Packaged: 2016-12-02 17:12:03 UTC; schaubergerg

Packaged: 2017-01-16 10:57:34 UTC; schaubergerg

Description: Performs 'BTLLasso' (Schauberger and Tutz, 2017: Subject-Specific Modelling of Paired Comparison Data - a Lasso-Type Penalty Approach), a method to include different types of variables in paired
Packaged: 2017-05-23 11:52:56 UTC; schaubergerg

Packaged: 2017-10-26 10:42:35 UTC; schaubergerg

Description: Performs 'BTLLasso' as described by Schauberger and Tutz (2019): BTLLasso - A Common Framework and Software Package for the Inclusion and Selection of Covariates in Bradley-Terry Models, Journal of Statistical Software, to appear) and Schauberger and Tutz (2017) <doi:10.1177/1471082X17693086>. BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models.

Packaged: 2015-09-02 14:45:31 UTC; schaubergerg

Title: Biterm Topic Models for Short Text
person('Jan', 'Wijffels', role = c('aut', 'cre', 'cph'), email = 'jwijffels@bnosac.be', comment = "R wrapper"),
person('BNOSAC', role = 'cph', comment = "R wrapper"),
person('Xiaohui Yan', role = c('ctb', 'cph'), email = 'xhcloud@gmail.com', comment = "BTM C++ library"))
Description: Biterm Topic Models find topics in collections of short texts.
It is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns which are called biterms.
This in contrast to traditional topic models like Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis
which are word-document co-occurrence topic models.
A biterm consists of two words co-occurring in the same short text window.
This context window can for example be a twitter message, a short answer on a survey, a sentence of a text or a document identifier.
The techniques are explained in detail in the paper 'A Biterm Topic Model For Short Text' by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013) <https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/BTM-WWW13.pdf>.
Author: Jan Wijffels [aut, cre, cph] (R wrapper),
Xiaohui Yan [ctb, cph] (BTM C++ library)