weecology/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>.

Getting started

Package details

Maintainer
LicenseMIT + file LICENSE
Version0.2.5
URL https://weecology.github.io/LDATS https://github.com/weecology/LDATS
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("weecology/LDATS")
weecology/LDATS documentation built on Dec. 24, 2019, 8:11 p.m.