mlpack.org

Cite the mlpack 4 paper published in the Journal of Open Source Software. The mlpack website states that citations are useful for the continued development of the library, so if you use mlpack in published work, include this reference.

The official citation

The site provides the following paper as the citation to use:

mlpack 4: a fast, header-only C++ machine learning library.
R.R. Curtin, M. Edel, O. Shrit, S. Agrawal, S. Basak, J.J. Balamuta, R. Birmingham, K. Dutt, D. Eddelbuettel, R. Garg, S. Jaiswal, A. Kaushik, S. Kim, A. Mukherjee, N.G. Sai, N. Sharma, Y.S. Parihar, R. Swain, and C. Sanderson.
Journal of Open Source Software 8:82, 2023.

Why the project asks for it

The mlpack homepage explains that citations help support ongoing development. The project uses an open governance model and is fiscally sponsored by NumFOCUS, a nonprofit that supports open source scientific computing. Alongside donations, citations are one way the project measures and sustains its impact.

Where to find it

The citation appears on the mlpack home page, alongside the download link, license, and support information. If you need the current version of the reference, check the site directly rather than relying on a cached copy, since the paper details and version number are tied to the release the project points to.

Practical notes

• The paper covers mlpack 4, the header-only C++ library, so it is the appropriate reference for work using that version.
• mlpack is distributed under the permissive 3-clause BSD license, which is separate from the citation request: the license governs use and redistribution, while the citation is a scholarly acknowledgment.
• If you use the Python, Julia, R, Go, or command-line bindings, the same underlying library and paper apply.

mlpack is written in C++ and ships official bindings for Python, Julia, R, Go, and the command line, so you can use it from any of those six interfaces. The C++ library is header-only, meaning you include it directly rather than linking a compiled library. If your language isn't in that list, you'd need to call the C++ library through a foreign-function interface or use the command-line bindings from a subprocess.

The core: C++

mlpack's algorithms are implemented in C++ and built on three supporting libraries: Armadillo (linear algebra), ensmallen (optimization), and cereal (serialization). Because it's header-only, you add the include path and start using it — no separate build step for the library itself. This is the reference implementation that all other bindings wrap.

Official bindings

Each binding exposes the same underlying algorithms, but the calling conventions differ — argument names, data types, and how you pass matrices depend on the host language. Don't assume a Python example translates line-for-line into R or Go.

How to pick and where to look

• Already in C++? Use the C++ interface directly; it's the most complete and avoids binding overhead.
• Working in Python, Julia, R, or Go? Use the matching binding so you stay in your language's ecosystem and data structures.
• Using another language, or scripting? The command-line bindings let you invoke algorithms as separate programs, which works from any language that can run a subprocess.

Because the bindings differ, check the documentation for your specific language before writing code — the mlpack site links to per-language documentation from its Documentation page. The current release listed on the site is version 4.8.0.

Licensing and governance

mlpack is open source under the permissive 3-clause BSD license, which allows use in both open and proprietary projects. It uses an open governance model and is fiscally sponsored by NumFOCUS. If you use mlpack in published work, the project asks that you cite the 2023 Journal of Open Source Software paper describing mlpack 4.

mlpack is a header-only C++ machine learning library, so you do not compile mlpack itself as a separate library. Instead, you need a C++ compiler plus the libraries it builds on: Armadillo, ensmallen, and cereal. Because it is header-only, including the mlpack headers in your project is enough to use the library, provided those dependencies are available. The exact minimum versions are maintained in the documentation rather than on the home page, so check the docs for the version that matches your mlpack release.

Core dependencies

mlpack is written in C++ and built on these three libraries. If any of them is missing or too old, compilation of code that includes mlpack headers will fail.

What "header-only" means for your build

• There is no separate mlpack library binary to link against for the core C++ library.
• You add the mlpack include directory to your compiler's include path and #include the headers you need.
• Your compiler still needs to find Armadillo, ensmallen, and cereal headers, and you may need to link the underlying numerical libraries that Armadillo depends on (for example, a BLAS/LAPACK implementation), depending on your Armadillo configuration.

Bindings and other languages

mlpack also provides bindings to Python, Julia, R, Go, and the command-line. If you use a binding rather than writing C++ directly, the build requirements differ: you build or install the binding, which in turn depends on the C++ library and its dependencies. The home page lists these bindings but does not give per-binding build instructions, so consult the documentation for the binding you intend to use.

Practical build checklist

1. Install a C++ compiler that supports the C++ standard required by your mlpack version.
2. Install Armadillo, ensmallen, and cereal, meeting the minimum versions stated in the mlpack documentation for your release.
3. Make sure your build system can find those headers (include paths) and any libraries Armadillo needs.
4. Add the mlpack headers to your include path and compile your program.
5. If compilation fails, verify dependency versions first — version mismatches are the most common cause.

