LDATS: 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) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) <doi:10.1002/ecy.2373>.

Version:	0.2.4
Depends:	R (≥ 3.2.3)
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
Published:	2019-08-01
Author:	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]
Maintainer:	Juniper L. Simonis <juniper.simonis at weecology.org>
BugReports:	https://github.com/weecology/LDATS/issues
License:	MIT + file LICENSE
URL:	https://weecology.github.io/LDATS, https://github.com/weecology/LDATS
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	LDATS results

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