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| <full_title>Journal of Open Source Software</full_title> | ||
| <abbrev_title>JOSS</abbrev_title> | ||
| <issn media_type="electronic">2475-9066</issn> | ||
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| <month>10</month> | ||
| <year>2023</year> | ||
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| <volume>8</volume> | ||
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| <title>bayes-toolbox: A Python package for Bayesian | ||
| statistics</title> | ||
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| <person_name sequence="first" contributor_role="author"> | ||
| <given_name>Hyosub E.</given_name> | ||
| <surname>Kim</surname> | ||
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| <year>2023</year> | ||
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| <citation key="patil2010pymc"> | ||
| <article_title>PyMC: Bayesian Stochastic Modelling in | ||
| Python</article_title> | ||
| <author>Patil</author> | ||
| <journal_title>Journal of Statistical | ||
| Software</journal_title> | ||
| <issue>4</issue> | ||
| <volume>35</volume> | ||
| <doi>10.18637/jss.v035.i04</doi> | ||
| <cYear>2010</cYear> | ||
| <unstructured_citation>Patil, A., Huard, D., & | ||
| Fonnesbeck, C. J. (2010). PyMC: Bayesian Stochastic Modelling in Python. | ||
| Journal of Statistical Software, 35(4), 1. | ||
| https://doi.org/10.18637/jss.v035.i04</unstructured_citation> | ||
| </citation> | ||
| <citation key="kruschke2014doing"> | ||
| <volume_title>Doing Bayesian Data Analysis: A Tutorial with | ||
| R, JAGS, and Stan</volume_title> | ||
| <author>Kruschke</author> | ||
| <doi>10.1016/c2012-0-00477-2</doi> | ||
| <cYear>2014</cYear> | ||
| <unstructured_citation>Kruschke, J. (2014). Doing Bayesian | ||
| Data Analysis: A Tutorial with R, JAGS, and Stan. Academic Press. | ||
| https://doi.org/10.1016/c2012-0-00477-2</unstructured_citation> | ||
| </citation> | ||
| <citation key="wilson2017good"> | ||
| <article_title>Good enough practices in scientific | ||
| computing</article_title> | ||
| <author>Wilson</author> | ||
| <journal_title>PLoS computational biology</journal_title> | ||
| <issue>6</issue> | ||
| <volume>13</volume> | ||
| <doi>10.1371/journal.pcbi.1005510</doi> | ||
| <cYear>2017</cYear> | ||
| <unstructured_citation>Wilson, G., Bryan, J., Cranston, K., | ||
| Kitzes, J., Nederbragt, L., & Teal, T. K. (2017). Good enough | ||
| practices in scientific computing. PLoS Computational Biology, 13(6), | ||
| e1005510. | ||
| https://doi.org/10.1371/journal.pcbi.1005510</unstructured_citation> | ||
| </citation> | ||
| </citation_list> | ||
| </journal_article> | ||
| </journal> | ||
| </body> | ||
| </doi_batch> |
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| <?xml version="1.0" encoding="utf-8" ?> | ||
| <!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" | ||
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| <front> | ||
| <journal-meta> | ||
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| <journal-title-group> | ||
| <journal-title>Journal of Open Source Software</journal-title> | ||
| <abbrev-journal-title>JOSS</abbrev-journal-title> | ||
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| <issn publication-format="electronic">2475-9066</issn> | ||
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| <publisher-name>Open Journals</publisher-name> | ||
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| <article-meta> | ||
| <article-id pub-id-type="publisher-id">5526</article-id> | ||
| <article-id pub-id-type="doi">10.21105/joss.05526</article-id> | ||
| <title-group> | ||
| <article-title>bayes-toolbox: A Python package for Bayesian | ||
| statistics</article-title> | ||
| </title-group> | ||
| <contrib-group> | ||
| <contrib contrib-type="author"> | ||
| <contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-0109-593X</contrib-id> | ||
| <name> | ||
| <surname>Kim</surname> | ||
| <given-names>Hyosub E.</given-names> | ||
| </name> | ||
| <xref ref-type="aff" rid="aff-1"/> | ||
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| <aff id="aff-1"> | ||
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| <institution>School of Kinesiology, The University of British Columbia, | ||
| Canada</institution> | ||
| </institution-wrap> | ||
