R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.

Assists in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) and error correction (VEC) models. Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) <doi:10.1561/0800000013> and Luetkepohl (2006, ISBN: 9783540262398).

Functions to prepare tidy objects from estimated models via 'BVAR'
    (see Kuschnig & Vashold, 2019 <doi:10.13140/RG.2.2.25541.60643>) and
    visualisation thereof. Bridges the gap between estimating models with 'BVAR'
    and plotting the results in a more sophisticated way with 'ggplot2' as well
    as passing them on in a tidy format.

Tools to model and forecast multivariate time series
    including Bayesian Vector heterogeneous autoregressive (VHAR) model
    by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>).
    'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.

An R interface to the Stark-Parker implementation of an
        algorithm for bounded-variable least squares

Implements the EM algorithm with one-step Gradient Descent method to estimate
             the parameters of the Block-Basu bivariate Pareto distribution with location
             and scale. We also found parametric bootstrap and asymptotic confidence 
             intervals based on the observed Fisher information of scale and shape parameters,
             and exact confidence intervals for location parameters. Details are in 
             Biplab Paul and Arabin Kumar Dey (2023) <doi:10.48550/arXiv.1608.02199>
             "An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto
             distribution with location and scale"; 
             E L Lehmann and George Casella (1998) <doi:10.1007/b98854> "Theory of Point Estimation";
             Bradley Efron and R J Tibshirani (1994) <doi:10.1201/9780429246593> 
             "An Introduction to the Bootstrap"; 
             A P Dempster, N M Laird and D B Rubin
             (1977) <www.jstor.org/stable/2984875> "Maximum Likelihood from Incomplete
             Data via the EM Algorithm".