LDATS

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 30 days

This package has been downloaded 535 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 9 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 8,274 times in the last 365 days. That's a lot of interest! Someone might even write a blog post about it. The day with the most downloads was Sep 11, 2024 with 76 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.