Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering
Automatic layout for several plots in one figure
Best linear approximation with respect to the mean square error (theor...
B-spline basis
Plot a function together with its confidence/credible bands
Dirichlet distribution
Fitted profiles in the GLMM model
Generate all permutations of (1, ..., K)
Individual longitudinal profiles of a given variable
Discriminant analysis for longitudinal profiles based on fitted GLMM's
Discriminant analysis for longitudinal profiles based on fitted GLMM's
MCMC estimation of a (multivariate) generalized linear mixed model wit...
Data manipulation for the GLMM_MCMC function
Initial (RE)ML fits for the GLMM_MCMC function
Handle init.alpha or init2.alpha argument of GLMM_MCMC function
Handle init.b or init2.b argument of GLMM_MCMC function
Handle init.eps or init2.eps argument of GLMM_MCMC function
Handle prior.alpha argument of GLMM_MCMC function
Handle prior.eps argument of GLMM_MCMC function
Handle prior.eps argument of GLMM_MCMC function
Handle scale.b argument of GLMM_MCMC function
Wrapper to the GLMM_MCMC main simulation.
Moore-Penrose pseudoinverse of a squared matrix
Square root of a matrix
Multivariate normal distribution
Mixture of (multivariate) normal distributions
Multivariate Student t distribution
Chains for mixture parameters
Create MCMC chains derived from previously sampled values
Clustering based on the MCMC output of the mixture model
EM algorithm for a homoscedastic normal mixture
MCMC estimation of (multivariate) normal mixtures with possibly censor...
Data manipulation for the NMixMCMC function
Initial component allocations for the NMixMCMC function
Initial values of censored observations for the NMixMCMC function
Wrapper to the NMixMCMC main simulation.
Pairwise bivariate conditional densities: plug-in estimate
Univariate conditional densities: plug-in estimate
Discriminant analysis based on plug-in estimates from the mixture mode...
Pairwise bivariate densities: plug-in estimate
Marginal (univariate) densities: plug-in estimate
Marginal (univariate) predictive cumulative distribution function
Univariate conditional predictive cumulative distribution function
Pairwise bivariate conditional predictive densities
Univariate conditional predictive density
Discriminant analysis based on MCMC output from the mixture model
Pairwise bivariate predictive density
Marginal (univariate) predictive density
Pseudo goodness-of-fit test for a normal mixture model
Re-labeling the MCMC output of the mixture model
Argument manipulation for the NMixRelabel functions
Summary for the mixture components
Plot computed pairwise bivariate conditional densities (plug-in estima...
Plot computed univariate conditional densities (plug-in estimate)
Plot computed marginal pairwise bivariate densities (plug-in estimate)
Plot computed marginal predictive densities
Plot computed marginal predictive cumulative distribution functions
Plot computed univariate conditional predictive cumulative distributio...
Plot computed predictive pairwise bivariate conditional densities
Plot computed univariate conditional predictive densities
Plot computed marginal pairwise bivariate predictive densities
Plot computed marginal predictive densities
Plot individual longitudinal profiles
Random rotation matrix
Sample a pair (with replacement)
Conversion of a symmetric matrix stored in a packed format (lower tria...
Posterior summary statistics for a difference of two quantities
Truncated multivariate normal distribution
Truncated normal distribution
Traceplots for selected parameters
Wishart distribution
Transform fitted distribution of Y=trans(T) into distribution of T
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. The package is primarily related to the publications Komárek (2009, Comp. Stat. and Data Anal.) <doi:10.1016/j.csda.2009.05.006> and Komárek and Komárková (2014, J. of Stat. Soft.) <doi:10.18637/jss.v059.i12>. It also implements methods published in Komárek and Komárková (2013, Ann. of Appl. Stat.) <doi:10.1214/12-AOAS580>, Hughes, Komárek, Bonnett, Czanner, García-Fiñana (2017, Stat. in Med.) <doi:10.1002/sim.7397>, Jaspers, Komárek, Aerts (2018, Biom. J.) <doi:10.1002/bimj.201600253> and Hughes, Komárek, Czanner, García-Fiñana (2018, Stat. Meth. in Med. Res) <doi:10.1177/0962280216674496>.