update descriptions #41

DESCRIPTION

@@ -1,11 +1,11 @@
 Package: bsvarSIGNs
 Type: Package
 Title: Bayesian Estimation of Structural Vector Autoregressions Identified by Sign, Zero, and Narrative Restrictions
-Version: 0.0.1.9000
+Version: 1.0
 Date: 2024-07-19
 Authors@R: c(person("Xiaolei", "Wang", , "adamwang15@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0009-0005-6192-9061")),person("Tomasz", "Woźniak", , "wozniak.tom@pm.me", role = c("aut"), comment = c(ORCID = "0000-0003-2212-2378")))
 Maintainer: Xiaolei Wang <adamwang15@gmail.com>
-Description: 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 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 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.
 License: GPL (>= 3)
 Imports: 
     Rcpp (>= 1.0.12),

R/bsvarSIGNs-package.R

@@ -25,8 +25,8 @@
 #' @description 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 as in  
-#' Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483>. The sign 
+#' 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 

README.Rmd

@@ -22,7 +22,7 @@ An **R** package for Bayesian Estimation of Structural Vector Autoregressions Id
 [![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 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.
 
 <a href="mailto:bsvars@pm.me"> <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

@@ -14,8 +14,8 @@ Autoregressions Identified by Sign, Zero, and Narrative Restrictions
 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 as
-in [Giannone, Lenza, Primiceri
+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