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

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.

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

graphics, stats, methods, splines, fastGHQuad, mnormt,
parallel, coda

c( person(given = c("Georgi", "N."),
family = "Boshnakov",
role = c("aut", "cre"),
email = "georgi.boshnakov@manchester.ac.uk"),
person(given = "Davide",
family = "Ravagli",
role = "aut", email = "davide.ravagli@manchester.ac.uk") )
Model time series using mixture autoregressive (MAR)
models.  Implemented are frequentist (EM) and Bayesian
methods for estimation, prediction and model
evaluation. See Wong and Li (2002)
<doi:10.1111/1467-9868.00222>, Boshnakov (2009)
<doi:10.1016/j.spl.2009.04.009>), and the extensive
references in the documentation.
stats, graphics, utils, stats4, BB, combinat, timeDate,
fGarch, Rdpack (>= 0.7), gbutils (>= 0.3-1), MCMCpack, e1071,
permute, mvtnorm
raggedCoef.R raggedCoefS.R mixComp.R mixAR.R mixARcalc.R
mixutil.R dist.R predict.R mix_se.R obs_info_matrix.R
samp_functions.R bayes_mixAR.R label_switch.R Choose_pk.R
marg_loglik.R mixSARfit.R mixARreg.R fit_mixARreg.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
exmodels.R mixARsim.R
Georgi N. Boshnakov [aut, cre],
Davide Ravagli [aut]

raggedCoef.R raggedCoefS.R mixComp.R mixAR.R mixARcalc.R
mixutil.R dist.R predict.R mix_se.R obs_info_matrix.R
samp_functions.R bayes_mixAR.R label_switch.R Choose_pk.R
marg_loglik.R mixSARfit.R mixARreg.R fit_mixARreg.R
raggedCoefV.R mixVAR.R fit_mixVAR.R cond_loglikV.R mixVAR_sim.R
tsdiag.R mixAR_diag.R devel.R em0.R emGaussian.R emgen.R
00marmath.R exmodels.R mixARsim.R