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https://issues.apache.org/jira/browse/STATISTICS-7?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16805011#comment-16805011
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Gilles commented on STATISTICS-7:
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The question of matrix utilities is indeed fundamental and makes porting part
of the stat tools not an abvious task.
The "linear" package itself is in need of refactoring (see list of open issues
on the [Commons Math JIRA
page|https://issues.apache.org/jira/projects/MATH/issues/MATH].
The new "STATISTICS" component is logically intended to be a dependency for the
next major release of (legacy) "Commons Math" (i.e. v.4.0) who will contain
whatever codes have not been moved to more focused components (i.e. "Commons
Numbers", "Commons RNG", "Commons Geometry" and "Commons Statistics"). Hence
making the last/old official release of CM a dependency seems *not* the right
way to go, indeed. ;-)
This can be solved "temporarily", even though you are right that this is bad in
general, by copying (into _private_ or _package-private_ classes) the necessary
functionality. Better still would be to consider a refactoring (bringing in
only required functionality) of the linear algebra utilities specifically
geared to its usage in the STATISTICS component.
> Stream-based Java statistical processing
> ----------------------------------------
>
> Key: STATISTICS-7
> URL: https://issues.apache.org/jira/browse/STATISTICS-7
> Project: Apache Commons Statistics
> Issue Type: New Feature
> Reporter: Eric Barnhill
> Priority: Major
> Labels: GSoC2019, gsoc2019, statistics, streams
>
> The new component aims to be a library of commons statistics functions
> synchronized with the latest developments in the Java language, in particular
> Java's functional programming syntax.
> The library will make commonly used statistical functions available to an end
> user through a simple grammar comparable to commons-math-statistics or
> scikit-learn, while under the hood will implement Java's mapping, streaming,
> and other producer and consumer functions to ensure the statistical methods
> run optimally in new Java implementations.
> Developers working on the project will have the opportunity to demonstrate
> Java programming, functional programming, algorithm design, and data science
> skills and receive authorship on a commons project that is likely to be
> widely used.
> The ideal contributor will also be able to help with important architectural
> decision making. The old source of these libraries, commons-math, grew too
> large, hierarchically complex and interdependent for the commons mission. The
> developers on this project need to make architectural choices that will
> enable the statiscal code to be lightweight and reusable, with a minimum of
> outside dependencies while avoiding redundancy.
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