Package: sstvars
Type: Package
Title: Toolkit for Reduced Form and Structural Smooth Transition Vector
        Autoregressive Models
Version: 1.2.3
Authors@R: person("Savi", "Virolainen",
                  email = "savi.virolainen@helsinki.fi",
                  role = c("aut", "cre"),
                  comment = c(ORCID = "https://orcid.org/0000-0002-5075-6821"))
Author: Savi Virolainen [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-5075-6821>)
Maintainer: Savi Virolainen <savi.virolainen@helsinki.fi>
Description: Penalized and non-penalized maximum likelihood estimation of smooth
  transition vector autoregressive models with various types of transition weight
  functions, conditional distributions, and identification methods. Constrained
  estimation with various types of constraints is available. Residual based
  model diagnostics, forecasting, simulations, counterfactual analysis, and
  computation of impulse response functions, generalized impulse response functions,
  generalized forecast error variance decompositions, as well as historical
  decompositions. See
  Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>,
  Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>,
  Markku Lanne, Savi Virolainen (2025) <doi:10.1016/j.jedc.2025.105162>,
  Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.
Depends: R (>= 4.0.0)
URL: https://github.com/saviviro/sstvars
BugReports: https://github.com/saviviro/sstvars/issues
SystemRequirements: BLAS, LAPACK
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp (>= 1.0.0), RcppArmadillo (>= 0.12.0.0.0), parallel (>=
        4.0.0), pbapply (>= 1.7-0), stats (>= 4.0.0), graphics (>=
        4.0.0), utils (>= 4.0.0)
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-01-26 17:02:54 UTC; savi
Repository: CRAN
Date/Publication: 2026-01-26 18:00:02 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-03-27 08:58:57 UTC; unix
Archs: sstvars.so.dSYM
