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.


bayesdfa 0.1.0

  • Initial submission to CRAN.

bayesdfa 0.1.1

  • Changed Makevars per exchange with Stan developers.

bayesdfa 0.1.2

  • Changed find_inverted_chains() and invert_chains() to be compatible with dplyr 0.8 release. Specifically, removed deprecated group_by_() and summarise_() functions and changed code to remove unused factor levels.

Reference manual

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0.1.7 by Eric J. Ward, 5 days ago

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Browse source code at

Authors: 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]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports dplyr, ggplot2, loo, methods, Rcpp, RcppParallel, reshape2, rlang, rstan, rstantools, viridisLite

Suggests testthat, parallel, knitr, rmarkdown

Linking to BH, Rcpp, RcppEigen, RcppParallel, rstan, StanHeaders

System requirements: GNU make

See at CRAN