Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Creating pull request for 10.21105.joss.06321 #5189

Merged
merged 4 commits into from
Mar 27, 2024
Merged

Creating pull request for 10.21105.joss.06321 #5189

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
9f14cba
Creating 10.21105.joss.06321.pdf
editorialbot Mar 27, 2024
5f73751
Creating 10.21105.joss.06321.crossref.xml
editorialbot Mar 27, 2024
da1a5f3
Creating 10.21105.joss.06321.jats
editorialbot Mar 27, 2024
5091399
Adding media file: software.pdf
editorialbot Mar 27, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
283 changes: 283 additions & 0 deletions joss.06321/10.21105.joss.06321.crossref.xml
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,283 @@
<?xml version="1.0" encoding="UTF-8"?>
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1"
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd"
xmlns:rel="http://www.crossref.org/relations.xsd"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
version="5.3.1"
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd">
<head>
<doi_batch_id>20240327T234716-53783c8ea0ec973b06e30e6b878e3b11d4af281c</doi_batch_id>
<timestamp>20240327234716</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>admin@theoj.org</email_address>
</depositor>
<registrant>The Open Journal</registrant>
</head>
<body>
<journal>
<journal_metadata>
<full_title>Journal of Open Source Software</full_title>
<abbrev_title>JOSS</abbrev_title>
<issn media_type="electronic">2475-9066</issn>
<doi_data>
<doi>10.21105/joss</doi>
<resource>https://joss.theoj.org</resource>
</doi_data>
</journal_metadata>
<journal_issue>
<publication_date media_type="online">
<month>03</month>
<year>2024</year>
</publication_date>
<journal_volume>
<volume>9</volume>
</journal_volume>
<issue>95</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>TDApplied: An R package for machine learning and
inference with persistence diagrams</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Shael</given_name>
<surname>Brown</surname>
<ORCID>https://orcid.org/0000-0001-8868-2867</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Reza</given_name>
<surname>Farivar-Mohseni</surname>
<ORCID>https://orcid.org/0000-0002-3123-2627</ORCID>
</person_name>
</contributors>
<publication_date>
<month>03</month>
<day>27</day>
<year>2024</year>
</publication_date>
<pages>
<first_page>6321</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.06321</identifier>
</publisher_item>
<ai:program name="AccessIndicators">
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
</ai:program>
<rel:program>
<rel:related_item>
<rel:description>Software archive</rel:description>
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.10814141</rel:inter_work_relation>
</rel:related_item>
<rel:related_item>
<rel:description>GitHub review issue</rel:description>
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/6321</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.06321</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.06321</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.06321.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="R-TDA">
<volume_title>TDA: Statistical tools for topological data
analysis</volume_title>
<author>Fasy</author>
<cYear>2021</cYear>
<unstructured_citation>Fasy, B. T., Kim, J., Lecci, F.,
Maria, C., Millman, D. L., &amp; Rouvreau., V. (2021). TDA: Statistical
tools for topological data analysis.
https://CRAN.R-project.org/package=TDA</unstructured_citation>
</citation>
<citation key="R-TDAstats">
<volume_title>TDAstats: Pipeline for topological data
analysis</volume_title>
<author>Wadhwa</author>
<cYear>2019</cYear>
<unstructured_citation>Wadhwa, R., Dhawan, A., Williamson,
D., &amp; Scott, J. (2019). TDAstats: Pipeline for topological data
analysis. https://github.com/rrrlw/TDAstats</unstructured_citation>
</citation>
<citation key="TDAstats2018">
<article_title>TDAstats: R pipeline for computing persistent
homology in topological data analysis</article_title>
<author>Wadhwa</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>28</issue>
<volume>3</volume>
<doi>10.21105/joss.00860</doi>
<cYear>2018</cYear>
<unstructured_citation>Wadhwa, R. R., Williamson, D. F. K.,
Dhawan, A., &amp; Scott, J. G. (2018). TDAstats: R pipeline for
computing persistent homology in topological data analysis. Journal of
Open Source Software, 3(28), 860.
https://doi.org/10.21105/joss.00860</unstructured_citation>
</citation>
<citation key="PHoriginal">
<article_title>Topological persistence and
simplification</article_title>
<author>Edelsbrunner</author>
<journal_title>Discrete &amp; Computational
Geometry</journal_title>
<volume>28</volume>
<doi>10.1007/s00454-002-2885-2</doi>
<cYear>2000</cYear>
<unstructured_citation>Edelsbrunner, H., Letscher, D., &amp;
Zomorodian, A. (2000). Topological persistence and simplification.
Discrete &amp; Computational Geometry, 28, 511–533.
https://doi.org/10.1007/s00454-002-2885-2</unstructured_citation>
</citation>
<citation key="ComputingPH">
<article_title>Computing persistent homology</article_title>
<author>Zomorodian</author>
<journal_title>Discrete and Computational
Geometry</journal_title>
<volume>33</volume>
<doi>10.1007/s00454-004-1146-y</doi>
<cYear>2005</cYear>
<unstructured_citation>Zomorodian, A., &amp; Carlsson, G.
(2005). Computing persistent homology. Discrete and Computational
