53 vignettes out

.Rbuildignore

@@ -9,3 +9,8 @@
 ^pkgdown$
 ^cran-comments\.md$
 ^CRAN-SUBMISSION$
+^vignettes/paper$
+^doc$
+^Meta$
+^vignettes/paper/bsvars\.Rmd$
+^vignettes/paper/bsvars\.knit\.md$

.github/workflows/R-CMD-check.yaml

@@ -43,7 +43,16 @@ jobs:
         with:
           extra-packages: any::rcmdcheck
           needs: check
+      - uses: r-lib/actions/setup-tinytex@v2
+      - run: tlmgr --version
+
+      - name: Install additional LaTeX packages
+        run: |
+          tlmgr update --self
+          tlmgr install pdfpages pdflscape
+          tlmgr list --only-installed
 
       - uses: r-lib/actions/check-r-package@v2
         with:
           upload-snapshots: true
+          build_args: '"--compact-vignettes=gs+qpdf"'

.gitignore

@@ -57,3 +57,5 @@ rsconnect/
 .quarto
 *.so
 docs
+/doc/
+/Meta/

DESCRIPTION

@@ -1,11 +1,11 @@
 Package: bsvarSIGNs
 Type: Package
 Title: Bayesian SVARs with Sign, Zero, and Narrative Restrictions
-Version: 1.0.1.9000
-Date: 2024-08-10
+Version: 2.0
+Date: 2025-01-28
 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. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.
+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 including the vignette by Wang & Woźniak (2024) <doi:10.48550/arXiv.2501.16711>. 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),
@@ -20,9 +20,12 @@ Depends:
     R (>= 2.10),
     RcppArmadillo,
     bsvars
-Suggests: tinytest
-URL: https://bsvars.github.io/bsvarSIGNs/
+Suggests: 
+    knitr,
+    tinytest
+URL: https://bsvars.org/bsvarSIGNs/
 BugReports: https://github.com/bsvars/bsvarSIGNs/issues
 Encoding: UTF-8
 LazyData: true
+VignetteBuilder: knitr
 RoxygenNote: 7.3.2

NEWS.md

@@ -1,6 +1,7 @@
-# bsvarSIGNs (development version)
+# bsvarSIGNs 2.0
 
-1. Fixing C++ code to avoid problems with future check [#59](https://github.com/bsvars/bsvarSIGNs/issues/59)
+1. The package has a [vignette](https://doi.org/10.48550/arXiv.2501.16711) now! ...and better citation information [#53](https://github.com/bsvars/bsvarSIGNs/issues/53)
+2. Fixing C++ code to avoid problems with future check [#59](https://github.com/bsvars/bsvarSIGNs/issues/59)
 
 # bsvarSIGNs 1.0.1
 

README.Rmd

@@ -25,14 +25,19 @@ 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 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.
+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 & 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 including the vignette by [Wang & Woźniak (2024)](https://doi.org/10.48550/arXiv.2501.16711). 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://bsvars.org"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/solid/house.svg" width="40" height="40"/> </a>
+<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> 
-<a href="https://bsky.app/profile/bsvars.org"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/solid/cloud.svg" width="50" height="50"/> </a>
 <a href="https://fosstodon.org/@bsvars"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/mastodon.svg" width="50" height="50"/> </a>
+<a href="https://bsky.app/profile/bsvars.org"> <img src="https://upload.wikimedia.org/wikipedia/commons/7/7a/Bluesky_Logo.svg" width="40" height="40"/> </a>
 
 
+<a href="https://bsvars.org/"><img src="https://raw.githubusercontent.com/bsvars/hex/refs/heads/main/bsvars.org/bsvars.org.png" width="120" alt="bsvars.org website" /></a>
+<a href="https://bsvars.org/bsvars/"><img src="https://raw.githubusercontent.com/bsvars/hex/refs/heads/main/bsvars/bsvars.png" width="120" alt="bsvars website" /></a>
+<a href="https://bsvars.org/bsvarSIGNs/"><img src="https://raw.githubusercontent.com/bsvars/hex/refs/heads/main/bsvarSIGNs/bsvarSIGNs.png" width="120" alt="bsvarSIGNs website" /></a>
+
 
