Skip to content

Commit

Permalink
Merge pull request #5947 from openjournals/joss.06906
Browse files Browse the repository at this point in the history 
Merging automatically
  • Loading branch information
@editorialbot
editorialbot authored Oct 1, 2024
2 parents 99d45c2 + 18abf94 commit bf7abc0
Show file tree
Hide file tree
Showing 3 changed files with 847 additions and 0 deletions.
239 changes: 239 additions & 0 deletions joss.06906/10.21105.joss.06906.crossref.xml
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,239 @@
<?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>20241001132901-6aa88d7ad332c2ef6cf688208591145122365b40</doi_batch_id>
<timestamp>20241001132901</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>[email protected]</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>10</month>
<year>2024</year>
</publication_date>
<journal_volume>
<volume>9</volume>
</journal_volume>
<issue>102</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>lintsampler: Easy random sampling via linear
interpolation</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Aneesh P.</given_name>
<surname>Naik</surname>
<ORCID>https://orcid.org/0000-0001-6841-1496</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Michael S.</given_name>
<surname>Petersen</surname>
<ORCID>https://orcid.org/0000-0003-1517-3935</ORCID>
</person_name>
</contributors>
<publication_date>
<month>10</month>
<day>01</day>
<year>2024</year>
</publication_date>
<pages>
<first_page>6906</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.06906</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.13846862</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/6906</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.06906</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.06906</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.06906.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="devroye.book">
<volume_title>Non-uniform random variate
generation</volume_title>
<author>Devroye</author>
<doi>10.1007/978-1-4613-8643-8</doi>
<cYear>1986</cYear>
<unstructured_citation>Devroye, L. (1986). Non-uniform
random variate generation. Springer-Verlag.
https://doi.org/10.1007/978-1-4613-8643-8</unstructured_citation>
</citation>
<citation key="nfw.paper">
<article_title>A Universal Density Profile from Hierarchical
Clustering</article_title>
<author>Navarro</author>
<issue>2</issue>
<volume>490</volume>
<doi>10.1086/304888</doi>
<cYear>1997</cYear>
<unstructured_citation>Navarro, J. F., Frenk, C. S., &amp;
White, S. D. M. (1997). A Universal Density Profile from Hierarchical
Clustering. 490(2), 493–508.
https://doi.org/10.1086/304888</unstructured_citation>
</citation>
<citation key="emcee.paper">
<article_title>emcee v3: A Python ensemble sampling toolkit
for affine-invariant MCMC</article_title>
<author>Foreman-Mackey</author>
<journal_title>The Journal of Open Source
Software</journal_title>
<issue>43</issue>
<volume>4</volume>
<doi>10.21105/joss.01864</doi>
<cYear>2019</cYear>
<unstructured_citation>Foreman-Mackey, D., Farr, W., Sinha,
M., Archibald, A., Hogg, D., Sanders, J., Zuntz, J., Williams, P.,
Nelson, A., de Val-Borro, M., Erhardt, T., Pashchenko, I., &amp; Pla, O.
(2019). emcee v3: A Python ensemble sampling toolkit for
affine-invariant MCMC. The Journal of Open Source Software, 4(43), 1864.
https://doi.org/10.21105/joss.01864</unstructured_citation>
</citation>
<citation key="pymc.paper">
<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., &amp;
Fonnesbeck, C. J. (2010). PyMC: Bayesian Stochastic Modelling in Python.
Journal of Statistical Software, 35(4), 1–81.
https://doi.org/10.18637/jss.v035.i04</unstructured_citation>
</citation>
<citation key="pxmcmc.paper">
<article_title>PxMCMC: A Python package for proximal Markov
Chain Monte Carlo</article_title>
<author>Marignier</author>
<journal_title>The Journal of Open Source
Software</journal_title>
<issue>87</issue>
<volume>8</volume>
<doi>10.21105/joss.05582</doi>
<cYear>2023</cYear>
<unstructured_citation>Marignier, A. (2023). PxMCMC: A
Python package for proximal Markov Chain Monte Carlo. The Journal of
Open Source Software, 8(87), 5582.
https://doi.org/10.21105/joss.05582</unstructured_citation>
</citation>
<citation key="sgmcmcjax.paper">
<article_title>SGMCMCJax: A lightweight JAX library for
stochastic gradient Markov Chain Monte Carlo algorithms</article_title>
<author>Coullon</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>72</issue>
<volume>7</volume>
<doi>10.21105/joss.04113</doi>
<cYear>2022</cYear>
<unstructured_citation>Coullon, J., &amp; Nemeth, C. (2022).
SGMCMCJax: A lightweight JAX library for stochastic gradient Markov
Chain Monte Carlo algorithms. Journal of Open Source Software, 7(72),
4113. https://doi.org/10.21105/joss.04113</unstructured_citation>
</citation>
<citation key="scipy.paper">
<article_title>SciPy 1.0: Fundamental Algorithms for
Scientific Computing in Python</article_title>
<author>Virtanen</author>
<journal_title>Nature Methods</journal_title>
<volume>17</volume>
<doi>10.1038/s41592-019-0686-2</doi>
<cYear>2020</cYear>
<unstructured_citation>Virtanen, P., Gommers, R., Oliphant,
T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson,
P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson,
J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R.,
Larson, E., … SciPy 1.0 Contributors. (2020). SciPy 1.0: Fundamental
Algorithms for Scientific Computing in Python. Nature Methods, 17,
261–272.
https://doi.org/10.1038/s41592-019-0686-2</unstructured_citation>
</citation>
<citation key="numpy.paper">
<article_title>Array programming with NumPy</article_title>
<author>Harris</author>
<journal_title>Nature</journal_title>
<issue>7825</issue>
<volume>585</volume>
<doi>10.1038/s41586-020-2649-2</doi>
<cYear>2020</cYear>
<unstructured_citation>Harris, C. R., Millman, K. J., Walt,
S. J. van der, Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E.,
Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S.,
Kerkwijk, M. H. van, Brett, M., Haldane, A., Río, J. F. del, Wiebe, M.,
Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy.
Nature, 585(7825), 357–362.
https://doi.org/10.1038/s41586-020-2649-2</unstructured_citation>
</citation>
<citation key="agama.paper">
<article_title>AGAMA: action-based galaxy modelling
architecture</article_title>
<author>Vasiliev</author>
<issue>2</issue>
<volume>482</volume>
<doi>10.1093/mnras/sty2672</doi>
<cYear>2019</cYear>
<unstructured_citation>Vasiliev, E. (2019). AGAMA:
action-based galaxy modelling architecture. 482(2), 1525–1544.
https://doi.org/10.1093/mnras/sty2672</unstructured_citation>
</citation>
<citation key="exp.paper">
<article_title>EXP: N-body integration using basis function
expansions</article_title>
<author>Petersen</author>
<issue>4</issue>
<volume>510</volume>
<doi>10.1093/mnras/stab3639</doi>
<cYear>2022</cYear>
<unstructured_citation>Petersen, M. S., Weinberg, M. D.,
&amp; Katz, N. (2022). EXP: N-body integration using basis function
expansions. 510(4), 6201–6217.
https://doi.org/10.1093/mnras/stab3639</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Binary file added joss.06906/10.21105.joss.06906.pdf
View file
Binary file not shown.
Loading

0 comments on commit bf7abc0

Please sign in to comment.