There are mainly two packages in R that can be used for performing LDA on documents. One is the **topicmodels** package developed by Bettina Grün and Kurt Hornik and the second one is **lda** developed by Jonathan Chang. Here, we describe both these packages.

The topicmodels package is an interface to the C and C++ codes developed by the authors of the papers on LDA and
**Correlated Topic Models** (**CTM**) (references 7, 8, and 9 in the *References* section of this chapter). The main function `LDA`

in this package is used to fit LDA models. It can be called by:

>LDA(X,K,method = "Gibbs",control = NULL,model = NULL,...)

Here, *X* is a document-term matrix that can be generated using the **tm** package and *K* is the number of topics. The `method`

is the method to be used for fitting. There are two methods that are supported: `Gibbs`

and `VEM`

.

Let's do a small example of building LDA models using this package. The dataset used is the
**Reuter_50_50** dataset from the UCI Machine Learning...