r-ldats 0.3.0
Dependencies: gsl@2.8
Propagated dependencies: r-viridis@0.6.5 r-topicmodels@0.2-17 r-progress@1.2.3 r-nnet@7.3-20 r-mvtnorm@1.3-3 r-memoise@2.0.1 r-magrittr@2.0.4 r-lubridate@1.9.4 r-extradistr@1.10.0 r-digest@0.6.39 r-coda@0.19-4.1
Channel: guix-cran
Home page: https://weecology.github.io/LDATS/
Licenses: Expat
Synopsis: Latent Dirichlet Allocation Coupled with Time Series Analyses
Description:
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>.
Total results: 1