Title: Multivariate Normal Mixture Models and Mixtures of Generalized
Linear Mixed Models Including Model Based Clustering
Depends: R (>= 3.0.0), colorspace, lme4 (>= 1.0)
Imports: splines, fastGHQuad, mnormt, parallel, coda
Description: This package contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.

Imports: splines, lme4, mnormt, parallel

Imports: splines, lme4 (>= 1.0), fastGHQuad, mnormt, parallel

Imports: splines, fastGHQuad, mnormt, parallel

Description: Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.

Imports: graphics, stats, splines, fastGHQuad, mnormt, parallel, coda

Imports: graphics, stats, methods, splines, fastGHQuad, mnormt,

role = "aut", email = "davide.ravagli@manchester.ac.uk") )
Description: Model time series using mixture autoregressive (MAR)
models.  Implemented are frequentist (EM) and Bayesian
methods for estimation, prediction and model
Imports: stats, graphics, utils, stats4, BB, combinat, timeDate,
fGarch, Rdpack (>= 0.7), gbutils (>= 0.3-1), MCMCpack, e1071,
Collate: raggedCoef.R raggedCoefS.R mixComp.R mixAR.R mixARcalc.R
mixutil.R dist.R predict.R mix_se.R obs_info_matrix.R
raggedCoefV.R mixVAR.R fit_mixVAR.R cond_loglikV.R mixVAR_sim.R
mixAR_diag.R devel.R em0.R emGaussian.R emgen.R 00marmath.R

tsdiag.R mixAR_diag.R devel.R em0.R emGaussian.R emgen.R

Title: Estimation of the Order of Mixture Distributions
Description: Methods for estimating the order of a mixture model. The approaches considered are
based on the following papers (the extended list of references is available in the vignette):
1. Dacunha-Castelle, Didier, and Elisabeth Gassiat. The estimation of the order of a mixture model. Bernoulli 3, no. 3 (1997): 279-299. <https://projecteuclid.org/download/pdf_1/euclid.bj/1177334456>.
2. Woo, Mi-Ja, and T. N. Sriram. Robust estimation of mixture complexity. Journal of the American Statistical Association 101, no. 476 (2006): 1475-1486. <doi:10.1198/016214506000000555>.
3. Woo, Mi-Ja, and T. N. Sriram. Robust estimation of mixture complexity for count data. Computational statistics & data analysis 51, no. 9 (2007): 4379-4392. <doi:10.1016/j.csda.2006.06.006>.
4. Umashanger, T., and T. N. Sriram. L2E estimation of mixture complexity for count data. Computational statistics & data analysis 53, no. 12 (2009): 4243-4254. <doi:10.1016/j.csda.2009.05.013>.
5. Karlis, Dimitris, and Evdokia Xekalaki. On testing for the number of components in a mixed Poisson model. Annals of the Institute of Statistical Mathematics 51, no. 1 (1999): 149-162. <doi:10.1023/A:1003839420071>.
6. Cutler, Adele, and Olga I. Cordero-Brana. Minimum Hellinger Distance Estimation for Finite Mixture Models. Journal of the American Statistical Association 91, no. 436 (1996): 1716-1723. <doi:10.2307/2291601>.
Authors@R: c(person(given = "Anja", family = "Weigel", role = "aut", email = "anja.weigel@yahoo.de"),
person(given = "Yulia", family = "Kulagina", role = c("aut", "cre"), email = "yulia.kulagina@stat.math.ethz.ch"),
person(given = "Fadoua", family = "Balabdaoui", role = c("aut", "ths"), email = "fadoua.balabdaoui@stat.math.ethz.ch"),
person(given = "Lilian", family = "Mueller", role = "ctb", email = "lilian.mueller@gmail.com"),
person(given = "Martin", family = "Maechler", role = "ctb", comment = c("package 'nor1mix' as model", ORCID = "0000-0002-8685-9910"))
Imports: cluster, boot, expm, matrixcalc, Rsolnp, kdensity
Martin Maechler [ctb] (package 'nor1mix' as model,