Constructs the normalized Laplacian matrix of a square matrix, returns the eigenvectors (singular vectors) and visualization of normalized Laplacian map.

Fits the penalized matrix-normal model to be used for linear discriminant analysis with matrix-valued predictors. For a description of the method, see Molstad and Rothman (2018) <doi:10.1080/10618600.2018.1476249>.

Implements finite mixtures of matrix-variate contaminated normal distributions via expectation conditional-maximization algorithm for model-based clustering, as described in Tomarchio et al.(2020) <arXiv:2005.03861>. One key advantage of this model is the ability to automatically detect potential outlying matrices by computing their a posteriori probability of being typical or atypical points. Finite mixtures of matrix-variate t and matrix-variate normal distributions are also implemented by using expectation-maximization algorithms.

Modelling with sparse and dense 'Matrix' matrices, using
  modular prediction and response module classes.

Provides functions for row-reducing and inverting matrices with
    entries in many of the finite fields (those with a prime number of 
    elements). With this package, users will be able to find the reduced row 
    echelon form (RREF) of a matrix and calculate the inverse of a (square, 
    invertible) matrix.

Computes densities, probabilities, and random deviates of the Matrix Normal (Pocuca et al. (2019) <doi:10.48550/arXiv.1910.02859>). Also includes simple but useful matrix functions. See the vignette for more information.

A simple and the early stage package for matrix profile based on the paper of Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh (2016) <DOI:10.1109/ICDM.2016.0179>. This package calculates all-pairs-similarity for a given window size for time series data.

This is the core functions needed by the 'tsmp' package.  The
    low level and carefully checked mathematical functions are here.
    These are implementations of the Matrix Profile concept that was
    created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.