Authors@R: c(person("Nikolas", "Kuschnig", role = c("aut", "cre"), email = "nikolas.kuschnig@wu.ac.at"),
Description: Toolkit for the estimation of hierarchical Bayesian vector
autoregressions. Implements hierarchical prior selection for conjugate
priors in the fashion of Giannone, Lenza & Primiceri (2015)
<doi:10.1162/REST_a_00483>. Allows for the computation of impulse responses
and forecasts and provides several methods for assessing results.
Imports: mvtnorm, stats, graphics, utils

person("Michael", "McCracken", role = "dtc"),

Authors@R: c(person("Nikolas", "Kuschnig", role = c("aut", "cre"), email = "nikolas.kuschnig@wu.ac.at", comment = c(ORCID = "0000-0002-6642-2543")),
Author: Nikolas Kuschnig [aut, cre] (<https://orcid.org/0000-0002-6642-2543>),
Description: Estimation of hierarchical Bayesian vector autoregressive models.
Implements hierarchical prior selection for conjugate priors in the fashion
of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>. Functions
to compute and identify impulse responses, calculate forecasts,
forecast error variance decompositions and scenarios are available.
Several methods to print, plot and summarise results facilitate analysis.
Imports: mvtnorm, stats, graphics, utils, grDevices

person("Lukas", "Vashold", role = "aut", comment = c(ORCID = "0000-0002-3562-3414")),

Description: Estimation of hierarchical Bayesian vector autoregressive models
following Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>.
of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>.
Functions to compute and identify impulse responses, calculate forecasts,

Title: Tidy Bayesian Vector Autoregression
Authors@R: c(person("Lukas", "Vashold", role = c("aut", "cre"), email = "lukas.vashold@wu.ac.at", comment = c(ORCID = "0000-0002-3562-3414")),
person("Nikolas", "Kuschnig", role = c("aut"), comment = c(ORCID = "0000-0002-6642-2543")))
Author: Lukas Vashold [aut, cre] (<https://orcid.org/0000-0002-3562-3414>),
Description: Functions to prepare tidy objects from estimated models via 'BVAR'
visualisation thereof. Bridges the gap between estimating models with 'BVAR'
and plotting the results in a more sophisticated way with 'ggplot2' as well
Imports: ggplot2, tidyr, generics, rlang