Generic EM Algorithm

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Documentation for package ‘em’ version 1.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

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