bsvars 2.0.0

Published on 23 October 2023

  1. Included Imports from package stochvol
  2. Posterior computations for:
  1. Implemented faster samplers based on random number generators from armadillo via RcppArmadillo #7
  2. The estimate_bsvar* functions now also normalise the output w.r.t. to a structural matrix with positive elements on the main diagonal #9
  3. Changed the order of arguments in the estimate_bsvar* functions with posterior first to facilitate workflows using the pipe |> #10
  4. Include citation info for the package #12
  5. Corrected sampler for AR parameter of the SV equations #19
  6. Added samplers from joint predictive densities #15
  7. A new centred Stochastic Volatility heteroskedastic process is implemented #22
  8. Introduced a three-level local-global equation-specific prior shrinkage hierarchy for the parameters of matrices and #34
  9. Improved checks for correct specification of arguments S and thin of the estimate method as enquired by @mfaragd #33
  10. Improved the ordinal numerals presentation for thinning in the progress bar #27

bsvars 1.0.0

Published on 1 September 2022

  1. repo transferred from GitLab to GitHub
  2. repository is made public
  3. version to be premiered on CRAN


  1. Added a new progress bar for the estimate_bsvar* functions
  2. Developed R6 classes for model specification and posterior outcomes; model specification includes sub-classes for priors, identifying restrictions, data matrices, and starting values
  3. Added a complete package documentation
  4. Written help files
  5. Developed tests for MCMC reproducibility
  6. Included sample data


  1. cpp scripts are imported, compile, and give no Errors, Warnings, or Notes
  2. R wrappers for the functions are fully operating
  3. full documentation describing package and functions’ functionality [sic!]