---
layout: default-nav
title: FAQ
displaytitle: FAQ
description: mlpack | fast, flexible C++ machine learning library
order: 6
---

<div class="page-title-header">FAQ</div>

 * **I found a bug in the code and/or documentation. How do I report it?**

    Please make a small and self-contained program that exposes the bug, and
then open an issue at the [Github
repo](https://github.com/mlpack/mlpack/issues).  Issue templates are available
to help with the bug report process.

 * **How do I cite mlpack in my work?**

    Please cite the following paper if you use mlpack in your work.  Citations
    are useful for the continued development of the library.

    * R.R. Curtin, M. Edel, M. Lozhnikov, Y. Mentekidis, S. Ghaisas, S. Zhang.
      "mlpack 3: a fast, flexible C++ machine learning library." Journal of Open
      Source Software 3(26): pp. 726, 2018.
    <p/>

 * **What is the distribution license for mlpack?**

    mlpack is licensed under the permissive [3-clause BSD
    license](http://opensource.org/licenses/BSD-3-Clause).  See also a [Quick
    Summary](https://tldrlegal.com/license/bsd-3-clause-license-(revised)) of
    the license.

 * **Who are the developers?**

    mlpack is a community-led effort. mlpack uses an open governance model and
    is fiscally sponsored by NumFOCUS.  Consider making a [tax-deductible
    donation](https://numfocus.org/donate-to-mlpack) to help the project pay for
    developer time, professional services, travel, workshops, and a variety of
    other needs. See the [community](community.html#developers) page for more
    details and a list of contributors.

 * **Where is the source code?**

    mlpack development is done on [GitHub](https://github.com/mlpack/mlpack) and
    the source code is available there.  There are a few related repositories
    that might be worth checking out also:

    - [ensmallen](https://github.com/mlpack/ensmallen/): numerical optimization
      library

    - [examples](https://github.com/mlpack/examples/): simple examples of mlpack
      usage

    - [models](https://github.com/mlpack/models/): additional implementations of
      machine learning models

 * **Do you accept contributions?**

    Contributions are absolutely welcome for mlpack, and anyone is welcome to
    participate and contribute.  See the [community](community.html) page for
    more details.