some first updates on README #12

README.Rmd

@@ -13,23 +13,45 @@ knitr::opts_chunk$set(
 )
 ```
 
+
+# bsvarSIGNs
+
+An **R** package for Bayesian Estimation of Structural Vector Autoregressions Identified by Sign, Zero, and Narrative Restrictions
+
 <!-- badges: start -->
 [![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 -->
 
-# bsvarSIGNs
+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)](http://doi.org/10.1162/REST_a_00483). The sign restrictions are implemented employing the methods outlined 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 code structure, objects, and workflows with the **R** package **bsvars** by [Woźniak (2024)](http://doi.org/10.32614/CRAN.package.bsvars), and they constitute an integrated toolset.
 
-Developing an R package for Bayesian Structural VARs identified by zero, sign and narrative restrictions. 
 
-# Installation
 
-```r
+
+
+## Installation
+
+#### The first time you install the package
+
+You must have a **cpp** compiler. Follow the instructions from [Section 1.3. by Eddelbuettel & François (2023)](https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-FAQ.pdf). In short, for **Windows:** install [RTools](https://CRAN.R-project.org/bin/windows/Rtools/), for **macOS:** install [Xcode Command Line Tools](https://www.freecodecamp.org/news/install-xcode-command-line-tools/), and for **Linux:** install the standard developement packages.
+
+#### Once that's done:
+
+Just open your **R** and type:
+```
+install.packages("bsvarSIGNs")
+```
+The developer's version of the package with the newest features can be installed by typing:
+```
 devtools::install_github("bsvars/bsvarSIGNs")
 ```
 
-# Example
+## Development
+
+The package is under intensive development. Your help is most welcome! Please, have a look at the [roadmap](https://github.com/bsvars/bsvarSIGNs/milestones) and [report a bug](https://github.com/bsvars/bsvarSIGNs/issues). Thank you!
+
+## Example
 
-A replication of Arias, Rubio-Ramírez and Waggoner (2018).
+A replication of [Arias, Rubio-Ramírez, & Waggoner (2018)](http://doi.org/10.3982/ECTA14468).
 
 ```r
 data(optimism)
@@ -47,7 +69,7 @@ irf            = compute_impulse_responses(posterior, horizon = 40)
 plot(irf, probability = 0.68)
 ```
 
-A replication of Antolín-Díaz and Rubio-Ramírez (2018).
+A replication of [Antolín-Díaz and Rubio-Ramírez (2018)](http://doi.org/10.1257/aer.20161852).
 
 ```r
 data(monetary)

README.md

@@ -1,24 +1,77 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
-<!-- badges: start -->
-
-[![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 -->
 
 # bsvarSIGNs
 
-Developing an R package for Bayesian Structural VARs identified by zero,
-sign and narrative restrictions.
-
-# Installation
+An **R** package for Bayesian Estimation of Structural Vector
+Autoregressions Identified by Sign, Zero, and Narrative Restrictions
 
-``` r
-devtools::install_github("bsvars/bsvarSIGNs")
-```
+<!-- badges: start -->
 
-# Example
+[![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 -->
 
-A replication of Arias, Rubio-Ramírez and Waggoner (2018).
+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)](http://doi.org/10.1162/REST_a_00483). The sign restrictions are
+implemented employing the methods outlined 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 code structure, objects, and workflows with the **R** package
+**bsvars** by [Woźniak
+(2024)](http://doi.org/10.32614/CRAN.package.bsvars), and they
+constitute an integrated toolset.
+
+## Installation
+
+#### The first time you install the package
+
+You must have a **cpp** compiler. Follow the instructions from [Section
+1.3. by Eddelbuettel & François
+(2023)](https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-FAQ.pdf).
+In short, for **Windows:** install
+[RTools](https://CRAN.R-project.org/bin/windows/Rtools/), for **macOS:**
+install [Xcode Command Line
+Tools](https://www.freecodecamp.org/news/install-xcode-command-line-tools/),
+and for **Linux:** install the standard developement packages.
+
+#### Once that’s done:
+
+Just open your **R** and type:
+
+    install.packages("bsvarSIGNs")
+
+The developer’s version of the package with the newest features can be
+installed by typing:
+
+    devtools::install_github("bsvars/bsvarSIGNs")
+
+## Development
+
+The package is under intensive development. Your help is most welcome!
+Please, have a look at the
+[roadmap](https://github.com/bsvars/bsvarSIGNs/milestones) and [report a
+bug](https://github.com/bsvars/bsvarSIGNs/issues). Thank you!
+
+## Example
+
+A replication of [Arias, Rubio-Ramírez, & Waggoner
+(2018)](http://doi.org/10.3982/ECTA14468).
 
 ``` r
 data(optimism)
@@ -36,7 +89,8 @@ irf            = compute_impulse_responses(posterior, horizon = 40)
 plot(irf, probability = 0.68)
 ```
 
-A replication of Antolín-Díaz and Rubio-Ramírez (2018).
+A replication of [Antolín-Díaz and Rubio-Ramírez
+(2018)](http://doi.org/10.1257/aer.20161852).
 
 ``` r
 data(monetary)