Our research group regularly releases code associated with our papers. We use Github organization to release it.
Please post questions, comments, and suggestions about this code to the topic models mailing list.
|lda-c||Latent Dirichlet allocation||C||D. Blei||This implements variational inference for LDA.|
|class-slda||Supervised topic models for classifiation||C++||C. Wang||Implements supervised topic models with a categorical response.|
|lda||R package for Gibbs sampling in many models||R||J. Chang||Implements many models and is fast . Supports LDA, RTMs (for networked documents), MMSB (for network data), and sLDA (with a continuous response).|
|online lda||Online inference for LDA||Python||M. Hoffman||Fits topic models to massive data. The demo downloads random Wikipedia articles and fits a topic model to them.|
|online hdp||Online inference for the HDP||Python||C. Wang||Fits hierarchical Dirichlet process topic models to massive data. The algorithm determines the number of topics.|
|tmve (online)||Topic Model Visualization Engine||Python||A. Chaney||A package for creating corpus browsers. See, for example, Wikipedia .|
|ctr||Collaborative modeling for recommendation||C++||C. Wang||Implements variational inference for a collaborative topic models. These models recommend items to users based on item content and other users' ratings.|
|dtm||Dynamic topic models and the influence model||C++||S. Gerrish||This implements topics that change over time and a model of how individual documents predict that change.|
|hdp||Hierarchical Dirichlet processes||C++||C. Wang||Topic models where the data determine the number of topics. This implements Gibbs sampling.|
|ctm-c||Correlated topic models||C||D. Blei||This implements variational inference for the CTM.|
|diln||Discrete infinite logistic normal||C||J. Paisley||This implements the discrete infinite logistic normal, a Bayesian nonparametric topic model that finds correlated topics.|
|hlda||Hierarchical latent Dirichlet allocation||C||D. Blei||This implements a topic model that finds a hierarchy of topics. The structure of the hierarchy is determined by the data.|
|turbotopics||Turbo topics||Python||D. Blei||Turbo topics find significant multiword phrases in topics.|