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.
Keywords for this software
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Modesto Escobar, Luis Martinez-Uribe: Network Coincidence Analysis: The netCoin R Package (2020) not zbMATH
- Margaret Roberts; Brandon Stewart; Dustin Tingley: stm: An R Package for Structural Topic Models (2019) not zbMATH
- Rieger, Jonas: Book review of: Mónica Bécue-Bertaut, Textual data science with R (2019)
- Mair, Patrick: Modern psychometrics with R (2018)
- 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) not zbMATH