Latent Dirichlet Allocation Using 'tidyverse' Conventions
Draw from the marginal posteriors of a tidylda topic model
Get predictions from a Latent Dirichlet Allocation model
Print Method for tidylda
Get Count Matrices from Beta or Theta (and Priors)
Objects exported from other packages
Update a Latent Dirichlet Allocation topic model
Abstracts and metadata from NIH research grants awarded in 2014
Augment method for tidylda objects
Calculate a matrix whose rows represent P(topic_i|tokens)
Calculate R-squared for a tidylda Model
Probabilistic coherence of topics
Convert various things to a dgCMatrix to work with various functions...
Make a lexicon for looping over in the gibbs sampler
Main C++ Gibbs sampler for Latent Dirichlet Allocation
Format alpha For Input into fit_lda_c
Format eta For Input into fit_lda_c
Generate a sample of LDA posteriors
Glance method for tidylda objects
Initialize topic counts for gibbs sampling
Construct a new object of class tidylda
Summarize a topic model consistently across methods/functions
Create a tidy tibble for a dgCMatrix
Utility function to tidy a simple triplet matrix
Tidy a matrix from a tidylda topic model
Bridge function for fitting tidylda topic models
Latent Dirichlet Allocation Using 'tidyverse' Conventions
Fit a Latent Dirichlet Allocation topic model
Implements an algorithm for Latent Dirichlet Allocation (LDA), Blei et at. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>, using style conventions from the 'tidyverse', Wickham et al. (2019)<doi:10.21105/joss.01686>, and 'tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>. Fitting is done via collapsed Gibbs sampling. Also implements several novel features for LDA such as guided models and transfer learning.