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, autoregressive and moving average components can be optionally included. 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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0.1.6 by Eric J. Ward, 7 months ago

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Browse source code at

Authors: Eric J. Ward [aut, cre] , Sean C. Anderson [aut] , Luis A. Damiano [aut] , Mary E. Hunsicker , [ctb] , Mike A. Litzow [ctb] , Trustees of Columbia University [cph]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports rstan, rstantools, ggplot2, loo, dplyr, reshape2, rlang

Depends on Rcpp, methods

Suggests testthat, parallel, knitr, rmarkdown

Linking to StanHeaders, rstan, BH, Rcpp, RcppEigen

System requirements: GNU make

See at CRAN