The Macroeconomics-at-Risk (MaR) approach is based on a two-step semi-parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the MaR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.

A rich hierarchy of sparse and dense matrix classes,
	including general, symmetric, triangular, and diagonal matrices
	with numeric, logical, or pattern entries.  Efficient methods for
	operating on such matrices, often wrapping the 'BLAS', 'LAPACK',
	and 'SuiteSparse' libraries.

A collection of functions to support matrix calculations
        for probability, econometric and numerical analysis. There are
        additional functions that are comparable to APL functions which
        are useful for actuarial models such as pension mathematics.
        This package is used for teaching and research purposes at the
        Department of Finance and Risk Engineering, New York
        University, Polytechnic Institute, Brooklyn, NY 11201.
        Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326.
        Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507.
        Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563.

Compute correlation, association, agreement, and reliability
    measures for small to high-dimensional datasets through a consistent
    matrix-oriented interface. Supports classical correlations (Pearson,
    Spearman, Kendall, Chatterjee's rank correlation), distance correlation, partial correlation with
    regularised estimators, shrinkage correlation for p >= n settings, robust
    correlations including biweight mid-correlation, percentage-bend,
    Winsorized, and skipped correlation, latent-variable methods for binary
    and ordinal data, pairwise and overall intraclass correlation for wide
    data, repeated-measures correlation, and agreement/reliability analyses
    based on Cohen's kappa, weighted kappa, multi-rater kappa, Gwet's AC1/AC2,
    Krippendorff's alpha, Bland-Altman methods, Lin's concordance correlation
    coefficient, Poisson GLMM concordance for count data, and
    repeated-measures intraclass/concordance correlation.
    Implemented with optimized C++ backends using BLAS/OpenMP and memory-aware
    symmetric updates, and returns standard R objects with print/summary/plot
    methods plus optional Shiny viewers for matrix inspection. Methods based
    on Ledoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4>;
    high-dimensional shrinkage covariance estimation
    <doi:10.2202/1544-6115.1175>; Lin (1989) <doi:10.2307/2532051>; Wilcox
    (1994) <doi:10.1007/BF02294395>; Wilcox (2004)
    <doi:10.1080/0266476032000148821>; Hayes and Krippendorff (2007)
    <doi:10.1080/19312450709336664>; weighted repeated-measures correlation by
    Kondo et al. (2025) <doi:10.1002/sim.70046>.