# Generic EM Algorithm

## 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