bsvars💘 The HEX logo for the new R package bvars is fully reproducible using R!
Just follow the script at https://github.com/bsvars/hex/tree/main/bvars
Step 1: generate forcasts
Step 2: generate 3D plot
Step 3: generate hexagonal logo
💘 the new R package bvars includes state-of-the-art forecasting models 🚀
🔭 They are most useful for macroeconomic and financial forecasting!
🤖 the range of models is already great! ...and more to come!
💘 bvars: Bayesian Forecasting with Large Vector Autoregressions
🌐 https://bsvars.org/bvars/
🌐 https://cran.r-project.org/package=bvars
#bvars #bsvars #rstats #foss
🌐 https://bsvars.org/bvars/
🌐 https://cran.r-project.org/package=bvars
#bvars #bsvars #rstats #foss
💘 Our new R package bvars is for Bayesian Forecasting with Large Vector Autoregressions
💘 It's blazingly fast and has just landed on CRAN!
💘 Hey hey! Our new package written by Rui, Andres and Tomasz has just landed on CRAN!
💘 bvars is the R package for Bayesian Forecasting with Large Vector Autoregressions
💘 It's for state-of-the-art Bayesian VARs and it's blazingly fast!
bvars: Bayesian Forecasting with Large Vector Autoregressions
Provides fast and efficient procedures for Bayesian estimation and forecasting using state-of-the-art Vector Autoregressions. This package includes the model proposed by Chan (2020) <<a href="https://doi.org/10.1080%2F07350015.2018.1451336" target="_top">doi:10.1080/07350015.2018.1451336</a>>, that is, a Bayesian Vector Autoregression with Minnesota priors and a flexible structure of the error term specification. The latter includes: conditional multivariate normal or Student’s t distributions, as well as homoskedastic or heteroskedastic specifications with a common volatility modelled by centred or non-centred Stochastic Volatility. Additionally, the package facilitates predictive analyses using density forecasting and forecast-error variance decompositions. All this is complemented by simple workflows, useful plots and summary functions, and comprehensive documentation. The 'bvars' package aligns with R packages 'bsvars' by Woźniak (2024) <<a href="https://doi.org/10.32614%2FCRAN.package.bsvars" target="_top">doi:10.32614/CRAN.package.bsvars</a>>, 'bsvarSIGNs' by Wang & Woźniak (2025) <<a href="https://doi.org/10.32614%2FCRAN.package.bsvarSIGNs" target="_top">doi:10.32614/CRAN.package.bsvarSIGNs</a>>, and 'bpvars' by Woźniak (2025) <<a href="https://doi.org/10.32614%2FCRAN.package.bpvars" target="_top">doi:10.32614/CRAN.package.bpvars</a>> regarding objects, workflows, and code structure, and they constitute an integrated toolset.
✨Ha! Have a look at that! That's Adam's seminar about the bsvarSIGNs package after winning Di Cook Award for open-source research software for the Statistical Society of Australia! And we discovered it only now! 🌟
Di Cook Award: Promoting Open Source Statistical Software Development
🚀 Aww! It's so good when the promise of open source delivers and when the community starts contributing. Bruno Cavalcante found a small but impossible-to-find-otherwise typo in my R package bsvars 💝 submitted a PR, and it's all fixed now! Thanks, Bruno! ✨ https://github.com/bsvars/bsvars/pull/136
💛❤️ Awww It's finally out! Our presentation on bsvars.org design concept in which @adamwang15.bsky.social talks about the main design features for our packages!
bsvars.org: Bayesian Structural Vector Autoregressions - Adam Wang
Tomasz Woźniak💖 Our newest working paper is now available on arxiv: https://doi.org/10.48550/arXiv.2603.16035
💝 We propose a new volatility model for structural vector autoregressions!
🎀 And it's great for even more precise estimation, homoskedasticity verification, forecasting, and structural analyses!
❤️💛 AND the models are all implemented in my R package bsvars! Enjoy the reading and fast computations!
