cstep                   C-Step of EM algorithm
em                      A Generic EM Algorithm
em.clogit               The em function for 'survival::clogit'.
em.default              The default em function
em.fitdist              The default em function
em.glmerMod             The em function for glmerMod
em.panelmodel           The em function for 'panelmodel' such as 'plm'.
estep                   This function performs an E-Step of EM
                        Algorithm.
fit.den                 Fit the density function for a fitted model.
fit.den.coxph           Fit the density for the survival::clogit
fit.den.fitdist         Fitting the density function using in
                        'fitdistrplus::fitdist()'
fit.den.glm             Fit the density function for a generalized
                        linear regression model.
fit.den.glmerMod        Fit the density function for a generalized
                        linear mixed effect model.
fit.den.gnm             Fit the density function for a generalized
                        non-linear regression model.
fit.den.lm              Fit the density function for a linear
                        regression model.
fit.den.multinom        Fit the density function for a multinomial
                        regression model.
fit.den.nnet            Fit the density function for a 'nnet' model.
fit.den.plm             Fit the density function for a panel regression
                        model.
flatten                 Flatten a data.frame or matrix by column or row
                        with its name. The name will be transformed
                        into the number of row/column plus the name of
                        column/row separated by '.'.
init.em                 Initialization of EM algorithm
init.em.hc              model-based agglomerative hierarchical
                        clustering
init.em.kmeans          K-mean initialization
init.em.random          Random initialization
init.em.random.weights
                        Random initialization with weights
init.em.sample5         Initialization using sampling 5 times.
logLik.em               This function computes logLik of EM Algorithm.
mstep                   M-Step of EM algorithm
mstep.concomitant       The mstep for the concomitant model.
mstep.concomitant.refit
                        The refit of for the concomitant model. This
                        section was inspired by Flexmix.
multi.em                Multiple run of EM algorithm
multi.em.default        Default generic for multi.em
plot.em                 Plot the fitted results of EM algorithm
predict.em              Predict the fitted finite mixture models
print.em                Print the 'em' object
print.summary.em        Print the 'summary.em' object
simbinom                Simulated Data from a logistic regression
simclogit               Simulated Data from a conditional logistic
                        regression
simreg                  Simulated Regression Data
sstep                   S-step of EM algorithm
summary.em              Summaries of fitted finite mixture models using
                        EM algorithm
vdummy                  Transform a factor variable to a matrix of
                        dummy variables
