Skip to content

Commit

Permalink
Merge pull request #3559 from openjournals/joss.04705
Browse files Browse the repository at this point in the history
Merging automatically
  • Loading branch information
editorialbot authored Sep 28, 2022
2 parents 01318b1 + 4ede6ad commit 679a2e6
Show file tree
Hide file tree
Showing 4 changed files with 649 additions and 0 deletions.
208 changes: 208 additions & 0 deletions joss.04705/10.21105.joss.04705.crossref.xml
Original file line number Diff line number Diff line change
@@ -0,0 +1,208 @@
<?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>20220928T103734-42385c1a905e0f07d990fe30e4263ce5597fa585</doi_batch_id>
<timestamp>20220928103734</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>09</month>
<year>2022</year>
</publication_date>
<journal_volume>
<volume>7</volume>
</journal_volume>
<issue>77</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>aorsf: An R package for supervised learning using the
oblique random survival forest</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Byron C.</given_name>
<surname>Jaeger</surname>
<ORCID>https://orcid.org/0000-0001-7399-2299</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Sawyer</given_name>
<surname>Welden</surname>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Kristin</given_name>
<surname>Lenoir</surname>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Nicholas M</given_name>
<surname>Pajewski</surname>
</person_name>
</contributors>
<publication_date>
<month>09</month>
<day>28</day>
<year>2022</year>
</publication_date>
<pages>
<first_page>4705</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.04705</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.7116855</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/4705</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.04705</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.04705</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.04705.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="ishwaran2008random">
<article_title>Random survival forests</article_title>
<author>Ishwaran</author>
<journal_title>The Annals of Applied
Statistics</journal_title>
<issue>3</issue>
<volume>2</volume>
<doi>10.1214/08-AOAS169</doi>
<cYear>2008</cYear>
<unstructured_citation>Ishwaran, H., Kogalur, U. B.,
Blackstone, E. H., &amp; Lauer, M. S. (2008). Random survival forests.
The Annals of Applied Statistics, 2(3), 841–860.
doi:10.1214/08-AOAS169</unstructured_citation>
</citation>
<citation key="therneau_survival_2022">
<article_title>Survival package source code
documentation</article_title>
<author>Therneau</author>
<cYear>2022</cYear>
<unstructured_citation>Therneau, T. (2022, April). Survival
package source code documentation. Retrieved from
https://github.com/therneau/survival/blob/5440691d44abea537b08aeb60153a31654d66a9b/noweb</unstructured_citation>
</citation>
<citation key="aorsf_arxiv">
<article_title>Accelerated and interpretable oblique random
survival forests</article_title>
<author>Jaeger</author>
<doi>10.48550/ARXIV.2208.01129</doi>
<cYear>2022</cYear>
<unstructured_citation>Jaeger, B., Welden, S., Lenoir, K.,
Speiser, J. L., Segar, M. W., Pandey, A., &amp; Pajewski, N. M. (2022).
Accelerated and interpretable oblique random survival forests. arXiv.
doi:10.48550/ARXIV.2208.01129</unstructured_citation>
</citation>
<citation key="breiman2001random">
<article_title>Random forests</article_title>
<author>Breiman</author>
<journal_title>Machine Learning</journal_title>
<issue>1</issue>
<volume>45</volume>
<doi>10.1023/A:1010933404324</doi>
<cYear>2001</cYear>
<unstructured_citation>Breiman, L. (2001). Random forests.
Machine Learning, 45(1), 5–32.
doi:10.1023/A:1010933404324</unstructured_citation>
</citation>
<citation key="menze2011oblique">
<article_title>On oblique random forests</article_title>
<author>Menze</author>
<journal_title>Joint european conference on machine learning
and knowledge discovery in databases</journal_title>
<cYear>2011</cYear>
<unstructured_citation>Menze, B. H., Kelm, B. M.,
Splitthoff, D. N., Koethe, U., &amp; Hamprecht, F. A. (2011). On oblique
random forests. Joint european conference on machine learning and
knowledge discovery in databases (pp. 453–469).
Springer.</unstructured_citation>
</citation>
<citation key="jaeger2019oblique">
<article_title>Oblique random survival
forests</article_title>
<author>Jaeger</author>
<journal_title>The Annals of Applied
Statistics</journal_title>
<issue>3</issue>
<volume>13</volume>
<doi>10.1214/19-AOAS1261</doi>
<cYear>2019</cYear>
<unstructured_citation>Jaeger, B., Long, D. L., Long, D. M.,
Sims, M., Szychowski, J. M., Min, Y.-I., Mcclure, L. A., et al. (2019).
Oblique random survival forests. The Annals of Applied Statistics,
13(3), 1847–1883. doi:10.1214/19-AOAS1261</unstructured_citation>
</citation>
<citation key="segar2021development">
<article_title>Development and validation of machine
learning–based race-specific models to predict 10-year risk of heart
failure: A multicohort analysis</article_title>
<author>Segar</author>
<journal_title>Circulation</journal_title>
<issue>24</issue>
<volume>143</volume>
<doi>10.1161/circulationaha.120.053134</doi>
<cYear>2021</cYear>
<unstructured_citation>Segar, M. W., Jaeger, B. C., Patel,
K. V., Nambi, V., Ndumele, C. E., Correa, A., Butler, J., et al. (2021).
Development and validation of machine learning–based race-specific
models to predict 10-year risk of heart failure: A multicohort analysis.
Circulation, 143(24), 2370–2383.
doi:10.1161/circulationaha.120.053134</unstructured_citation>
</citation>
<citation key="katuwal2020heterogeneous">
<article_title>Heterogeneous oblique random
forest</article_title>
<author>Katuwal</author>
<journal_title>Pattern Recognition</journal_title>
<volume>99</volume>
<doi>10.1016/j.patcog.2019.107078</doi>
<cYear>2020</cYear>
<unstructured_citation>Katuwal, R., Suganthan, P. N., &amp;
Zhang, L. (2020). Heterogeneous oblique random forest. Pattern
Recognition, 99, 107078.
doi:10.1016/j.patcog.2019.107078</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Loading

0 comments on commit 679a2e6

Please sign in to comment.