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) .


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("LDATS")

0.2.7 by Juniper L. Simonis, 9 days ago


https://weecology.github.io/LDATS, https://github.com/weecology/LDATS


Report a bug at https://github.com/weecology/LDATS/issues


Browse source code at https://github.com/cran/LDATS


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