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: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]

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bayesdfa/json (API)
NEWS

# Install 'bayesdfa' in R:
install.packages('bayesdfa', repos = c('https://fate-ewi.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/fate-ewi/bayesdfa/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

21 exports 28 stars 2.81 score 59 dependencies 95 scripts 1.5k downloads

Last updated 7 months agofrom:6c7e03634c. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 13 2024
R-4.5-win-x86_64NOTESep 13 2024
R-4.5-linux-x86_64NOTESep 13 2024
R-4.4-win-x86_64NOTESep 13 2024
R-4.4-mac-x86_64NOTESep 13 2024
R-4.4-mac-aarch64NOTESep 13 2024
R-4.3-win-x86_64NOTESep 13 2024
R-4.3-mac-x86_64NOTESep 13 2024
R-4.3-mac-aarch64NOTESep 13 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 Sep 13 2024.

Last update: 2023-04-24
Started: 2023-04-20

Estimating process trend variability with bayesdfa

Rendered froma5_estimate_process_sigma.Rmdusingknitr::rmarkdownon Sep 13 2024.

Last update: 2023-04-20
Started: 2023-04-20

Examples of fitting DFA models with lots of data

Rendered froma7_bigdata.Rmdusingknitr::rmarkdownon Sep 13 2024.

Last update: 2023-04-20
Started: 2023-04-20

Examples of fitting smooth trend DFA models

Rendered froma4_smooth.Rmdusingknitr::rmarkdownon Sep 13 2024.

Last update: 2023-04-20
Started: 2023-04-20

Examples of including covariates with bayesdfa

Rendered froma3_covariates.Rmdusingknitr::rmarkdownon Sep 13 2024.

Last update: 2023-04-20
Started: 2023-04-20

Fitting compositional dynamic factor models with bayesdfa

Rendered froma6_compositional.Rmdusingknitr::rmarkdownon Sep 13 2024.

Last update: 2023-04-20
Started: 2023-04-20

Overview of the bayesdfa package

Rendered froma1_bayesdfa.Rmdusingknitr::rmarkdownon Sep 13 2024.

Last update: 2023-12-07
Started: 2023-04-20