
 Description:
choix is a Python library that provides inference algorithms for models based on Luce's choice axiom. These probabilistic models can be used to explain and predict outcomes of comparisons between items.
 Pairwise comparisons: when the data consists of comparisons between two items, the model variant is usually referred to as the BradleyTerry model. It is closely related to the Elo rating system used to rank chess players.
 Partial rankings: when the data consists of rankings over (a subset of) the items, the model variant is usually referred to as the PlackettLuce model.
 Top1 lists: another variation of the model arises when the data consists of discrete choices, i.e., we observe the selection of one item out of a subset of items.
 Choices in a network: when the data consists of counts of the number of visits to each node in a network, the model is known as the Network Choice Model.
choix makes it easy to infer model parameters from these different types of data, using a variety of algorithms:
 Luce Spectral Ranking
 MinorizationMaximization
 Rank Centrality
 Approximate Bayesian inference with expectation propagation
 Changes to previous version:
Initial Announcement on mloss.org.
 BibTeX Entry: Download
 URL: Project Homepage
 Supported Operating Systems: Any
 Data Formats: Not Applicable
 Tags: Python, Choice, Comparisons, Preferences, Rankings
 Archive: download here
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