Provides Bayesian estimation and forecasting of dynamic panel data using 
  Bayesian Panel Vector Autoregressions with hierarchical prior distributions. 
  The models include country-specific VARs that share a global prior 
  distribution that extend the model by Jarociński (2010) <doi:10.1002/jae.1082>. 
  Under this prior expected value, each country's system follows 
  a global VAR with country-invariant parameters. Further flexibility is 
  provided by the hierarchical prior structure that retains the Minnesota prior 
  interpretation for the global VAR and features estimated prior covariance 
  matrices, shrinkage, and persistence levels. Bayesian forecasting is developed 
  for models including exogenous variables, allowing conditional forecasts given 
  the future trajectories of some variables and restricted forecasts assuring 
  that rates are forecasted to stay positive and less than 100. The package 
  implements the model specification, estimation, and forecasting routines, 
  facilitating coherent workflows and reproducibility. It also includes automated
  pseudo-out-of-sample forecasting and computation of forecasting performance 
  measures. Beautiful plots, 
  informative summary functions, and extensive documentation complement all 
  this. An extraordinary computational speed is achieved thanks to employing 
  frontier econometric and numerical techniques and algorithms written in 'C++'. 
  The 'bpvars' package is aligned regarding objects, workflows, and code 
  structure with the 'R' packages 'bsvars' by 
  Woźniak (2024) <doi:10.32614/CRAN.package.bsvars> and 'bsvarSIGNs' by 
  Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they 
  constitute an integrated toolset. Copyright: 2025 International Labour 
  Organization.

Package provides functions for estimation and inference in Bayesian quantile regression with ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm. The summary output presents the posterior mean, posterior standard deviation, 95% credible intervals, and the inefficiency factors along with the two model comparison measures – logarithm of marginal likelihood and the deviance information criterion (DIC). The package also provides functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models.
    Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, 11(1): 1-24 <doi: 10.1214/15-BA939>.
    Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and Probability Letters, 54(4): 437–447 <doi: 10.1016/S0167-7152(01)00124-9>.
    Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica, 46(1): 33-50 <doi: 10.2307/1913643>.
    Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90(432):1313–1321, 1995. <doi: 10.1080/01621459.1995.10476635>.
    Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. <doi: 10.1198/016214501750332848>.