Posterior sampling and inference for Bayesian Poisson regression models. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2023) <doi:10.1080/10618600.2022.2123337>.

Bayesian models to estimate causal effects of biological 
    treatments on time-to-event endpoints in clinical trials with principal
    strata defined by the occurrence of antidrug antibodies. 
    The methodology is based on Frangakis and Rubin (2002) 
    <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997)
    <doi:10.1214/aos/1034276631>, and here adapted to a specific 
    time-to-event setting.

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.