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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>.
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Package details |
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| Author | Juniper L. Simonis [aut, cre] (<https://orcid.org/0000-0001-9798-0460>), Erica M. Christensen [aut] (<https://orcid.org/0000-0002-5635-2502>), David J. Harris [aut] (<https://orcid.org/0000-0003-3332-9307>), Renata M. Diaz [aut] (<https://orcid.org/0000-0003-0803-4734>), Hao Ye [aut] (<https://orcid.org/0000-0002-8630-1458>), Ethan P. White [aut] (<https://orcid.org/0000-0001-6728-7745>), S.K. Morgan Ernest [aut] (<https://orcid.org/0000-0002-6026-8530>), Weecology [cph] |
| Maintainer | Juniper L. Simonis <juniper.simonis@weecology.org> |
| License | MIT + file LICENSE |
| Version | 0.3.0 |
| URL | https://weecology.github.io/LDATS/ https://github.com/weecology/LDATS |
| Package repository | View on CRAN |
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