Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a twostage analysis to quantify dynamics in highdimensional temporal data. LDA decomposes multivariate data into lowerdimension 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.2003.3.45.993>, Western and Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and Ripley (2002, ISBN13:9780387954578), and Christensen et al. (2018) <doi:10.1002/ecy.2373>.
Package details 


Author  Juniper L. Simonis [aut, cre] (<https://orcid.org/0000000197980460>), Erica M. Christensen [aut] (<https://orcid.org/0000000256352502>), David J. Harris [aut] (<https://orcid.org/0000000333329307>), Renata M. Diaz [aut] (<https://orcid.org/0000000308034734>), Hao Ye [aut] (<https://orcid.org/0000000286301458>), Ethan P. White [aut] (<https://orcid.org/0000000167287745>), S.K. Morgan Ernest [aut] (<https://orcid.org/0000000260268530>), Weecology [cph] 
Maintainer  Juniper L. Simonis <[email protected]> 
License  MIT + file LICENSE 
Version  0.2.4 
URL  https://weecology.github.io/LDATS https://github.com/weecology/LDATS 
Package repository  View on CRAN 
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