Geometry, 33, 249–274.
https://doi.org/10.1007/s00454-004-1146-y</unstructured_citation>
</citation>
<citation key="R-devtools">
<volume_title>devtools: Tools to make developing R packages
easier</volume_title>
<author>Wickham</author>
<cYear>2021</cYear>
<unstructured_citation>Wickham, H., Hester, J., Chang, W.,
&amp; Bryan, J. (2021). devtools: Tools to make developing R packages
easier.
https://CRAN.R-project.org/package=devtools</unstructured_citation>
</citation>
<citation key="Robinson_Turner">
<article_title>Hypothesis testing for topological data
analysis</article_title>
<author>Robinson</author>
<journal_title>Journal of Applied and Computational
Topology</journal_title>
<volume>1</volume>
<doi>10.1007/s41468-017-0008-7</doi>
<cYear>2017</cYear>
<unstructured_citation>Robinson, A., &amp; Turner, K.
(2017). Hypothesis testing for topological data analysis. Journal of
Applied and Computational Topology, 1.
https://doi.org/10.1007/s41468-017-0008-7</unstructured_citation>
</citation>
<citation key="persistence_fisher">
<article_title>Persistence Fisher kernel: A Riemannian
manifold kernel for persistence diagrams</article_title>
<author>Le</author>
<journal_title>Advances in neural information processing
systems</journal_title>
<volume>31</volume>
<doi>10.48550/arXiv.1802.03569</doi>
<cYear>2018</cYear>
<unstructured_citation>Le, T., &amp; Yamada, M. (2018).
Persistence Fisher kernel: A Riemannian manifold kernel for persistence
diagrams. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N.
Cesa-Bianchi, &amp; R. Garnett (Eds.), Advances in neural information
processing systems (Vol. 31). Curran Associates, Inc.
https://doi.org/10.48550/arXiv.1802.03569</unstructured_citation>
</citation>
<citation key="TDA_ADHD">
<article_title>Topological data analysis reveals robust
alterations in the whole-brain and frontal lobe functional connectomes
in attention-deficit/hyperactivity disorder</article_title>
<author>Gracia-Tabuenca</author>
<journal_title>eneuro</journal_title>
<doi>10.1523/eneuro.0543-19.2020</doi>
<cYear>2020</cYear>
<unstructured_citation>Gracia-Tabuenca, Z., Diaz-Patino, J.
C., Arelio, I., &amp; Alcauter, S. (2020). Topological data analysis
reveals robust alterations in the whole-brain and frontal lobe
functional connectomes in attention-deficit/hyperactivity disorder.
Eneuro.
https://doi.org/10.1523/eneuro.0543-19.2020</unstructured_citation>
</citation>
<citation key="R-testthat">
<article_title>testthat: Get started with
testing</article_title>
<author>Wickham</author>
<journal_title>The R Journal</journal_title>
<volume>3</volume>
<doi>10.32614/rj-2011-002</doi>
<cYear>2011</cYear>
<unstructured_citation>Wickham, H. (2011). testthat: Get
started with testing. The R Journal, 3, 5–10.
https://doi.org/10.32614/rj-2011-002</unstructured_citation>
</citation>
<citation key="TDA_chemistry">
<article_title>Machine learning with persistent homology and
chemical word embeddings improves prediction accuracy and
interpretability in metal-organic frameworks</article_title>
<author>Krishnapriyan</author>
<journal_title>Nature Scientific Report</journal_title>
<volume>11</volume>
<doi>10.1038/s41598-021-88027-8</doi>
<cYear>2021</cYear>
<unstructured_citation>Krishnapriyan, A. S. et al. (2021).
Machine learning with persistent homology and chemical word embeddings
improves prediction accuracy and interpretability in metal-organic
frameworks. Nature Scientific Report, 11.
https://doi.org/10.1038/s41598-021-88027-8</unstructured_citation>
</citation>
<citation key="word_embeddings">
<article_title>Unsupervised geometric and topological
approaches for cross-lingual sentence representation and
comparison</article_title>
<author>Haim Meirom</author>
<journal_title>Proceedings of the 7th workshop on
representation learning for NLP</journal_title>
<doi>10.18653/v1/2022.repl4nlp-1.18</doi>
<cYear>2022</cYear>
<unstructured_citation>Haim Meirom, S., &amp; Bobrowski, O.
(2022). Unsupervised geometric and topological approaches for
cross-lingual sentence representation and comparison. Proceedings of the
7th Workshop on Representation Learning for NLP, 173–183.
https://doi.org/10.18653/v1/2022.repl4nlp-1.18</unstructured_citation>
</citation>
<citation key="Yash">
<article_title>Topological data analysis in medical imaging:
Current state of the art</article_title>
<author>Singh</author>
<journal_title>Insights into Imaging</journal_title>
<issue>1</issue>
<volume>14</volume>
<doi>10.1186/s13244-023-01413-w</doi>
<cYear>2023</cYear>
<unstructured_citation>Singh, Y., Farrelly, C. M., Hathaway,
Q. A., Leiner, T., Jagtap, J., Carlsson, G. E., &amp; Erickson, B. J.
(2023). Topological data analysis in medical imaging: Current state of
the art. Insights into Imaging, 14(1), 58.
https://doi.org/10.1186/s13244-023-01413-w</unstructured_citation>
</citation>
<citation key="Cox2008">
<article_title>Multidimensional scaling</article_title>
<author>Cox</author>
<journal_title>Handbook of data
visualization</journal_title>
<doi>10.1007/978-3-540-33037-0_14</doi>
<isbn>978-3-540-33037-0</isbn>
<cYear>2008</cYear>
<unstructured_citation>Cox, M. A. A., &amp; Cox, T. F.
(2008). Multidimensional scaling. In Handbook of data visualization (pp.
315–347). Springer Berlin Heidelberg.
https://doi.org/10.1007/978-3-540-33037-0_14</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Loading