Skip to content

Commit

Permalink
Merge pull request #4615 from openjournals/joss.05612
Browse files Browse the repository at this point in the history
Merging automatically
  • Loading branch information
editorialbot authored Sep 26, 2023
2 parents 9775d3d + f29432c commit 23c78bc
Show file tree
Hide file tree
Showing 3 changed files with 748 additions and 0 deletions.
230 changes: 230 additions & 0 deletions joss.05612/10.21105.joss.05612.crossref.xml
Original file line number Diff line number Diff line change
@@ -0,0 +1,230 @@
<?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>20230926T103117-00ea19afc9996f096835500cbd3a6d23a1d37b3a</doi_batch_id>
<timestamp>20230926103117</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>09</month>
<year>2023</year>
</publication_date>
<journal_volume>
<volume>8</volume>
</journal_volume>
<issue>89</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>Bernadette: Bayesian Inference and Model Selection for
Stochastic Epidemics in R</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Lampros</given_name>
<surname>Bouranis</surname>
<ORCID>https://orcid.org/0000-0002-1291-2192</ORCID>
</person_name>
</contributors>
<publication_date>
<month>09</month>
<day>26</day>
<year>2023</year>
</publication_date>
<pages>
<first_page>5612</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.05612</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.8376673</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/5612</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.05612</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.05612</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.05612.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="bouranis">
<article_title>Bayesian analysis of diffusion-driven
multi-type epidemic models with application to COVID-19</article_title>
<author>Bouranis</author>
<doi>10.48550/arXiv.2211.15229</doi>
<cYear>2022</cYear>
<unstructured_citation>Bouranis, L., Demiris, N.,
Kalogeropoulos, K., &amp; Ntzoufras, I. (2022). Bayesian analysis of
diffusion-driven multi-type epidemic models with application to
COVID-19. arXiv.
https://doi.org/10.48550/arXiv.2211.15229</unstructured_citation>
</citation>
<citation key="bernadette">
<volume_title>Bernadette: Bayesian inference and model
selection for stochastic epidemics</volume_title>
<author>Bouranis</author>
<cYear>2023</cYear>
<unstructured_citation>Bouranis, L. (2023). Bernadette:
Bayesian inference and model selection for stochastic epidemics.
https://CRAN.R-project.org/package=Bernadette</unstructured_citation>
</citation>
<citation key="epidemia">
<article_title>epidemia: Modeling of epidemics using
hierarchical Bayesian models</article_title>
<author>Scott</author>
<cYear>2020</cYear>
<unstructured_citation>Scott, J., Gandy, A., Mishra, S.,
Unwin, J., Flaxman, S., &amp; Bhatt, S. (2020). epidemia: Modeling of
epidemics using hierarchical Bayesian models.
https://imperialcollegelondon.github.io/epidemia/</unstructured_citation>
</citation>
<citation key="rsoft">
<volume_title>R: A language and environment for statistical
computing</volume_title>
<author>R Core Team</author>
<cYear>2023</cYear>
<unstructured_citation>R Core Team. (2023). R: A language
and environment for statistical computing. R Foundation for Statistical
Computing. https://www.R-project.org/</unstructured_citation>
</citation>
<citation key="rstan">
<article_title>RStan: The R interface to
Stan</article_title>
<author>Stan Development Team</author>
<cYear>2023</cYear>
<unstructured_citation>Stan Development Team. (2023). RStan:
The R interface to Stan. https://mc-stan.org/</unstructured_citation>
</citation>
<citation key="carpenter2017stan">
<article_title>Stan: A probabilistic programming
language</article_title>
