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: 0.3.0 Depends: R (≥ 3.5.0) Imports: coda, digest, extraDistr, graphics, grDevices, lubridate, magrittr, memoise, methods, mvtnorm, nnet, progress, stats, topicmodels, viridis Suggests: knitr, pkgdown, rmarkdown, testthat, vdiffr Published: 2023-09-19 DOI: 10.32614/CRAN.package.LDATS 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 SystemRequirements: gsl Materials: README NEWS CRAN checks: LDATS results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=LDATS to link to this page.
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