lda: Collapsed Gibbs sampling methods for topic models This package implements latent Dirichlet allocation (LDA) and related models. This includes (but is not limited to) sLDA, corrLDA, and the mixed-membership stochastic blockmodel. Inference for all of these models is implemented via a fast collapsed Gibbs sampler writtten in C. Utility functions for reading/writing data typically used in topic models, as well as tools for examining posterior distributions are also included.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Taylor B. Arnold: A Tidy Data Model for Natural Language Processing using cleanNLP (2017) arXiv
- Tan, Linda S. L.; Chan, Aik Hui; Zheng, Tian: Topic-adjusted visibility metric for scientific articles (2016)
- Williamson, Sinead A.: Nonparametric network models for link prediction (2016)
- Fishkind, D. E.; Lyzinski, V.; Pao, H.; Chen, L.; Priebe, C. E.: Vertex nomination schemes for membership prediction (2015)
- Taddy, Matt: Multinomial inverse regression for text analysis (2013)
- Zhao, Yanchang: R and data mining. Examples and case studies (2013)
- Bettina Grün; Kurt Hornik: topicmodels: An R Package for Fitting Topic Models (2011)