MAP Estimation of Topic Models

Posterior maximization for topic models (LDA) in text analysis, as described in Taddy (2012) `on estimation and selection for topic models'. Previous versions of this code were included as part of the textir package. If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.


MAP estimation of topic models in R. It implements estimation via posterior maximization for latent topic models in text analysis, as described in "On Estimation and Selection for Topic Models". These routines were previously part of textir, but have been spun off since version 2.0 of that package. This maptpx package is no longer actively maintained; instead, we are focusing on developing faster distributed factor models based on distributed multinomial regression, as implemented in the distrom package.

If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.

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Reference manual

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install.packages("maptpx")

1.9-2 by Matt Taddy, 4 years ago


http://faculty.chicagobooth.edu/matt.taddy/index.html


Browse source code at https://github.com/cran/maptpx


Authors: Matt Taddy <[email protected]>


Documentation:   PDF Manual  


GPL-3 license


Depends on slam

Suggests MASS


See at CRAN