<author>Carpenter</author>
<journal_title>Journal of statistical
software</journal_title>
<issue>1</issue>
<volume>76</volume>
<doi>10.18637/jss.v076.i01</doi>
<cYear>2017</cYear>
<unstructured_citation>Carpenter, B., Gelman, A., Hoffman,
M., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li,
P., &amp; Riddell, A. (2017). Stan: A probabilistic programming
language. Journal of Statistical Software, 76(1), 1–32.
https://doi.org/10.18637/jss.v076.i01</unstructured_citation>
</citation>
<citation key="epiestim">
<volume_title>EpiEstim: Estimate time varying reproduction
numbers from epidemic curves</volume_title>
<author>Cori</author>
<cYear>2021</cYear>
<unstructured_citation>Cori, A. (2021). EpiEstim: Estimate
time varying reproduction numbers from epidemic curves.
https://CRAN.R-project.org/package=EpiEstim</unstructured_citation>
</citation>
<citation key="Cori2013">
<article_title>A new framework and software to estimate
time-varying reproduction numbers during epidemics</article_title>
<author>Cori</author>
<journal_title>American Journal of
Epidemiology</journal_title>
<issue>9</issue>
<volume>178</volume>
<doi>10.1093/aje/kwt133</doi>
<cYear>2013</cYear>
<unstructured_citation>Cori, A., Ferguson, N., Fraser, C.,
&amp; Cauchemez, S. (2013). A new framework and software to estimate
time-varying reproduction numbers during epidemics. American Journal of
Epidemiology, 178(9), 1505–1512.
https://doi.org/10.1093/aje/kwt133</unstructured_citation>
</citation>
<citation key="gostic">
<article_title>Practical considerations for measuring the
effective reproductive number, Rt</article_title>
<author>Gostic</author>
<journal_title>PLoS Computational Biology</journal_title>
<issue>12</issue>
<volume>16</volume>
<doi>10.1371/journal.pcbi.1008409</doi>
<cYear>2020</cYear>
<unstructured_citation>Gostic, K., McGough, L., Baskerville,
E., Abbott, S., Joshi, K., Tedijanto, C., Kahn, R., Niehus, R., Hay, J.,
De Salazar, P., Hellewell, J., Meakin, S., Munday, J., Bosse, N.,
Sherrat, K. e., Thompson, R., White, L., Huisman, J., Scire, J., …
Cobey, S. (2020). Practical considerations for measuring the effective
reproductive number, Rt. PLoS Computational Biology, 16(12), 1–21.
https://doi.org/10.1371/journal.pcbi.1008409</unstructured_citation>
</citation>
<citation key="brooks">
<volume_title>Handbook of Markov chain Monte
Carlo</volume_title>
<author>Brooks</author>
<cYear>2011</cYear>
<unstructured_citation>Brooks, S., Gelman, A., Jones, G.,
&amp; Meng, X. (2011). Handbook of Markov chain Monte Carlo. CRC
press.</unstructured_citation>
</citation>
<citation key="psisloo2">
<article_title>loo: Efficient leave-one-out cross-validation
and WAIC for Bayesian models</article_title>
<author>Vehtari</author>
<cYear>2023</cYear>
<unstructured_citation>Vehtari, A., Gabry, J., Magnusson,
M., Yao, Y., Bürkner, P., Paananen, T., &amp; Gelman, A. (2023). loo:
Efficient leave-one-out cross-validation and WAIC for Bayesian models.
https://mc-stan.org/loo/</unstructured_citation>
</citation>
<citation key="ward_react2">
<article_title>SARS-CoV-2 antibody prevalence in England
following the first peak of the pandemic</article_title>
<author>Ward</author>
<journal_title>Nature Communications</journal_title>
<volume>12</volume>
<doi>10.1038/s41467-021-21237-w</doi>
<cYear>2021</cYear>
<unstructured_citation>Ward, H., Atchison, C., Whitaker, M.,
Ainslie, K., Elliott, J., Okell, L., Redd, R., Ashby, D., Donnelly, C.,
Barclay, W., Darzi, A., Cooke, G., Riley, S., &amp; Elliott, P. (2021).
SARS-CoV-2 antibody prevalence in England following the first peak of
the pandemic. Nature Communications, 12, 905.
https://doi.org/10.1038/s41467-021-21237-w</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Loading

0 comments on commit 23c78bc

Please sign in to comment.