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 and other association matrices from
    small to high-dimensional datasets with relative simple functions and
    sensible defaults. Includes options for shrinkage and robustness to improve
    results in noisy or high-dimensional settings (p >= n), plus convenient
    print/plot methods for inspection. Implemented with optimised C++ backends
    using BLAS/OpenMP and memory-aware symmetric updates. Works with base
    matrices and data frames, returning standard R objects via a consistent S3
    interface. Useful across genomics, agriculture, and machine-learning
    workflows. Supports Pearson, Spearman, Kendall, distance correlation,
    partial correlation, and robust biweight mid-correlation; Bland–Altman
    analyses and Lin's concordance correlation coefficient (including
    repeated-measures extensions). Methods based on Ledoit and Wolf (2004)
    <doi:10.1016/S0047-259X(03)00096-4>; Schäfer and Strimmer (2005)
    <doi:10.2202/1544-6115.1175>; Lin (1989) <doi:10.2307/2532051>.