diff --git a/joss.03115/10.21105.joss.03115.crossref.xml b/joss.03115/10.21105.joss.03115.crossref.xml new file mode 100644 index 0000000000..bdbbf75174 --- /dev/null +++ b/joss.03115/10.21105.joss.03115.crossref.xml @@ -0,0 +1,132 @@ + + + + 8ef29d2ab0e853bdb190e4d3b4a6985e + 20210513135812 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 05 + 2021 + + + 6 + + 61 + + + + PyGModels: A Python package for exploring Probabilistic Graphical Models with Graph Theoretical Structures + + + + Doğu + Eraslan + http://orcid.org/0000-0002-1552-8938 + + + + 05 + 13 + 2021 + + + 3115 + + + 10.21105/joss.03115 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + “https://doi.org/10.5281/zenodo.4751740” + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/3115 + + + + 10.21105/joss.03115 + https://joss.theoj.org/papers/10.21105/joss.03115 + + + https://joss.theoj.org/papers/10.21105/joss.03115.pdf + + + + + + 10.25080/Majora-7b98e3ed-001 + + + Mastering probabilistic graphical models using python: master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python, 978-1-78439-468-4, Ankan, Ankur and Panda, Abinash, 2015 + + + New York, Probabilistic networks and expert systems, http://accesbib.uqam.ca/cgi-bin/bduqam/transit.pl?&noMan=25126878, Springer-Verlag, Cowell, Robert G, 2005 + + + 10.2200/S00893ED2V01Y201901AIM041 + + + Hamburg, 5, Graph Theory, 978-3-662-53621-6, Springer, Diestel, Reinhard, 2017 + + + 10.1007/978-3-319-73235-0 + + + Cambridge, NY, 2nd ed, Graph algorithms, 978-0-521-51718-8, Cambridge University Press, Even, Shimon and Even, Guy, 2012 + + + pyGM, https://github.com/ihler/pyGM, Ihler, Alexander, 2020, oct, 10 + + + pgmPy, https://github.com/indapa/pgmPy, Indap, Amit, 2013, aug, 8 + + + Cambridge, MA, Adaptive computation and machine learning, Probabilistic graphical models: principles and techniques, 978-0-262-01319-2, MIT Press, Koller, Daphne and Friedman, Nir, 2009, Adaptive computation and machine learning + + + Oxford : New York, Oxford statistical science series, Graphical models, 978-0-19-852219-5, Clarendon Press ; Oxford University Press, Lauritzen, Steffen L., 1996, Oxford statistical science series + + + 10.1111/1467-9868.00340 + + + pyfac, https://github.com/rdlester/pyfac, Lester, Ryan, 2016, may, 5 + + + pgm, https://github.com/paulorauber/pgm, Rauber, Paulo, 2019, mar, 3 + + + London Heidelberg New York Dordrecht, Advances in computer vision and pattern recognition, Probabilistic graphical models: principles and applications, 978-1-4471-6698-6, Springer, Sucar, Luis Enrique, 2015, Advances in computer vision and pattern recognition + + + Pomegranate: fast and flexible probabilistic modeling in python, http://arxiv.org/abs/1711.00137, arXiv: 1711.00137, arXiv:1711.00137 [cs, stat], Schreiber, Jacob, 2018, feb, 2 + + + + + +