sciruby.com

SciRuby is an open-source collection of tools for scientific computing and data visualization in Ruby. It is aimed at Ruby developers who want to do numerical work — linear algebra, machine learning, statistical modelling, and plotting — without leaving the language. The project's centerpiece is NMatrix, a linear algebra library with dense and sparse matrix storage and tensor support. SciRuby is released under the BSD three-clause license, and its components are described by the project itself as tested but "not battle-hardened," so it suits exploration, research code, and tooling rather than mission-critical systems without caution.

What SciRuby includes

The project spans several areas rather than being a single library:

• Linear algebra — NMatrix is the flagship component, supporting both dense and sparse matrix storage. Tensor support was added more recently. MDArray is a related linear algebra effort the project lists among its goals.
• Visualization — Several experimental plotting libraries exist, including Rubyvis, Plotrb, and Nyaplot. The project also expresses interest in improving existing APIs such as Gnuplot.
• Machine learning and statistics — The site mentions tools for machine learning and linear mixed modelling as part of the ecosystem.

The project's stated short-term goals are improved visualization software and continued development of its linear algebra software (NMatrix and MDArray).

Who it is for

SciRuby targets people who already value Ruby and want to use it for scientific work. The project's manifesto argues that Ruby is a good choice for scientific computing and data visualization, acknowledging that other languages have more libraries while maintaining that Ruby "has its own place in Science." If you are comfortable in Ruby and prefer not to switch languages for numerical tasks, SciRuby is the ecosystem built for you. If you need a mature, heavily battle-tested numerical stack, the project's own warning about bugs is worth taking seriously.

Project status and maturity

The site is direct about maturity: the gems "have been tested, but are not battle-hardened." It explicitly cautions against using NMatrix or other SciRuby components for mission-critical code — the examples given are a self-driving car or controlling an ARKYD 100 satellite — and advises expecting bugs. Treat SciRuby as a capable but evolving toolkit, and plan for debugging.

Getting started

Installation instructions for the various SciRuby components are available in the project's docs, and NMatrix documentation is hosted separately. The site does not list prices or login requirements, so check the docs directly for current setup details.

Community and contributing

• Google Group — The site offers an email subscription form to join the SciRuby Google Group.
• IRC — The project's channel is #sciruby on Freenode.net. It is most active during Google Summer of Code months, though a few people are generally around.
• Contributing — The best entry point for new contributors is the NMatrix issue tracker on GitHub: pick something that interests you and fork the project. If matrices are not your focus, the SciRuby issue tracker is the alternative.
• Developers — A full roster is maintained on the project wiki.

License and history

SciRuby and NMatrix are available under the BSD three-clause license. The project acknowledges Google Summer of Code participation across 2013, 2014, 2015, and 2016–2019, along with Google's Open Source Programs Office.

SciRuby components are distributed as Ruby gems, and installation instructions for each component are available in the project's docs. The main library you'll likely install first is NMatrix, the linear algebra library, which has its own documentation page. Before you start, note the project's own warning: these gems have been tested but are not "battle-hardened," so if you plan to use them in mission-critical code, expect to encounter bugs.

What SciRuby Actually Provides

SciRuby is a collection of tools for scientific computing and data visualization in Ruby. According to the project's own status page, the released core is NMatrix, the linear algebra library, which supports both dense and sparse matrix storage. The project has also added tensor support, and lists visualization tools plus libraries for machine learning and linear mixed modelling.

Several plotting libraries exist at an experimental stage — Rubyvis, Plotrb, and Nyaplot — and the project states an interest in improving existing APIs such as Gnuplot. The stated short-term goals are improved visualization software and continued development of the linear algebra software (NMatrix and MDArray).

Installation: What the Official Sources Tell You

The site does not reproduce full install commands on the main page; instead it points you to the documentation:

• General instructions for installing the various SciRuby components are in the project's docs.
• NMatrix documentation has its own dedicated page.

This means the reliable path is: identify the component you want, then follow its documentation rather than guessing at commands. The site does not list specific gem names, version numbers, or dependency requirements beyond what is described above, so treat any command you find outside the official docs as unverified.

A Practical Sequence

1. Confirm you have a working Ruby environment. SciRuby components are Ruby gems, so a functioning Ruby installation is the prerequisite. The site does not specify a minimum Ruby version.
2. Decide which component you need. For matrix and linear algebra work, that is NMatrix. For plotting, the options listed are experimental (Rubyvis, Plotrb, Nyaplot) or API-improvement targets (Gnuplot).
3. Go to the docs for that component. The main site directs you there rather than hosting the steps itself.
4. Install and then verify with a small task. Because the project warns about bugs, test with something trivial before building on it — for example, construct a small dense matrix and a small sparse matrix and confirm both behave as expected.

