Abstract
This paper discusses the definitions of open source software, free software and freeware, and the concept of big data. The authors then introduce R and Python as the two most popular open source statistical software (OSSS). Additional OSSS, such as JASP, PSPP, GRETL, SOFA Statistics, Octave, KNIME, and Scilab, are also introduced in this paper with function descriptions and modeling examples. They further discuss OSSS's capability in artificial intelligence application and modeling and Popular OSSS-based machine learning libraries and systems. The paper intends to provide a reference for readers to make proper selections of open source software when statistical analysis tasks are needed. In addition, working platform and selective numerical, descriptive and analysis examples are provided for each software. Readers could have a direct and in-depth understanding of each software and its functional highlights.
Highlights
In this paper, the authors discuss the most popular open source statistical software with its creation history, target practitioners, and statistical usage examples
The objective of this paper is to create a reference for the readers and guide them to make proper selection of open source software when a statistical analysis task is in demand
Lakens (2017) wrote that Jamovi was developed by a group of developers who used to work on JASP, the user interface and functionality have a lot of similarities
Summary
The authors discuss the most popular open source statistical software with its creation history, target practitioners, and statistical usage examples. The book introduces OSSS, presents multiple applications and discusses research opportunities. We first introduced an artificial intelligence (AI) techniques categorization, and surveyed the popular OSSS designed for AI applications. The objective of this paper is to create a reference for the readers and guide them to make proper selection of open source software when a statistical analysis task is in demand. Second section of the paper presents multiple popular OSSS, such as R, Python and etc., designed for statistical applications are presented. Popular AI techniques are categorized and briefly described, and OSSS designed for AI processing are presented. The authors focus on creating an overview of all open source statistical software in this paper. Readers can be benefitted from this short reference of Open Source Statistical Software
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