Description Details Author(s) References Examples
Functions to estimate an as-yet unnamed extension and generalization of the TPME model introduced in Krafft et al. (2012). Samplers are written in C++ and use the Boost libraries. The Model should scale up to a maximum of about 10-20,000 emails sent by up to 50-100 senders and still run in a few weeks. (STILL VERY MUCH IN DEVELOPMENT).
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
To use this function, first call the Run_Full_Model function to actually run the ContentStructure model on an organizational email corpora. Then run the Create_Output function to actually generate output that you can look at. At this point, the Create_Output function does not support outputting a tidy aggregate level dataset unless you are using the North Carolina County Government dataset, however, this functionality will be added in the future. The package also only currently supports 0-1 binary covariates, although further support is on the way.
Matt Denny, Bruce Desmarais, Hanna Wallach Maintainer: Matt Denny <mzd5530@psu.edu>
First Publication:
Krafft, P., Moore, J., Desmarais, B. A., & Wallach, H. (2012). Topic-partitioned multinetwork embeddings. In Advances in Neural Information Processing Systems Twenty-Five. Retrieved from http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2012_1288.pdf
The appropriate publication to cite for this package is still in preparation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | # Load in necessary data
data(vocabulary)
data(author_attributes)
data(document_edge_matrix)
data(document_word_matrix)
# Run Model
Estimation_Results <- Run_Full_Model(
main_iterations = 100,
sample_step_burnin = 2000,
sample_step_iterations = 8000,
sample_step_sample_every = 20,
topics = 6,
clusters = 2,
mixing_variable = "Gender",
Auth_Attr = author_attributes,
Doc_Edge_Matrix = document_edge_matrix ,
Doc_Word_Matrix = document_word_matrix,
Vocab = vocabulary,
Seed = 123456
)
# Generate Output
Data <- Create_Output(
output_names = "Testing",
Estimation_Results = Estimation_Results,
print_agg_stats = TRUE,
Topic_Model_Burnin = 50,
Skip = 0,
Thin = 2
)
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