| </aff> | ||
| <aff id="aff-2"> | ||
| <institution-wrap> | ||
| <institution>Department of Physical Therapy, University of Delaware, | ||
| United States</institution> | ||
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| </contrib-group> | ||
| <pub-date date-type="pub" publication-format="electronic" iso-8601-date="2023-04-22"> | ||
| <day>22</day> | ||
| <month>4</month> | ||
| <year>2023</year> | ||
| </pub-date> | ||
| <volume>8</volume> | ||
| <issue>90</issue> | ||
| <fpage>5526</fpage> | ||
| <permissions> | ||
| <copyright-statement>Authors of papers retain copyright and release the | ||
| work under a Creative Commons Attribution 4.0 International License (CC | ||
| BY 4.0)</copyright-statement> | ||
| <copyright-year>2022</copyright-year> | ||
| <copyright-holder>The article authors</copyright-holder> | ||
| <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"> | ||
| <license-p>Authors of papers retain copyright and release the work under | ||
| a Creative Commons Attribution 4.0 International License (CC BY | ||
| 4.0)</license-p> | ||
| </license> | ||
| </permissions> | ||
| <kwd-group kwd-group-type="author"> | ||
| <kwd>Python</kwd> | ||
| <kwd>Bayesian statistics</kwd> | ||
| <kwd>psychology</kwd> | ||
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| </article-meta> | ||
| </front> | ||
| <body> | ||
| <sec id="summary"> | ||
| <title>Summary</title> | ||
| <p><monospace>bayes-toolbox</monospace> is a Python package intended | ||
| to facilitate the increased use and adoption of Bayesian statistics in | ||
| scientific research. As Python is one of the fastest growing and most | ||
| widely used programming languages, | ||
| <monospace>bayes-toolbox</monospace> fills the need for a Python | ||
| library that makes it as easy to perform Bayesian statistical tests as | ||
| it currently is to perform their “frequentist” counterparts. The | ||
| intended users of <monospace>bayes-toolbox</monospace> are students | ||
| and researchers, particularly those in the behavioral and neural | ||
| sciences, who are looking for a low friction way to learn Bayesian | ||
| statistics and incorporate it into their research.</p> | ||
| </sec> | ||
| <sec id="statement-of-need"> | ||
| <title>Statement of need</title> | ||
| <p>Currently, Python users can choose between several packages that | ||
| provide simple-to-use functions for running classical/frequentist | ||
| statistical tests (e.g., | ||
| <ext-link ext-link-type="uri" xlink:href="https://pingouin-stats.org/build/html/index.html#">Pingouin</ext-link>, | ||
| <ext-link ext-link-type="uri" xlink:href="https://scipy.org/">SciPy</ext-link>, | ||
| <ext-link ext-link-type="uri" xlink:href="https://pandas.pydata.org/">pandas</ext-link>, | ||
| and | ||
| <ext-link ext-link-type="uri" xlink:href="https://www.statsmodels.org/stable/index.html">statsmodels</ext-link>). | ||
| In contrast, for Bayesian statistics there has only been the excellent | ||
| <ext-link ext-link-type="uri" xlink:href="https://bambinos.github.io/bambi/">Bambi</ext-link> | ||
| package, which, while quite powerful and robust, does require more | ||
| advanced knowledge and familiarity with | ||
| <ext-link ext-link-type="uri" xlink:href="https://cran.r-project.org/web/packages/brms/index.html">R-brms</ext-link> | ||
| syntax. Therefore, the goal of <monospace>bayes-toolbox</monospace> is | ||
| to fill an important gap in the Python-Bayesian community, by | ||
| providing an easy-to-use module for less experienced users that makes | ||
| it as simple, in Python, to fit a Bayesian model to data as it is to | ||
| run a frequentist statistical test. As all of the models (tests) are | ||
| executable with single functions, they are ideal for use in an open, | ||
| replicable workflow | ||
| (<xref alt="Wilson et al., 2017" rid="ref-wilson2017good" ref-type="bibr">Wilson | ||
| et al., 2017</xref>).</p> | ||
| <p><monospace>bayes-toolbox</monospace> is a Python package that makes | ||
| performing such Bayesian analyses simple and straight forward. By | ||
| leveraging PyMC, a probabilistic programming library written in Python | ||
| (<xref alt="Patil et al., 2010" rid="ref-patil2010pymc" ref-type="bibr">Patil | ||
| et al., 2010</xref>), and providing easy-to-use functions, | ||
