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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0.2.7 by Juniper L. Simonis, 2 years 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, extraDistr, graphics, grDevices, lubridate, magrittr, memoise, methods, mvtnorm, nnet, progress, stats, topicmodels, viridis

Suggests knitr, pkgdown, rmarkdown, testthat, vdiffr

See at CRAN