run_LDA: Run Latent Dirichlet Allocation on Tabular Data
In weecology/MATSS-LDATS: LDA_TS meta-analysis using MATSS

Test the Latent Dirichlet Allocation (LDA) model on the data with different number of topics (from 2 to max_topics), select the best one using AIC, and return the model object that is selected.

1	run_LDA(data, max_topics = 6, nseeds = 200, control = list())

`data`	a data.frame or tibble; each row is an observation (e.g. in time or space), and each column is a variable. Here, the common usage is for each column to be a species or taxon, and each row to be an observed sample. In the original specification for LDA, each row is a document, and each column is a word, with the entries being the counts of the words in each document.
`max_topics`	the maximum number of topics to try (the function will test a number of topics from 2 to `max_topics`)
`nseeds`	Number of seeds (replicate starts) to use for each value of `topics`. Must be conformable to `integer` value.
`control`	A `list` of parameters to control the running and selecting of LDA models. Values not input assume default values set by `LDA_set_control`. Values for running the LDAs replace defaults in (`LDAcontol`, see `LDA` (but if `seed` is given, it will be overwritten; use `iseed` instead).