Man pages for kshirley/LDAtools
Tools to fit a topic model using Latent Dirichlet Allocation (LDA)
| bigram.table | Compute table of bigrams |
| collapse.bigrams | Replace specified bigrams with terms representing the bigrams |
| ent | Entropy |
| fitLDA | Fit LDA model via Gibbs sampler |
| flag.exact | Flag the documents that exactly match a pre-specified list of... |
| flag.partial | Flag the documents that contain an occurrence of one or more... |
| getProbs | Compute topic-word and document-topic probability... |
| jsviz | Create a list of required objects from fitted topic model to... |
| KL | Compute symmetric version of Kullback-Leibler (KL) divergence... |
| lu | Compute the number of unique elements in a vector |
| normalize | Normalize |
| perplexity.bounds | Compute the lower bound of the perplexity of a topic model... |
| plotLoglik | Traceplot of log-likelihood. |
| plotTokens | Plot probable tokens for a given topic |
| predictLDA | Estimate topics for new documents using a Gibbs sampler |
| preprocess | Preprocess raw documents according to various options |
| preprocess.newdocs | Preprocess raw version of new documents based on previously... |
| remap.terms | This function replaces instances of specified terms with... |
| su | Sort the unique elements in a vector |
| token.rank | Compute distinctiveness and saliency of the words in the... |
| topdocs | Find representative documents for each topic |
