pyAnno 2.0 with 2to3 applied
This copy is made thanks to the BSD license of pyanno
pyAnno 2.0
pyAnno 2.0 is a Python library for the analysis and diagnostic testing of annotation and curation efforts. pyAnno implements statistical models for inferring from categorical data annotated by multiple annotators
annotator accuracies and biases,
gold standard categories of items,
prevalence of categories in population, and
population distribution of annotator accuracies and biases.
The models include a generalization of Dawid and Skene’s (1979) multinomial model with Dirichlet priors on prevalence and estimator accuracy, and the two models introduces in Rzhetsky et al.’s (2009). The implementation allows Maximum Likelihood and Maximum A Posteriori estimation of parameters, and to draw samples from the full posterior distribution over annotator accuracy.
LICENSING
pyAnno is licensed under a modified BSD license (2-clause). For more information, see
LICENSE.txt
DOCUMENTATION
The documentation is hosted at http://docs.enthought.com/uchicago-pyanno/ .
CONTRIBUTORS
Pietro Berkes (Enthought, Ltd.)
Bob Carpenter (Columbia University, Statistics)
Andrey Rzhetsky (University of Chicago, Medicine)
James Evans (University of Chicago, Sociology)
Release files for pyanno3 2.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pyanno3-2.0.2.tar.gz | 76.8 kB | Details |
Release files / pyanno3-2.0.2.tar.gz
| Download URL | pyanno3-2.0.2.tar.gz |
|---|---|
| Size | 76.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
cc34bda34b9ce96f5ceff17fce624cbf10246b0df32ed0ca0871be577f03a77d
|
|
BLAKE2b-256 checksum How to use checksums |
c51aee2b136ea0283adb2a9302c29594127f84b6e34cb0b02b91c63bed0a534b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |