Penalized Matrix-Normal Linear Discriminant Analysis
Fits the penalized matrix-normal model to be used for linear discriminant analysis with matrix-valued predictors.

person("Aaron J.", "Molstad", email = "amolstad@fredhutch.org",
role = c("aut", "cre"))
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>.

Model-Based Clustering via Matrix-Variate Mixture Models
c(
person(given = "Salvatore D.",
family = "Tomarchio",
role = "aut"),
person(given = "Michael P.B.",
family = "Gallaugher",
role = c("aut","cre"),
email = "michael_gallaugher@baylor.edu"),
person(given = "Antonio",
family = "Punzo",
role = "aut"),
person(given = "Paul D.",
family = "McNicholas",
role = "aut"))
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.
Salvatore D. Tomarchio [aut],
Michael P.B. Gallaugher [aut, cre],
Antonio Punzo [aut],
Paul D. McNicholas [aut]

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

R (>= 2.14.0), stats, utils, methods, Matrix (>= 1.0-1)

Modelling with Sparse and Dense Matrices

c(person("Julien", "Neves", email = "jmn252@cornell.edu", role = c("aut", "cre")), person("Jean-Marie", "Dufour", email = "jean-marie.dufour@mcgill.ca", role = c("aut")))
An implementation of the Monte Carlo techniques described in details by Dufour (2006) <doi:10.1016/j.jeconom.2005.06.007> and Dufour and Khalaf (2007) <doi:10.1002/9780470996249.ch24>. The two main features available are the Monte Carlo method with tie-breaker, mc(), for discrete statistics, and the Maximized Monte Carlo, mmc(), for statistics with nuisance parameters.
fUnitRoots, microbenchmark, boot, MASS, knitr, rmarkdown
GenSA, pso, GA, NMOF, scales, stats, graphics, utils
Julien Neves [aut, cre],
Jean-Marie Dufour [aut]