Package: bayesdfa 1.3.3
Eric J. Ward
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.
Authors:
bayesdfa_1.3.3.tar.gz
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bayesdfa.pdf |bayesdfa.html✨
bayesdfa/json (API)
NEWS
| # Install 'bayesdfa' in R: |
| install.packages('bayesdfa', repos = c('https://nmfs-opensci.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/fate-ewi/bayesdfa/issues
Last updated 1 months agofrom:709ed7aaf0. Checks:OK: 1 NOTE: 8. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 25 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 25 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 25 2024 |
| R-4.4-win-x86_64 | NOTE | Nov 25 2024 |
| R-4.4-mac-x86_64 | NOTE | Nov 25 2024 |
| R-4.4-mac-aarch64 | NOTE | Nov 25 2024 |
| R-4.3-win-x86_64 | NOTE | Nov 25 2024 |
| R-4.3-mac-x86_64 | NOTE | Nov 25 2024 |
| R-4.3-mac-aarch64 | NOTE | Nov 25 2024 |
Exports:dfa_cvdfa_fitteddfa_loadingsdfa_trendsfind_dfa_trendsfind_inverted_chainsfind_regimesfind_swansfit_dfafit_regimesinvert_chainsis_convergedlooplot_fittedplot_loadingsplot_regime_modelplot_trendspredictedrotate_trendssim_dfatrend_cor
Dependencies:abindbackportsBHcallrcheckmateclicolorspacedescdistributionaldplyrfansifarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgbuildpkgconfigplyrposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelreshape2rlangrstanrstantoolsscalesStanHeadersstringistringrtensorAtibbletidyselectutf8vctrsviridisLitewithr
Combining data with bayesdfa
Rendered froma2_combining_data.Rmdusingknitr::rmarkdownon Nov 25 2024.Last update: 2023-04-24
Started: 2023-04-20
Estimating process trend variability with bayesdfa
Rendered froma5_estimate_process_sigma.Rmdusingknitr::rmarkdownon Nov 25 2024.Last update: 2023-04-20
Started: 2023-04-20
Examples of fitting DFA models with lots of data
Rendered froma7_bigdata.Rmdusingknitr::rmarkdownon Nov 25 2024.Last update: 2023-04-20
Started: 2023-04-20
Examples of fitting smooth trend DFA models
Rendered froma4_smooth.Rmdusingknitr::rmarkdownon Nov 25 2024.Last update: 2023-04-20
Started: 2023-04-20
Examples of including covariates with bayesdfa
Rendered froma3_covariates.Rmdusingknitr::rmarkdownon Nov 25 2024.Last update: 2023-04-20
Started: 2023-04-20
Fitting compositional dynamic factor models with bayesdfa
Rendered froma6_compositional.Rmdusingknitr::rmarkdownon Nov 25 2024.Last update: 2023-04-20
Started: 2023-04-20
Overview of the bayesdfa package
Rendered froma1_bayesdfa.Rmdusingknitr::rmarkdownon Nov 25 2024.Last update: 2023-12-07
Started: 2023-04-20
