R Client for the Microsoft Cognitive Services Web Language Model
REST API
R Client for the Microsoft Cognitive Services Web Language Model
REST API, including Break Into Words, Calculate Conditional
Probability, Calculate Joint Probability, Generate Next Words, and List
Available Models. A valid account MUST be registered at the Microsoft
Cognitive Services website (https://www.microsoft.com/cognitive-services/)
in order to obtain a (free) API key. Without an API key, this package will
not work properly.
A valid account MUST be registered with Microsoft's
Cognitive Services website
(https://www.microsoft.com/cognitive-services/) in order to
obtain a (free) API key. Without an API key, this package will
not work properly.

R Client for the Microsoft Cognitive Services Web Language Model
REST API, including Break Into Words, Calculate Conditional
Probability, Calculate Joint Probability, Generate Next Words, and List
Available Models. A valid account MUST be registered at the Microsoft
Cognitive Services website <https://www.microsoft.com/cognitive-services/>
in order to obtain a (free) API key. Without an API key, this package will
not work properly.

Implements the method of successive dichotomizations by
Bradley and Massof (2018) <doi:10.1371/journal.pone.0206106>,
which estimates item measures, person measures and ordered rating
category thresholds given ordinal rating scale data.

Qing Mai <mai@stat.fsu.edu>, Yi Yang <yiyang@umn.edu>,  Hui Zou <hzou@stat.umn.edu>
Efficient procedures for computing a new multi-class sparse discriminant analysis method that estimates all discriminant directions simultaneously.

Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously.

Qing Mai <mai@stat.fsu.edu>, Yi Yang <yi.yang6@mcgill.ca>,  Hui Zou <hzou@stat.umn.edu>
Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously. It is an implementation of the work proposed by Mai, Q., Yang, Y., and Zou, H. (2019) <doi:10.5705/ss.202016.0117>.