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>.
Version: 0.2.0 Depends: R (≥ 4.1.0) Imports: cowplot, dplyr, ggdist, ggokabeito, ggplot2, loo, methods, posterior, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rlang, rstan (≥ 2.18.1), rstantools (≥ 2.3.1.1), stats, tidyr, utils LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0) Suggests: testthat (≥ 3.0.0) Published: 2025-06-20 DOI: 10.32614/CRAN.package.tsnet Author: Björn S. Siepe [aut, cre, cph], Matthias Kloft [aut], Daniel W. Heck [ctb] Maintainer: Björn S. Siepe <bjoernsiepe at gmail.com> BugReports: https://github.com/bsiepe/tsnet/issues License: GPL-3 URL: https://github.com/bsiepe/tsnet NeedsCompilation: yes SystemRequirements: GNU make Materials: README NEWS In views: TimeSeries CRAN checks: tsnet results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=tsnet to link to this page.
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