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PyMC: a modern, and comprehensive probabilistic programming framework in Python

Overview of attention for article published in PeerJ Computer Science, September 2023
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#47 of 1,242)
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

twitter
37 X users
wikipedia
2 Wikipedia pages

Readers on

mendeley
64 Mendeley
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Title
PyMC: a modern, and comprehensive probabilistic programming framework in Python
Published in
PeerJ Computer Science, September 2023
DOI 10.7717/peerj-cs.1516
Pubmed ID
Authors

Oriol Abril-Pla, Virgile Andreani, Colin Carroll, Larry Dong, Christopher J. Fonnesbeck, Maxim Kochurov, Ravin Kumar, Junpeng Lao, Christian C. Luhmann, Osvaldo A. Martin, Michael Osthege, Ricardo Vieira, Thomas Wiecki, Robert Zinkov

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 37 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 20%
Student > Ph. D. Student 12 19%
Unspecified 3 5%
Student > Doctoral Student 2 3%
Student > Bachelor 2 3%
Other 8 13%
Unknown 24 38%
Readers by discipline Count As %
Engineering 9 14%
Biochemistry, Genetics and Molecular Biology 3 5%
Psychology 3 5%
Unspecified 3 5%
Earth and Planetary Sciences 3 5%
Other 18 28%
Unknown 25 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 July 2024.
All research outputs
#1,662,603
of 26,381,177 outputs
Outputs from PeerJ Computer Science
#47
of 1,242 outputs
Outputs of similar age
#28,915
of 363,857 outputs
Outputs of similar age from PeerJ Computer Science
#1
of 90 outputs
Altmetric has tracked 26,381,177 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,242 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.7. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 363,857 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 90 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.