Inlay

See our 𝗥 packages 𝗯𝘀𝘃𝗮𝗿𝘀, 𝗯𝘀𝘃𝗮𝗿𝗦𝗜𝗚𝗡𝘀, 𝗯𝗽𝘃𝗮𝗿𝘀, 𝗯𝘃𝗮𝗿𝘀, and 𝗦𝘁𝗲𝗮𝗹𝗟𝗶𝗸𝗲𝗕𝗮𝘆𝗲𝘀 developed using frontier econometric methods and 𝗖++ code by @tomaszwozniak.bsky.social and @adamwang15.bsky.social https://bsvars.org/ #bsvars #bsvarSIGNs #bpvars #bvars #rstats

💘 bvars: Bayesian Forecasting with Large Vector Autoregressions 🌐 bsvars.org/bvars/ 🌐 cran.r-project.org/package=bvars #bvars #bsvars #rstats #foss

💛❤️ 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! www.youtube.com/watch?v=Gmd7... #bsvars #bsvarSIGNs #rstats

✨Ha! Have a look at that! That's @adamwang15.bsky.social seminar about the bsvarSIGNs package after winning Di Cook Award for open-source research software for the Statistical Society of Australia! 🌟 @visnut.bsky.social www.youtube.com/watch?v=0Uqw... #rstats #bsvarSIGNs #bsvars

💘 Our new R package bvars is for Bayesian Forecasting with Large Vector Autoregressions 💘 It's blazingly fast and has just landed on CRAN! 🌐 cran.r-project.org/package=bvars #bvars #bsvars #rstats

🚀 Aww! It's so good when the promise of open source delivers and when the community starts contributing. Bruno 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! ✨ github.com/bsvars/bsvar... #bsvars #foss #rstats

💘 interested in super precise and reliable forecasts? The new R package bvars is the way to go! 👾 great tools for forecasting! 👽 super fast routines! 👻 models proven best at forecasting 🌐 cloud.r-project.org/package=bvars (where's the astronaut emoji when you need it?) #bvars #rstats #forecasting

💘 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! 🌐 cran.r-project.org/package=bvars #bvars #bsvars #rstats

New on CRAN: bvars (1.0). View at https://CRAN.R-project.org/package=bvars

💘 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! 🌐 bsvars.org/bvars/ #bvars #bsvars #rstats #macro #fin #forecasting

💘 The HEX logo for the new R package bvars is fully reproducible using R! Just follow the script at github.com/bsvars/hex/t... Step 1: generate forcasts Step 2: generate 3D plot Step 3: generate hexagonal logo #bvars #bsvars #rstats #hexSticker #3Dplot

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.

Loop using 's' but accessing 'S' (constant = n draws): S_T[,,s] = object$posterior$xi_cpp[S,1][[1]][,T,] # BUG: S -> s

YouTube video by International Institute of Forecasters

YouTube video by Statistical Society of Australia