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) .


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0.2.4 by Juniper L. Simonis, 6 months ago,

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Authors: Juniper L. Simonis [aut, cre] , Erica M. Christensen [aut] , David J. Harris [aut] , Renata M. Diaz [aut] , Hao Ye [aut] , Ethan P. White [aut] , S.K. Morgan Ernest [aut] , Weecology [cph]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports coda, digest, dplyr, extraDistr, graphics, grDevices, here, lubridate, magrittr, memoise, methods, mvtnorm, nnet, progress, reshape, stats, topicmodels, viridis

Suggests knitr, pkgdown, rmarkdown, testthat, vdiffr, clue, RCurl, tidyr

See at CRAN