Before You Commit to It

The most important thing the project says about readiness is its own caution: the gems "have been tested, but are not battle-hardened." The site gives deliberately extreme examples — a self-driving car or controlling an ARKYD 100 satellite — to make the point that mission-critical use should come with an expectation of bugs and a plan to handle them.

That framing is useful for deciding whether to install at all:

Getting Help and Contributing

If you get stuck or want to shape the tools, the project points to a few channels:

• Google Group — the site offers a subscription form for the SciRuby Google Group.
• IRC — #sciruby on Freenode.net. The project notes the channel is most active during Google Summer of Code months, but generally a few people are around.
• Issue trackers — the NMatrix issue tracker on GitHub is described as the best place to start if you want to contribute; there is also a general SciRuby issue tracker.
• Full developer roster — available on the project wiki.

Licensing

SciRuby and NMatrix are available under the BSD three-clause license, so you can review the terms before adopting them in your own project.

The short version: install via the official docs, start with NMatrix if you need linear algebra, verify with a small test, and weigh the project's own "not battle-hardened" warning against how critical your use case is.

You can get involved with SciRuby in two main ways: contribute code through the project's GitHub issue trackers, or reach the community through its Google Group and IRC channel. The most direct starting point for new contributors is the NMatrix issue tracker on GitHub — pick an issue that interests you and fork the repository. If matrix work isn't your focus, the broader SciRuby issue tracker is the alternative. For questions and discussion, subscribe to the SciRuby Google Group or join the #sciruby channel on Freenode.

Contributing Code

SciRuby's own guidance for newcomers is to "pick something that interests you and fork us." The project points to two issue trackers depending on your interest:

NMatrix is the project's released linear algebra library, supporting both dense and sparse matrix storage, with tensor support added more recently. That makes it the most active and best-documented place to make a first contribution.

Before you start, note the project's own warning about its gems: they "have been tested, but are not battle-hardened." The SciRuby team explicitly says that anyone using NMatrix or other components for mission-critical code — their examples are a self-driving car or controlling an ARKYD 100 satellite — "should expect to encounter a few bugs — and be prepared for them." For a contributor, this is useful context: bug reports and fixes are genuinely welcome, not just feature work.

Contacting the Community

Google Group. The site offers a subscription form: enter your email to subscribe to the SciRuby Google Group. This is the asynchronous option for questions and announcements.

IRC. The project's IRC channel is #sciruby on Freenode.net. Be aware of the activity pattern the site describes: the channel "is most active during the months of Google Summer of Code, but generally there are always a few people around." If you need a fast answer outside GSoC season, expect a slower response.

Finding the Full Developer Roster

The complete list of SciRuby developers is on the project wiki, not on the main site. If you want to know who maintains a specific component before opening an issue or pull request, check the wiki first.

Licensing and Background

SciRuby and NMatrix are released under the BSD three-clause license, so contributions and reuse are governed by a permissive license. The project has participated in Google Summer of Code in 2013, 2014, 2015, and 2016–2019, which is why IRC activity spikes during those periods — and why GSoC season is a reasonable time to introduce yourself if you're considering a larger contribution.

Practical Order of Operations

1. Check the developer roster on the project wiki to see who works on what.
2. Browse the NMatrix issue tracker (or the SciRuby tracker) and pick an issue.
3. Fork the repository and work on your chosen issue.
4. Subscribe to the Google Group for announcements and longer-form questions.
5. Join #sciruby on Freenode for real-time discussion, keeping in mind the GSoC-season activity pattern.

Installation instructions for the various SciRuby components live in the project docs, with NMatrix documentation available separately — worth reading before you file an issue about setup problems.

SciRuby is an active collection of Ruby libraries for scientific computing, and its most mature component is NMatrix, a released linear algebra library that supports both dense and sparse matrix storage. The project has also added tensor support and offers tools for machine learning and linear mixed modelling, alongside several experimental plotting libraries. If you need a battle-tested numerical stack, treat these gems as usable but not fully hardened — the project itself warns that mission-critical use should expect bugs.

Core toolset

NMatrix (linear algebra)

• Released and described as the SciRuby linear algebra library.
• Supports dense and sparse matrix storage.
• Recently gained tensor support.
• Documentation is available through the project's docs.

MDArray

• Listed as an ongoing linear algebra development target alongside NMatrix.

Visualisation and domain tools

• The project states it has "great visualisation tools" and tools for machine learning and linear mixed modelling.
• Experimental plotting libraries include Rubyvis, Plotrb, and Nyaplot.
• There is also interest in improving existing APIs such as Gnuplot.

