R packages for performing topic modeling / LDA: just `topicmodels` and `lda` It seems to me that only two R packages are able to perform Latent Dirichlet Allocation:
One is lda, authored by Jonathan Chang; and the other is topicmodels authored by Bettina Grün and Kurt Hornik.
What are the differences between these two packages, in terms of performance, implementation details and extensibility?
 A: The  R Structural Topic Model (STM) package by  Molly Roberts, Brandon Stewart and Dustin Tingley is also a great choice. Built on top of the tm package it's a general framework for topic modeling with document-level covariate information.
http://structuraltopicmodel.com/
The STM package includes a series of methods (grid search) and measures (semantic coherence, residuals and exclusivity) to determine the number of topics. Setting the number of topics to 0 will also let the model determine an optimum number of topics. 
The stmBrowser package is a great data visualization complement to visualize the influence of external variables on topics. See this example related to the 2016 presidential debates: http://alexperrier.github.io/stm-visualization/index.html. 
A: Implementation:
The topicmodels package provides an interface to the GSL C and C++ code for topic models by Blei et al. and Phan et al. For the earlier it uses Variational EM, for the latter Gibbs Sampling. See http://www.jstatsoft.org/v40/i13/paper . The package works well with the utilities from the tm package. 
The lda package uses a collapsed Gibbs Sampler for a number of models similar to those from the GSL library. However, it has been implemented by the package authors itself, not by Blei et al. This implementation therefore differs in general from the estimation technique proposed in the original papers introducing these model variants, where the VEM algorithm is usually applied. On the other hand, the package offers more functionality then the other package.
The package provides text mining functionality too. 
Extensibility:
Regarding extensibility, the topicmodel code by its very nature can be extended to interface other topic model code written in C and C++. The lda package seems to be more relying on the specific implementation provided by the authors, but there Gibbs sampler might allow specifying your own topic model. For extensibility issues nota bene, the former is licensed under GPL-2 and the latter LGPL, so it might depend on what you need to extend it for (GPL-2 is stricter regarding the open source aspect, i.e. you can't use it in proprietary software). 
Performance:
I can't help you here, I only used topicmodels so far.
Conclusion:
Personally I use topicmodels, as it is well documented (see the JSS paper above) and I trust the authors (Grün also implemeted flexmix and Hornik is R core member). 
A: +1 for topicmodels. @Momo's answer is very comprehensive. I'd just add that topicmodels takes input as document term matrices, which are easily made with the tm package or using python. The lda package uses a more esoteric form of input (based on Blei's LDA-C) and I've had no luck using the built-in functions to convert dtm into the lda package format (the lda documentation is very poor, as Momo notes). 
I have some code that starts with raw text, pre-processes it in tm and puts it through topicmodels (including finding the optimum number of topics in advance and working with the output) here. Could be useful to someone coming to topicmodels for the first time. 
A: I used all three libraries, among all 3 viz., topicmodels, lda, stm; not everyone works with n grams. The topicmodels library is good with its estimation and it also work with n grams. But if anyone is working with uni grams then the practitioner may preferred stm as it gives structured output.
