beepr, broom, chinese.misc, dplyr, DT (>= 0.15), highcharter,
htmlwidgets, ldatuning, parallel, plotly, purrr, quanteda,
shiny, shinyalert, shinyBS, shinycssloaders, shinydashboard,
shinyjs, shinyWidgets, SnowballC, stringr, textmineR, tidyr,
tidytext, tm, topicmodels

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>.
coda, digest, dplyr, extraDistr, graphics, grDevices, here,
lubridate, magrittr, memoise, methods, mvtnorm, nnet, progress,
reshape, stats, topicmodels, viridis
knitr, pkgdown, rmarkdown, testthat, vdiffr, clue, RCurl,
tidyr

coda, digest, extraDistr, graphics, grDevices, lubridate,
magrittr, memoise, methods, mvtnorm, nnet, progress, stats,
topicmodels, viridis
dplyr, here, knitr, pkgdown, rmarkdown, testthat, vdiffr,
clue, reshape, RCurl, tidyr

knitr, pkgdown, rmarkdown, testthat, vdiffr

Linkage Disequilibrium of Ancestry (LDA) and LDA Score (LDAS)
Computation of linkage disequilibrium of ancestry (LDA) and linkage disequilibrium of ancestry score (LDAS). LDA calculates the pairwise linkage disequilibrium of ancestry between single nucleotide polymorphisms (SNPs). LDAS calculates the LDA score of SNPs. The methods are described in Barrie W, Yang Y, Attfield K E, et al (2022) <doi:10.1101/2022.09.23.509097>.

Interactive Visualization of Topic Models
c(person("Carson", "Sievert", role = c("aut", "cre"), email =
"cpsievert1@gmail.com"), person("Kenny", "Shirley", role = "aut",
email = "kshirley@research.att.com"))
Tools to create an interactive web-based visualization of a
topic model that has been fit to a corpus of text data using
Latent Dirichlet Allocation (LDA). Given the estimated parameters of
the topic model, it computes various summary statistics as input to
an interactive visualization built with D3.js that is accessed via
a browser. The goal is to help users interpret the topics in their
LDA topic model.
mallet, lda, topicmodels, gistr (>= 0.0.8.99), servr, shiny,
knitr, rmarkdown
Carson Sievert [aut, cre],
Kenny Shirley [aut]