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From "Gilles (JIRA)" <>
Subject [jira] [Commented] (STATISTICS-7) Stream-based Java statistical processing
Date Tue, 26 Mar 2019 10:59:00 GMT


Gilles commented on STATISTICS-7:


Contributors are most wanted.

The idea behind the new "Commons Statistics" component is to offer the same statistical tools
as exist under the {{org.apache.commons.math4.stat}} package of ["Commons Math"|]
(see also STATISTICS-5) but with the opportunity for a complete overhaul in order to fix design
issues that have existed for years (see e.g. [this list of JIRA reports|]),
as well as for a modern API (Java 8+).

There isn't yet any specific plan or priorities; anything can be proposed to start a discussion,
and people with an interest in using these functionalities (i.e. having real-life problems
to solve) are especially welcome to drive the discussions.

> Stream-based Java statistical processing
> ----------------------------------------
>                 Key: STATISTICS-7
>                 URL:
>             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
> 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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