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>.
Last updated
changepointldaparallel-temperingportalsoftmax
7.36 score 25 stars 1 dependents 51 scripts 726 downloads
portalr - Create Useful Summaries of the Portal Data
Download and generate summaries for the rodent, plant, ant, and weather data from the Portal Project. Portal is a long-term (and ongoing) experimental monitoring site in the Chihuahuan desert. The raw data files can be found at <https://github.com/weecology/portaldata>.
Last updated
community-ecologyecologysmall-mammal-trapping
6.98 score 12 stars 79 scripts 894 downloads