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.

Computation and visualization of matrix correlation coefficients.
    The main method is the Similarity of Matrices Index, while various related
    measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2, adjusted RV, Rozeboom's
    linear correlation and Coxhead's coefficient are included
    for comparison and flexibility.

The user must supply a matrix filled with similarity values. The software will search for significant differences between similarity values at different hierarchical levels. The algorithm will return a Loess-smoothed plot of the similarity values along with the inflection point, if there are any. There is the option to search for an inflection point within a specified range. The package also has a function that will return the matrix components at a specified cutoff. References: Mullner. <ArXiv:1109.2378>; Cserhati, Carter. (2020, Journal of Creation 34(3):41-50), <https://dl0.creation.com/articles/p137/c13759/j34-3_64-73.pdf>.

Tools for phase-type distributions including the following variants:
    continuous, discrete, multivariate, in-homogeneous, right-censored, and regression. 
    Methods for functional evaluation, simulation and estimation using the 
    expectation-maximization (EM) algorithm are provided for all models.
    The methods of this package are based on the following references.
    Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm,
    Olsson, M. (1996). Estimation of phase-type distributions from censored data,
    Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>,
    Albrecher, H., Bladt, M., & Yslas, J. (2022) <doi:10.1111/sjos.12505>,
    Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>,
    Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>,
    Bladt, M. (2022) <doi:10.1017/asb.2021.40>,
    Bladt, M. (2023) <doi:10.1080/10920277.2023.2167833>,
    Albrecher, H., Bladt, M., & Mueller, A. (2023) <doi:10.1515/demo-2022-0153>,
    Bladt, M. & Yslas, J. (2023) <doi:10.1016/j.insmatheco.2023.02.008>.