MA-LR: Multiple-annotator Logistic Regression
NOTE: Please take a look at the more recent, more complete, cleaner and faster Julia implementations of these methods (and others) for learning from multiple annotators and crowds available here.
MA-LR is a Python implementation of the multiple-annotator logistic regression model proposed in:
Furthermore, it provides an implementation of the multi-class extension of the model proposed in:
 Raykar, V., Yu, S., Zhao, L., Jerebko, A., Florin, C., Valadez, G., Bogoni, L., Moy, L., Supervised learning from multiple experts: whom to trust when everyone lies a bit. In: Proc. of the 26th Int. Conf. on Machine Learning, pp. 889–896, 2009.
 Raykar, V., Yu, S., Zhao, L., Valadez, G., Florin, C., Bogoni, L., Moy, L., Learning from crowds. Journal of Machine Learning Research, 1297– 1322, 2010.
The tar.gz with the source code can be obtained here.
The Amazon’s Mechanical Turk data used in  is also available here for download.
Please send questions and comments to fmpr [at] dei.uc.pt