bayesdfa: Bayesian Dynamic Factor Analysis (DFA) with 'Stan'

Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic factor analysis is a dimension reduction tool for multivariate time series. 'bayesdfa' extends conventional dynamic factor models in several ways. First, extreme events may be estimated in the latent trend by modeling process error with a student-t distribution. Second, alternative constraints (including proportions are allowed). Third, the estimated dynamic factors can be analyzed with hidden Markov models to evaluate support for latent regimes.

Version: 0.1.7
Depends: R (≥ 3.5.0)
Imports: dplyr, ggplot2, loo (≥ 2.0.0), methods, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), reshape2, rlang, rstan (≥ 2.18.1), rstantools (≥ 2.1.1), viridisLite
LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.1)
Suggests: testthat, parallel, knitr, rmarkdown
Published: 2021-05-04
Author: Eric J. Ward [aut, cre], Sean C. Anderson [aut], Luis A. Damiano [aut], Michael J. Malick [aut], Mary E. Hunsicker, [ctb], Mike A. Litzow [ctb], Mark D. Scheuerell [ctb], Elizabeth E. Holmes [ctb], Nick Tolimieri [ctb], Trustees of Columbia University [cph]
Maintainer: Eric J. Ward <eric.ward at noaa.gov>
BugReports: https://github.com/fate-ewi/bayesdfa/issues
License: GPL (≥ 3)
URL: https://fate-ewi.github.io/bayesdfa/
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: NEWS
CRAN checks: bayesdfa results

Downloads:

Reference manual: bayesdfa.pdf
Vignettes: Overview of the bayesdfa package
Combining data with bayesdfa
Fitting compositional dynamic factor models with bayesdfa
Examples of including covariates with bayesdfa
Estimating process trend variability with bayesdfa
Package source: bayesdfa_0.1.7.tar.gz
Windows binaries: r-devel: bayesdfa_0.1.7.zip, r-release: bayesdfa_0.1.7.zip, r-oldrel: bayesdfa_0.1.7.zip
macOS binaries: r-release: bayesdfa_0.1.7.tgz, r-oldrel: bayesdfa_0.1.6.tgz
Old sources: bayesdfa archive

Linking:

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