Merge branch 'main' of https://github.com/bsvars/bsvarSIGNs

.Rbuildignore

@@ -4,3 +4,6 @@
 ^bsvarSIGNs\.Rproj$
 ^\.Rproj\.user$
 ^/inst/varia$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$

.github/workflows/pkgdown.yaml

@@ -0,0 +1,50 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown
+
+permissions: read-all
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/github-pages-deploy-action@v4.5.0
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs

.gitignore

@@ -56,3 +56,4 @@ rsconnect/
 .DS_Store
 .quarto
 *.so
+docs

DESCRIPTION

@@ -3,9 +3,9 @@ Type: Package
 Title: Bayesian Estimation of Structural Vector Autoregressions Identified by Sign, Zero, and Narrative Restrictions
 Version: 0.1.9000
 Date: 2024-01-11
-Authors@R: c(person("Xiaolei", "Wang", , "adamwang15@gmail.com", role = c("aut", "cre")),person("Tomasz", "Woźniak", , "wozniak.tom@pm.me", role = c("aut"), comment = c(ORCID = "0000-0003-2212-2378")))
+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 the flexible Vector Autoregression with the 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 outlined 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 code structure, objects, and workflows 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 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: 
     R6,
@@ -21,6 +21,8 @@ Depends:
     R (>= 2.10),
     bsvars
 Suggests: tinytest
+URL: https://bsvars.github.io/bsvarSIGNs/
+BugReports: https://github.com/bsvars/bsvarSIGNs/issues
 Encoding: UTF-8
 LazyData: true
 RoxygenNote: 7.3.2

R/bsvarSIGNs-package.R

@@ -24,10 +24,10 @@
 #'
 #' @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 the flexible Vector Autoregression 
-#' with the estimated hyper-parameters of the Minnesota prior as in 
+#' 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 outlined by 
+#' 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>. 
@@ -40,8 +40,8 @@
 #' 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 code structure, objects, and 
-#' workflows with the R package 'bsvars' by 
+#' `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.
 #' 
@@ -127,17 +127,24 @@
 #'  Woźniak (2024) bsvars: Bayesian Estimation of Structural Vector Autoregressive Models. R package version 3.1, <doi:10.32614/CRAN.package.bsvars>.
 #'  
 #' @examples
-#' # specify sign and zero restrictions on the impulse response
+#' # investigate the effects of the optimism shock
+#' data(optimism)
+#' 
+#' # specify identifying restrictions:
+#' # + no effect on productivity (zero restriction)
+#' # + positive effect on stock prices (positive sign restriction)
 #' sign_irf       = matrix(c(0, 1, rep(NA, 23)), 5, 5)
 #' 
 #' # specify the model
-#' spec           = specify_bsvarSIGN$new(optimism * 100,
+#' specification  = specify_bsvarSIGN$new(optimism * 100,
 #'                                        p        = 4,
 #'                                        sign_irf = sign_irf)
+#'                                        
+#' # estimate the model
+#' posterior      = estimate(specification, S = 100)
 #' 
-#' # estimate the hyper-parameters
-#' spec$prior$estimate_hyper()
+#' # compute and plot impulse responses
+#' irf            = compute_impulse_responses(posterior, horizon = 40)
+#' plot(irf, probability = 0.68)
 #' 
-#' # estimate the model
-#' post           = estimate(spec, S = 1000)
 NULL