists the running image with docker images and shows how to stop a container by id with docker stop, so basic lifecycle commands are covered. The image size is noted as roughly 4.38 GB in the example output, which sets an expectation for disk and download time. This approach is useful for teaching and for shared compute, since the environment is fixed and does not depend on the host having the right R version or system libraries. It also lets macOS users avoid the XQuartz step on the host by keeping that dependency inside the container. The README walks through inspecting running containers with docker ps and then stopping by the listed container id, so a new user can follow the whole cycle from pull to shutdown without guessing. Ports can be customized if 8787 is taken, and the password is set at launch rather than baked in, which keeps the image reusable across machines. For a course or a workshop, handing out one image that already contains R, the package, and RStudio removes the most common setup failures before they happen.

Editorial conclusion

The package is published on Bioconductor and carries the GPL license, version 2 or later, and it is written in R.