LDATS (0.2.4)

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Latent Dirichlet Allocation Coupled with Time Series Analyses.


Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a two-stage analysis to quantify dynamics in high-dimensional temporal data. LDA decomposes multivariate data into lower-dimension latent groupings, whose relative proportions are modeled using generalized Bayesian time series models that include abrupt changepoints and smooth dynamics. The methods are described in Blei et al. (2003) , Western and Kleykamp (2004) , Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) .

Maintainer: Juniper L. Simonis
Author(s): Juniper L. Simonis [aut, cre] (<https://orcid.org/0000-0001-9798-0460>), Erica M. Christensen [aut] (<https://orcid.org/0000-0002-5635-2502>), David J. Harris [aut] (<https://orcid.org/0000-0003-3332-9307>), Renata M. Diaz [aut] (<https://orcid.org/0000-0003-0803-4734>), Hao Ye [aut] (<https://orcid.org/0000-0002-8630-1458>), Ethan P. White [aut] (<https://orcid.org/0000-0001-6728-7745>), S.K. Morgan Ernest [aut] (<https://orcid.org/0000-0002-6026-8530>), Weecology [cph]

License: MIT + file LICENSE

Uses: coda, digest, dplyr, extraDistr, here, lubridate, magrittr, memoise, mvtnorm, nnet, progress, reshape, topicmodels, viridis, clue, RCurl, testthat, knitr, tidyr, rmarkdown, vdiffr, pkgdown

Released 3 months ago.



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