Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism

Documentation for package ‘EMMIXSSL’ version 1.1.1

Classifier_Bayes	Classifier based on Bayes rule
cov2vec	Transform a variance matrix into a vector
discriminant_beta	Discriminant function
EMMIXSSL	Fitting Gaussian mixture models
errorrate	Error rate of the Bayes rule for two-class Gaussian homoscedastic model
gastrodata	Gastrointestinal dataset
gastro_label_binary	Gastrointestinal binary labels
gastro_label_trinary	Gastrointestinal trinary labels
get_clusterprobs	Posterior probability
get_entropy	Shannon entropy
initialvalue	Initial values for ECM
list2par	Transfer a list into a vector
loglk_full	Full log-likelihood function
loglk_ig	Log likelihood for partially classified data with ingoring the missing mechanism
loglk_miss	Log likelihood function formed on the basis of the missing-label indicator
logsumexp	log summation of exponential function
makelabelmatrix	Label matrix
neg_objective_function	Negative objective function for EMMIXSSL
normalise_logprob	Normalize log-probability
par2list	Transfer a vector into a list
pro2vec	Transfer a probability vector into a vector
rlabel	Generation of a missing-data indicator
rmix	Normal mixture model generator.
vec2cov	Transform a vector into a matrix
vec2pro	Transfer an informative vector to a probability vector