 ## Features
 
@@ -89,6 +94,22 @@ This beautiful logo can be reproduced in R using [this file](https://github.com/
 <p></p>
 
 
+## Resources
+
+- a vignette by [Wang & Woźniak (2025)](https://doi.org/10.48550/arXiv.2501.16711) 
+- a [reference manual](https://cran.r-project.org/web/packages/bsvarSIGNs/bsvarSIGNs.pdf)
+- a website of the family of packages [bsvars.org](https://bsvars.org/)
+- **bsvarSIGNs** on [CRAN](https://cran.r-project.org/package=bsvarSIGNs)
+- presentations:
+  - for students at [Szkoła Główna Handlowa](https://www.sgh.waw.pl/) given in Warsaw in December 2024 [featuring **bsvars** 3.2 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-12-sgh/)]
+  - at [Uniwersytet Warszawski](https://www.wne.uw.edu.pl/) given in Warsaw in December 2024  [featuring **bsvars** 3.2 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-12-uwwne/)
+  - for students and researchers at [Uniwersytet Ekonomiczny w Krakowie](https://uek.krakow.pl/) given in Cracow in December 2024 [featuring **bsvars** 3.2 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-12-uek/)
+  - a [youtube recording](https://youtu.be/QT02OTZWW14)] for [[Forecasting for Social Good](https://www.f4sg.org/)
+  - for [Forecasting for Social Good](https://www.f4sg.org/) given online in December 2024 [featuring **bsvars** 3.2 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-12-F4SG/)]
+  - for Bayesian Econometrics students at the University of Melbourne given in October 2024 [featuring **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-10-be24-bsvarSIGNs/)
+  - for the [QuantEcon](https://quantecon.org/) lab at the Australian National University given in August 2024 [featuring **bsvars** 3.1 and **bsvarSIGNs** 1.0.1](https://bsvars.org/2024-08-bsvars-QuantEcon/)
+  - at Monash University given in August 2024 [featuring **bsvars** 3.1 and **bsvarSIGNs** 1.0](https://bsvars.org/2024-08-bsvars-monash/)
+
 ## Examples
 
 Replication of the results by [Arias, Rubio-Ramírez, & Waggoner (2018)](http://doi.org/10.3982/ECTA14468).
@@ -184,7 +205,7 @@ The package is under intensive development. Your help is most welcome! Please, h
 <a href="https://github.com/donotdespair"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/github.svg" width="40" height="40"/> </a> 
 <a href="https://orcid.org/0000-0003-2212-2378"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/orcid.svg" width="40" height="40"/> </a> 
 <a href="https://www.linkedin.com/in/tomaszwwozniak"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/linkedin.svg" width="40" height="40"/> </a>
-<a href="http://scholar.google.com/citations?user=2uWpFrYAAAAJ&hl"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/google.svg" width="40" height="40"/> </a> 
-<a href="https://arxiv.org/a/wozniak_t_1"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/solid/circle-xmark.svg" width="40" height="40"/> </a> 
-<a href="https://fosstodon.org/@tomaszwozniak"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/mastodon.svg" width="50" height="50"/> </a>
-<a href="https://bsky.app/profile/tomaszwozniak.bsky.social"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/solid/cloud.svg" width="50" height="50"/> </a>
+<a href="http://scholar.google.com/citations?user=2uWpFrYAAAAJ&hl"> <img src="https://raw.githubusercontent.com/jpswalsh/academicons/refs/heads/master/svg/google-scholar-square.svg" width="40" height="40"/> </a> 
+<a href="https://arxiv.org/a/wozniak_t_1"> <img src="https://raw.githubusercontent.com/jpswalsh/academicons/refs/heads/master/svg/arxiv-square.svg" width="40" height="40"/> </a> 
+<a href="https://fosstodon.org/@tomaszwozniak"> <img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/mastodon.svg" width="40" height="40"/> </a>
+<a href="https://bsky.app/profile/tomaszwozniak.bsky.social"> <img src="https://upload.wikimedia.org/wikipedia/commons/7/7a/Bluesky_Logo.svg" width="40" height="40"/> </a>