Version and licensing notes

• The latest version shown on the site is 4.8.0 (9.9 MB, released June 9, 2026).
• mlpack is open source under the permissive 3-clause BSD license.
• It uses an open governance model and is fiscally sponsored by NumFOCUS.

For the authoritative, version-specific dependency requirements, use the Documentation and Download links on mlpack.org rather than relying on the home page summary.

mlpack is released under the permissive 3-clause BSD license, and it is run under an open governance model with fiscal sponsorship from NumFOCUS. If you plan to use, modify, or redistribute mlpack in commercial or academic work, the BSD license generally lets you do so with minimal restrictions; if you want to understand who steers the project or how it is funded, the governance and sponsorship details below are the relevant parts.

License: 3-clause BSD

According to the mlpack website, mlpack is open source software licensed under the permissive 3-clause BSD license.

What this means in practice:

• You can use mlpack in open source and proprietary projects.
• You can modify the source code and redistribute it.
• The main obligations are typical BSD-style conditions, such as retaining the copyright notice and license text, and not using the names of the project or contributors to endorse derived products without permission.

The site also notes the copyright line: © 2007–2024 mlpack developers (BSD License). For exact legal terms, the authoritative text is the license file distributed with the project or linked from the mlpack repository.

Governance: open governance model

mlpack states that it uses an open governance model. The site does not spell out the full decision-making structure on the home page, but the key takeaway is that the project is not controlled by a single vendor and is intended to be community-driven.

For users and contributors, this matters because:

• Project direction and contributions are handled through public, community-oriented processes rather than a closed corporate roadmap.
• Development happens in the open, with the GitHub repository and documentation as the primary public resources.

Fiscal sponsorship: NumFOCUS

mlpack is fiscally sponsored by NumFOCUS. NumFOCUS is described on the site as a nonprofit dedicated to supporting the open source scientific computing community.

This sponsorship provides a legal and financial home for the project. It allows mlpack to receive and manage funds for things such as:

• Developer time
• Professional services
• Travel
• Workshops
• A variety of other project needs

Support and donations

Because of the NumFOCUS sponsorship, mlpack can accept tax-deductible donations to help pay for the items listed above. The site invites users to consider making a donation to support the project.

mlpack also acknowledges support from other organizations, though the home page excerpt does not name them individually.

Quick reference

What this means for you

• If you are adopting mlpack: The 3-clause BSD license is business-friendly and compatible with most commercial and academic uses, subject to standard notice requirements.
• If you are contributing: The open governance model and NumFOCUS sponsorship indicate a community-oriented project with a nonprofit backing structure.
• If you want to support the project: Donations are accepted through the NumFOCUS fiscal sponsorship and are described as tax-deductible.

For the most current and complete governance or licensing details, check the mlpack website and its GitHub repository, since those are the project’s official sources.

mlpack is a fast, header-only C++ machine learning library with bindings to Python, Julia, R, Go, and the command-line. mlpack.org is its home site, where you can download the latest release, read documentation, ask questions, and reach the GitHub repository. It is open source under the permissive 3-clause BSD license and is fiscally sponsored by NumFOCUS.

What mlpack is

mlpack is written in C++ and built on three supporting libraries: Armadillo (linear algebra), ensmallen (numerical optimization), and cereal (serialization). Its stated aim is to provide fast, extensible implementations of cutting-edge machine learning algorithms.

Two design choices follow from that description:

• Header-only C++: the library is distributed as headers rather than a compiled binary, which is why the download is small (the latest release is listed at 9.9MB).
• Multi-language bindings: the same algorithms are reachable from Python, Julia, R, Go, and the command line, so you are not locked into writing C++ to use it.

What mlpack.org offers

The site is organized around a short set of entry points:

The home page also carries a release announcement link, citation information, license terms, and support details.

Version, license, and governance

• Latest version: 4.8.0, released June 9, 2026, listed at 9.9MB.
• License: 3-clause BSD, a permissive open source license.
• Governance: an open governance model, fiscally sponsored by NumFOCUS, a nonprofit supporting the open source scientific computing community. The project also lists support from other organizations.

Citing mlpack

If you use mlpack in published work, the site asks you to cite the project paper, noting that citations help support continued development:

mlpack 4: a fast, header-only C++ machine learning library. R.R. Curtin, M. Edel, O. Shrit, S. Agrawal, S. Basak, J.J. Balamuta, R. Birmingham, K. Dutt, D. Eddelbuettel, R. Garg, S. Jaiswal, A. Kaushik, S. Kim, A. Mukherjee, N.G. Sai, N. Sharma, Y.S. Parihar, R. Swain, and C. Sanderson. Journal of Open Source Software 8:82, 2023.

Who it is for

mlpack fits you if you want machine learning algorithms implemented in C++ with a focus on speed and extensibility, or if you want to call those implementations from Python, Julia, R, Go, or a shell. If you need a purely Python-native workflow with no C++ layer, or you want a library with a large prebuilt binary distribution, check the documentation and download page first to confirm the bindings and build path match your environment.