Project status and short-term goals

The stated short-term goals are:

1. Improved visualisation software.
2. Continued development of the linear algebra software (NMatrix and MDArray).

Getting the components

Installation instructions for the various SciRuby components are in the project's docs, with NMatrix documentation available separately. The project gives a direct caution: these gems have been tested but are not battle-hardened. If you plan to use NMatrix or other SciRuby components for mission-critical code — the project's own examples are a self-driving car or controlling an ARKYD 100 satellite — expect to encounter bugs and be prepared for them.

Community and contribution

• Google Group: subscription is available via an email signup form on the site.
• IRC: #sciruby on Freenode.net; most active during Google Summer of Code months, though generally a few people are around.
• Developers: the full roster is on the project wiki.
• Getting involved: the NMatrix issue tracker on GitHub is recommended as the best starting point — pick something that interests you and fork. The SciRuby issue tracker is an alternative if matrices aren't your focus.

Licensing

SciRuby and NMatrix are available under the BSD three-clause license.

SciRuby's answer is straightforward: Ruby is a great choice for scientific computing and data visualization, and it is "always a pleasure to write software in Ruby." The manifesto concedes that other languages have more libraries, but argues Ruby still has its own place in science. The only way to test that claim, it says, is by writing code. This page is for developers deciding whether Ruby fits a scientific or data-heavy project, and for anyone trying to understand what SciRuby actually provides.

The core argument of the manifesto

The SciRuby Manifesto makes three points:

• Ruby is well suited to scientific computing and visualization — not just general-purpose scripting.
• Other ecosystems have more libraries — SciRuby does not dispute this, and says it "cannot do without them."
• Ruby has its own place in science — the value is partly in the experience of writing the software itself.

The manifesto illustrates its optimism with an analogy: when a sugar-and-water solution becomes super-saturated, a pure crystal of sweetness precipitates with no more than the tap of a finger. SciRuby applies the same image to numeric and visualization work in Ruby — the ecosystem may be close to a breakthrough, triggered by small contributions.

That analogy is a statement of belief, not a roadmap. Treat it as the project's framing rather than a technical guarantee.

What SciRuby actually ships

The manifesto's optimism is backed by concrete components. According to the project's own status page:

The stated short-term goals are improved visualization software and continued development of the linear algebra software (NMatrix and MDArray). The project also expresses interest in improving existing APIs, such as for Gnuplot.

So the practical answer to "why Ruby for scientific computing" is: because a linear algebra core with dense and sparse storage exists, alongside a set of plotting and modelling libraries — even if several of the plotting tools are still experimental.

Before you commit: the project's own warning

SciRuby is explicit about maturity. Its installation guidance says the gems "have been tested, but are not battle-hardened." It gives deliberately extreme examples — a self-driving car or controlling an ARKYD 100 satellite — and warns that in mission-critical code you should expect to encounter bugs and be prepared for them.

That warning is the most useful decision input on the site. It means:

• SciRuby is reasonable for exploration, research code, and prototyping where you can absorb a bug or two.
• It is a riskier default for production systems where a numerical error or crash is unacceptable without a fallback plan.

If your project falls in the second category, budget time for testing and expect to work around issues rather than assume stability.

How to get started

1. Read the docs first. Installation instructions for the various SciRuby components live in the project's docs, and NMatrix has its own documentation page. Start there rather than guessing at gem names.
2. Install the component you need — for linear algebra, that is NMatrix; for plotting, look at the experimental libraries listed above.
3. Verify against a small problem. Because the gems are not battle-hardened, run a known-answer test on your own data before building on top of them.
4. Expect to write code to judge the claim. The manifesto's own conclusion is that the only way to find out whether Ruby suits your scientific work is to try it.

Where to get help and contribute

• Google Group — the site offers a subscription form for the SciRuby Google Group.
• IRC — #sciruby on Freenode.net. The channel is most active during Google Summer of Code months, but the project says a few people are generally around.
• Issues and contribution — the best entry point for new contributors is the NMatrix issue tracker on GitHub; the SciRuby issue tracker is the alternative if matrices are not your interest. The full developer roster is on the project wiki.
• License — SciRuby and NMatrix are available under the BSD three-clause license, which matters if you plan to embed them in your own project.

Who should consider SciRuby

Choose it if you already write Ruby, want a dense-and-sparse matrix library plus plotting options in the same language, and can tolerate experimental tooling. Look elsewhere first if you need a battle-hardened numerical stack for mission-critical code, or if you depend on a mature, broad visualization API — the project itself lists improved visualization as a goal still in progress.