Maintainer: Jonathan Chang <jonchang@fb.com>, Andrew Dai

Suggests: Matrix, reshape2, ggplot2 (>= 0.9.1), penalized

Maintainer: Jonathan Chang <jcone@princeton.edu>, Andrew Dai

Title: Collapsed Gibbs Sampling Methods for Topic Models
Description: 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 written
in C.  Utility functions for reading/writing data
typically used in topic models, as well as tools for
examining posterior distributions are also included.
Suggests: Matrix, reshape2, ggplot2 (>= 1.0.0), penalized, nnet
Packaged: 2015-11-22 08:13:39 UTC; jonathanchang

Title: Cross-validation for linear discriminate analysis
Description: This is the gui interface to calculate cross-validation of the principle component and discriminate analysis
Packaged: Wed Aug 13 06:06:15 2008; wenxuan

Description: This is the gui interface to calculate cross-validation of
the principle component and discriminate analysis

Title: Fit Latent Dirichlet Allocation Models using Stochastic
Authors@R: person("Nicholas", "Erskine", email = "nicholas.erskine95@gmail.com", role = c("aut", "cre"))
Description: Fits Latent Dirichlet Allocation topic models to text data using the stochastic variational inference algorithm described in Hoffman et. al. (2013) <arXiv:1206.7051v3>. This method is more efficient than the original batch variational inference algorithm for LDA, and allows users to fit LDA models with more topics and to larger text corpora than would be feasible using that older method.
Imports: Rcpp, reshape2, tm (>= 0.6), methods, Rdpack

Title: Prototype of Multiple Latent Dirichlet Allocation Runs
Authors@R: person("Jonas", "Rieger", email="jonas.rieger@tu-dortmund.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0007-4478"))
Description: Determine a Prototype from a number of runs of Latent Dirichlet Allocation (LDA) measuring its similarities with S-CLOP: A procedure to select the LDA run with highest mean pairwise similarity, which is measured by S-CLOP (Similarity of multiple sets by Clustering with Local Pruning), to all other runs. LDA runs are specified by its assignments leading to estimators for distribution parameters. Repeated runs lead to different results, which we encounter by choosing the most representative LDA run as prototype.
Imports: checkmate (>= 1.8.5), colorspace (>= 1.4-1), data.table (>=
Suggests: batchtools (>= 0.9.11), future, parallelMap, RColorBrewer (>=
Author: Jonas Rieger [aut, cre] (<https://orcid.org/0000-0002-0007-4478>)