Di Cook Award

DESCRIPTION

@@ -5,7 +5,7 @@ Version: 1.0.1
 Date: 2024-08-10
 Authors@R: c(person("Xiaolei", "Wang", , "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0009-0005-6192-9061")),person("Tomasz", "Woźniak", , "[email protected]", role = c("aut"), comment = c(ORCID = "0000-0003-2212-2378")))
 Maintainer: Xiaolei Wang <[email protected]>
-Description: Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483>. The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) <doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) <doi:10.3982/ECTA14468>. Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) <doi:10.1257/aer.20161852>. Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation. The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, and they constitute an integrated toolset.
+Description: Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483>. The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) <doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) <doi:10.3982/ECTA14468>. Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) <doi:10.1257/aer.20161852>. Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation. The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.
 License: GPL (>= 3)
 Imports: 
     Rcpp (>= 1.0.12),

R/bsvarSIGNs-package.R

@@ -43,7 +43,8 @@
 #' 'bsvarSIGNs' package is aligned regarding objects, workflows, and code 
 #' structure with the R package 'bsvars' by 
 #' Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, and they constitute an 
-#' integrated toolset.
+#' integrated toolset. It was granted the Di Cook Open-Source Statistical 
+#' Software Award by the Statistical Society of Australia in 2024.
 #' 
 #' @details
 #' 

README.Rmd

@@ -18,12 +18,14 @@ knitr::opts_chunk$set(
 
 An **R** package for Bayesian Estimation of Structural Vector Autoregressions Identified by Sign, Zero, and Narrative Restrictions
 
+*The First Prize laureate of the Di Cook Open-Source Statistical Software Award granted by the Statistical Society of Australia in 2024*
+
 <!-- badges: start -->
 [![](https://www.r-pkg.org/badges/version/bsvarSIGNs)](https://cran.r-project.org/package=bsvarSIGNs)
 [![R-CMD-check](https://github.com/bsvars/bsvarSIGNs/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/bsvars/bsvarSIGNs/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in [Giannone, Lenza, Primiceri (2015)](http://doi.org/10.1162/REST_a_00483). The sign restrictions are implemented employing the methods proposed by [Rubio-Ramírez, Waggoner & Zha (2010)](http://doi.org/10.1111/j.1467-937X.2009.00578.x), while identification through sign and zero restrictions follows the approach developed by [Arias, Rubio-Ramírez, & Waggoner (2018)](http://doi.org/10.3982/ECTA14468). Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by [Antolín-Díaz and Rubio-Ramírez (2018)](http://doi.org/10.1257/aer.20161852). Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation. The **bsvarSIGNs** package is aligned regarding objects, workflows, and code structure with the **R** package **bsvars** by [Woźniak (2024)](http://doi.org/10.32614/CRAN.package.bsvars), and they constitute an integrated toolset.
+Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in [Giannone, Lenza, Primiceri (2015)](http://doi.org/10.1162/REST_a_00483). The sign restrictions are implemented employing the methods proposed by [Rubio-Ramírez, Waggoner & Zha (2010)](http://doi.org/10.1111/j.1467-937X.2009.00578.x), while identification through sign and zero restrictions follows the approach developed by [Arias, Rubio-Ramírez, & Waggoner (2018)](http://doi.org/10.3982/ECTA14468). Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by [Antolín-Díaz and Rubio-Ramírez (2018)](http://doi.org/10.1257/aer.20161852). Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation. The **bsvarSIGNs** package is aligned regarding objects, workflows, and code structure with the **R** package **bsvars** by [Woźniak (2024)](http://doi.org/10.32614/CRAN.package.bsvars), and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.
 
 <a href="mailto:[email protected]"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/solid/envelope.svg" width="40" height="40"/> </a> 
 <a href="https://github.com/bsvars/bsvarSIGNs"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/github.svg" width="40" height="40"/> </a> 

README.md

@@ -6,6 +6,9 @@
 An **R** package for Bayesian Estimation of Structural Vector
 Autoregressions Identified by Sign, Zero, and Narrative Restrictions
 
+*The First Prize laureate of the Di Cook Open-Source Statistical
+Software Award granted by the Statistical Society of Australia in 2024*
+
 <!-- badges: start -->
 
 [![](https://www.r-pkg.org/badges/version/bsvarSIGNs)](https://cran.r-project.org/package=bsvarSIGNs)
@@ -36,7 +39,9 @@ comprehensive documentation. The **bsvarSIGNs** package is aligned
 regarding objects, workflows, and code structure with the **R** package
 **bsvars** by [Woźniak
 (2024)](http://doi.org/10.32614/CRAN.package.bsvars), and they
-constitute an integrated toolset.
+constitute an integrated toolset. It was granted the Di Cook Open-Source
+Statistical Software Award by the Statistical Society of Australia in
+2024.
 
 <a href="mailto:[email protected]">
 <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/solid/envelope.svg" width="40" height="40"/>