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.
- Version1.3.3
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Last release02/26/2024
Documentation
- VignetteOverview of the bayesdfa package
- VignetteCombining data with bayesdfa
- VignetteExamples of including covariates with bayesdfa
- VignetteExamples of fitting smooth trend DFA models
- VignetteEstimating process trend variability with bayesdfa
- VignetteFitting compositional dynamic factor models with bayesdfa
- VignetteExamples of fitting DFA models with lots of data
- MaterialNEWS
- In ViewsBayesian
- In ViewsTimeSeries
Team
Eric J. Ward
Sean C. Anderson
Trustees of Columbia University
Show author detailsRolesCopyright holderMark D. Scheuerell
Luis A. Damiano
Show author detailsRolesAuthorMichael J. Malick
Show author detailsRolesAuthorMary E. Hunsicker
Show author detailsRolesContributorMike A. Litzow
Show author detailsRolesContributorElizabeth E. Holmes
Show author detailsRolesContributorNick Tolimieri
Show author detailsRolesContributor
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- Imports10 packages
- Suggests3 packages
- Linking To6 packages