| <monospace>bayes-toolbox</monospace> removes many of the technical | ||
| barriers previously associated with Bayesian analyses, especially for | ||
| users who would prefer to work with Python over other programming | ||
| languages (e.g., R). This package also removes the requirement to | ||
| include model formulas to perform statistical tests, another potential | ||
| barrier for end users. And as the <monospace>bayes-toolbox</monospace> | ||
| functions provide Bayesian analogues of many of the most common | ||
| classical tests used by scientists, including t-tests, ANOVAs, and | ||
| regression models, as well as hierarchical (multi-level) models and | ||
| meta-analyses, it provides a much needed bridge for researchers who | ||
| are familiar with frequentist statistics but wish to explore the | ||
| Bayesian framework.</p> | ||
| <p><monospace>bayes-toolbox</monospace> was designed for and targeted | ||
| to researchers primarily in the behavioral and neural sciences. | ||
| However, as many of the models and Jupyter notebook tutorials included | ||
| in the public <monospace>bayes-toolbox</monospace> repository are | ||
| adapted from the well-known textbook “Doing Bayesian Data Analysis” | ||
| (<xref alt="Kruschke, 2014" rid="ref-kruschke2014doing" ref-type="bibr">Kruschke, | ||
| 2014</xref>), <monospace>bayes-toolbox</monospace> can also serve as | ||
| an important pedagogical tool for students and researchers alike.</p> | ||
| </sec> | ||
| <sec id="acknowledgements"> | ||
| <title>Acknowledgements</title> | ||
| <p>Thank you to the PyMC developers, John Kruschke, and Jordi | ||
| Warmenhoven for generously sharing your work and knowledge.</p> | ||
| </sec> | ||
| </body> | ||
| <back> | ||
| <ref-list> | ||
| <ref id="ref-patil2010pymc"> | ||
| <element-citation publication-type="article-journal"> | ||
| <person-group person-group-type="author"> | ||
| <name><surname>Patil</surname><given-names>Anand</given-names></name> | ||
| <name><surname>Huard</surname><given-names>David</given-names></name> | ||
| <name><surname>Fonnesbeck</surname><given-names>Christopher J</given-names></name> | ||
| </person-group> | ||
| <article-title>PyMC: Bayesian Stochastic Modelling in Python</article-title> | ||
| <source>Journal of Statistical Software</source> | ||
| <publisher-name>Europe PMC Funders</publisher-name> | ||
| <year iso-8601-date="2010">2010</year> | ||
| <volume>35</volume> | ||
| <issue>4</issue> | ||
| <pub-id pub-id-type="doi">10.18637/jss.v035.i04</pub-id> | ||
| <fpage>1</fpage> | ||
| <lpage></lpage> | ||
| </element-citation> | ||
| </ref> | ||
| <ref id="ref-kruschke2014doing"> | ||
| <element-citation publication-type="book"> | ||
| <person-group person-group-type="author"> | ||
| <name><surname>Kruschke</surname><given-names>John</given-names></name> | ||
| </person-group> | ||
| <source>Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan</source> | ||
| <publisher-name>Academic Press</publisher-name> | ||
| <year iso-8601-date="2014">2014</year> | ||
| <pub-id pub-id-type="doi">10.1016/c2012-0-00477-2</pub-id> | ||
| </element-citation> | ||
| </ref> | ||
| <ref id="ref-wilson2017good"> | ||
| <element-citation publication-type="article-journal"> | ||
| <person-group person-group-type="author"> | ||
| <name><surname>Wilson</surname><given-names>Greg</given-names></name> | ||
| <name><surname>Bryan</surname><given-names>Jennifer</given-names></name> | ||
| <name><surname>Cranston</surname><given-names>Karen</given-names></name> | ||
| <name><surname>Kitzes</surname><given-names>Justin</given-names></name> | ||
| <name><surname>Nederbragt</surname><given-names>Lex</given-names></name> | ||
| <name><surname>Teal</surname><given-names>Tracy K</given-names></name> | ||
| </person-group> | ||
| <article-title>Good enough practices in scientific computing</article-title> | ||
| <source>PLoS computational biology</source> | ||
| <publisher-name>Public Library of Science</publisher-name> | ||
| <year iso-8601-date="2017">2017</year> | ||
| <volume>13</volume> | ||
| <issue>6</issue> | ||
| <pub-id pub-id-type="doi">10.1371/journal.pcbi.1005510</pub-id> | ||
| <fpage>e1005510</fpage> | ||
| <lpage></lpage> | ||
| </element-citation> | ||
| </ref> | ||
| </ref-list> | ||
| </back> | ||
| </article> |
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