Title: Mittag-Leffler Family of Distributions
Description: Implements the Mittag-Leffler function, distribution,
random variate generation, and estimation. Based on the Laplace-Inversion
Suggests: knitr, rmarkdown, testthat, animation, magrittr

person("Katharina", "Hees", email = "heeskatharina@gmail.com",
person("Peter", "Straka", email = "straka.ps@gmail.com",
person("Roberto", "Garrappa", email = "roberto.garrappa@uniba.it",

Authors@R: c(person("Serge", "Iovleff", role = c("aut","cre"), email =
Description: Algorithms and methods for estimating parametric mixture models with missing data.
Collate: 'global.R' 'MixAll.R' 'ClusterAlgo.R' 'ClusterInit.R'

Collate: 'global.R' 'ClusterAlgo.R' 'ClusterInit.R' 'ClusterStrategy.R'

Authors@R: c(person("Serge", "Iovleff", role = c("aut","cre"), email = "Serge.Iovleff@stkpp.org"))
Description: Algorithms and methods for estimating parametric mixture models for

Title: Clustering and Classification using Model-Based Mixture Models
Authors@R: c(person("Serge", "Iovleff", role = c("aut","cre"), email = "Serge.Iovleff@stkpp.org")
,person("Parmeet", "Bathia", role = c("ctb"), email = "Bathia.Parmeet@gmail.com")
Description: Algorithms and methods for model-based clustering and classification.
It supports various types of data: continuous, categorical and counting
and can handle mixed data of these types. It can fit Gaussian (with diagonal
covariance structure), gamma, categorical and Poisson models. The algorithms
also support missing values. This package can be used as an independent alternative to the
(not free) 'mixtcomp' software available at <https://massiccc.lille.inria.fr/>.
'LearnAlgo.R' 'Learners.R' 'MixAll.R' 'kmmNames.R' 'kmm.R'

also support missing values. This package can be used as an independent
alternative to the (not free) 'mixtcomp' software available at

Title: Model based clustering and classification using the mixture of
Author: Cristina Tortora, Ryan P. Browne, Brian C. Franczak and Paul D. McNicholas.
Description: Carries out model-based clustering using three different models. The models are all based on the  generalized hyperbolic distribution.The first model MGHD is the classical mixture of generalized hyperbolic distributions. The MGHFA is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The MCGHD, mixture of  coalesced generalized hyperbolic distributions is a new more flexible model.
Depends: Bessel, stats, MASS, mvtnorm, ghyp, numDeriv, R (>= 3.1.1)