Package: tsnet 0.1.0

tsnet: Fitting, Comparing, and Visualizing Networks Based on Time Series Data

Fit, compare, and visualize Bayesian graphical vector autoregressive (GVAR) network models using 'Stan'. These models are commonly used in psychology to represent temporal and contemporaneous relationships between multiple variables in intensive longitudinal data. Fitted models can be compared with a test based on matrix norm differences of posterior point estimates to quantify the differences between two estimated networks. See also Siepe, Kloft & Heck (2024) <doi:10.31234/osf.io/uwfjc>.

Authors:Björn S. Siepe [aut, cre, cph], Matthias Kloft [aut], Daniel W. Heck [ctb]

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tsnet/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/bsiepe/tsnet/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • fit_data - Example Posterior Samples
  • ts_data - Simulated Time Series Dataset

On CRAN:

8 exports 1 stars 1.46 score 64 dependencies 9 scripts 174 downloads

Last updated 7 months agofrom:15721ee1ca. Checks:OK: 4 NOTE: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 27 2024
R-4.5-win-x86_64OKAug 27 2024
R-4.5-linux-x86_64NOTEAug 27 2024
R-4.4-win-x86_64OKAug 27 2024
R-4.4-mac-x86_64NOTEAug 27 2024
R-4.4-mac-aarch64NOTEAug 27 2024
R-4.3-win-x86_64OKAug 27 2024
R-4.3-mac-x86_64NOTEAug 27 2024
R-4.3-mac-aarch64NOTEAug 27 2024

Exports:check_eigencompare_gvarget_centralityplot_centralitypost_distance_withinposterior_plotstan_fit_convertstan_gvar

Dependencies:abindbackportsBHcallrcheckmateclicolorspacecowplotcpp11descdistributionaldplyrfansifarvergenericsggdistggokabeitoggplot2gluegridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgbuildpkgconfigposteriorprocessxpspurrrquadprogQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanrstantoolsscalesStanHeadersstringistringrtensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr