Package: tseffects
Title: Dynamic (Causal) Inferences from Time Series (with Interactions)
Version: 0.1.4
Authors@R: c(person("Soren", "Jordan", comment = c(ORCID = "0000-0003-4201-1085"), email = "sorenjordanpols@gmail.com", role = c("aut", "cre", "cph")), person("Garrett N. Vande Kamp", email = "garrettvandekamp@uga.edu", role = c("aut")), person("Reshi Rajan", email = "rrajan@tamu.edu", role = c("aut")))
Maintainer: Soren Jordan <sorenjordanpols@gmail.com>
Description: Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) (see De Boef and Keele 2008 <doi:10.1111/j.1540-5907.2007.00307.x>) are the workhorse models in uncovering dynamic inferences. ADL models are simple to estimate; this is what makes them attractive. Once these models are estimated, what is less clear is how to uncover a rich set of dynamic inferences from these models. We provide tools for recovering those inferences in three forms: causal inferences from ADL models, traditional time series quantities of interest (short- and long-run effects), and dynamic conditional relationships.
URL: https://sorenjordan.github.io/tseffects/,
        https://github.com/sorenjordan/tseffects
BugReports: https://github.com/sorenjordan/tseffects/issues
Imports: mpoly, car, ggplot2, sandwich, stats, utils
Suggests: knitr, rmarkdown, vdiffr, testthat (>= 3.0.0)
Depends: R (>= 3.5.0)
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
BuildManual: yes
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-10-06 14:16:05 UTC; sorenjordan
Author: Soren Jordan [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0003-4201-1085>),
  Garrett N. Vande Kamp [aut],
  Reshi Rajan [aut]
Repository: CRAN
Date/Publication: 2025-10-09 12:10:02 UTC
Built: R 4.4.3; ; 2025-11-12 05:52:45 UTC; windows
