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.

References in zbMATH (referenced in 14 articles )

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  1. Chung-hong Chan; Marius Sältzer: oolong: An R package for validating automated content analysis tools (2020) not zbMATH
  2. Jonas Rieger: ldaPrototype: A method in R to get a Prototype of multiple Latent Dirichlet Allocations (2020) not zbMATH
  3. Modesto Escobar, Luis Martinez-Uribe: Network Coincidence Analysis: The netCoin R Package (2020) not zbMATH
  4. Margaret Roberts; Brandon Stewart; Dustin Tingley: stm: An R Package for Structural Topic Models (2019) not zbMATH
  5. Mair, Patrick: Modern psychometrics with R (2018)
  6. Taylor B. Arnold: A Tidy Data Model for Natural Language Processing using cleanNLP (2017) arXiv
  7. Tan, Linda S. L.; Chan, Aik Hui; Zheng, Tian: Topic-adjusted visibility metric for scientific articles (2016)
  8. Williamson, Sinead A.: Nonparametric network models for link prediction (2016)
  9. Fishkind, D. E.; Lyzinski, V.; Pao, H.; Chen, L.; Priebe, C. E.: Vertex nomination schemes for membership prediction (2015)
  10. Soriano, Jacopo; Au, Timothy; Banks, David: Text mining in computational advertising (2013)
  11. Taddy, Matt: Multinomial inverse regression for text analysis (2013)
  12. Zhao, Yanchang: R and data mining. Examples and case studies (2013)
  13. Salter-townshend, M.; White, A.; Gollini, I.; Murphy, T. B.: Review of statistical network analysis: models, algorithms, and software (2012)
  14. Bettina Grün; Kurt Hornik: topicmodels: An R Package for Fitting Topic Models (2011) not zbMATH