Mixture Models for Clustering and Classification
An implementation of all 14 Gaussian parsimonious
clustering models (GPCMs) for model-based clustering and
model-based classification.

Ryan P. Browne, Aisha ElSherbiny and Paul D. McNicholas

Finite Gaussian Mixture Models for Clustering and Classification
Orphaned and corrected on 2020-12-12

Failed to install on modern macOS

c(person("Nik", "Pocuca", role=c("aut")), person("Ryan P.", "Browne", role = c("aut")), person("Paul D.", "McNicholas", role = c("aut", "cre"), email = "mcnicholas@math.mcmaster.ca"))
Nik Pocuca [aut],
Ryan P. Browne [aut],
Paul D. McNicholas [aut, cre]
An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.

An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.

c(person("Nik", "Pocuca", role=c("aut"), comment = c(ORCID = "0000-0001-8365-0751")),
person("Ryan P.", "Browne", role = c("aut"), comment = c(ORCID = "0000-0003-4543-0218")),
person("Paul D.", "McNicholas", role = c("aut", "cre"), email = "mcnicholas@math.mcmaster.ca", comment = c(ORCID = "0000-0002-2482-523X")))

person("James", "Melville", email = "jlmelville@gmail.com",
role = c("aut", "cre"))
Optimization algorithms implemented in R, including
conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the
limited memory BFGS (L-BFGS) methods. Most internal parameters can be set
through the call interface. The solvers hold up quite well for
higher-dimensional problems.