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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.
Getting started
Package overview
Combining data with bayesdfa
Estimating process trend variability with bayesdfa
Examples of fitting DFA models with lots of data
Examples of fitting smooth trend DFA models
Examples of including covariates with bayesdfa
Fitting compositional dynamic factor models with bayesdfa
Overview of the bayesdfa package
Browse package contents
Package details |
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| Author | Eric J. Ward [aut, cre], Sean C. Anderson [aut], Luis A. Damiano [aut], Michael J. Malick [aut], Philina A. English [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@noaa.gov> |
| License | GPL (>= 3) |
| Version | 1.3.4 |
| URL | https://fate-ewi.github.io/bayesdfa/ |
| Package repository | View on CRAN |
| Installation |
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