This package is considered a duplicate. The official version of this package is found at:https://wudongjie.r-universe.dev/em

Package: em 1.0.0

Dongjie Wu

em: Generic EM Algorithm

A generic function for running the Expectation-Maximization (EM) algorithm within a maximum likelihood framework, based on Dempster, Laird, and Rubin (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x> is implemented. It can be applied after a model fitting using R's existing functions and packages.

Authors:Dongjie Wu [aut, cre, cph]

em_1.0.0.tar.gz
em_1.0.0.tar.gz(r-4.5-noble)em_1.0.0.tar.gz(r-4.4-noble)
em_1.0.0.tgz(r-4.4-emscripten)em_1.0.0.tgz(r-4.3-emscripten)
em.pdf |em.html✨
em/json (API)

# Install 'em' in R:

install.packages('em', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/wudongjie/em/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

simbinom - Simulated Data from a logistic regression
simclogit - Simulated Data from a conditional logistic regression
simreg - Simulated Regression Data

On CRAN:

openblas cpp openmp

2.00 score 181 downloads 8 exports 38 dependencies

Last updated 2 years agofrom:28b053c3df. Checks:1 OK, 2 NOTE. Indexed: no.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 12 2025
R-4.5-linux-x86_64	NOTE	Mar 12 2025
R-4.4-linux-x86_64	NOTE	Mar 12 2025

Exports:em estep fit.den flatten init.em mstep multi.em vdummy

Dependencies:bdsmatrix cli collapse digest dplyr fansi Formula generics glue lattice lifecycle lmtest magrittr MASS Matrix maxLik mclust miscTools nlme nnet numDeriv pillar pkgconfig plm R6 rbibutils Rcpp RcppArmadillo Rdpack rlang sandwich survival tibble tidyselect utf8 vctrs withr zoo

em: A Generic Function of the EM Algorithm for Finite Mixture Models in R

Rendered fromem_intro.Rnwusingutils::Sweaveon Mar 12 2025.

Last update: 2023-01-11
Started: 2023-01-11

Citation

To cite package ‘em’ in publications use:

Wu D (2023). em: Generic EM Algorithm. R package version 1.0.0, https://CRAN.R-project.org/package=em.

Corresponding BibTeX entry:

  @Manual{,
    title = {em: Generic EM Algorithm},
    author = {Dongjie Wu},
    year = {2023},
    note = {R package version 1.0.0},
    url = {https://CRAN.R-project.org/package=em},
  }

Readme and manuals

Help Manual

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