PEMM: A Penalized EM algorithm incorporating missing-data mechanism
Version 1.0

This package provides functions to perform multivariate Gaussian parameter estimation based on data with abundance-dependent missingness. It implements a penalized Expectation-Maximization (EM) algorithm. The package is tailored for but not limited to proteomics data applications, in which a large proportion of the data are often missing-not-at-random with lower values (or absolute values) more likely to be missing.

Browse man pages Browse package API and functions Browse package files

AuthorLin Chen <lchen@health.bsd.uchicago.edu> and Pei Wang <pwang@fhcrc.org>
Date of publication2014-01-25 00:37:55
MaintainerLin Chen <lchen@health.bsd.uchicago.edu>
LicenseGPL
Version1.0
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("PEMM")

Man pages

PEMM: A penalized EM algorithm incorporating missing-data mechanism...
PEMM_fun: A penalized EM algorithm incorporating missing-data mechanism...
sim_dat: A simulated multivariate data

Functions

PEMM Man page
PEMM_fun Man page Source code
sim_dat Man page

Files

NAMESPACE
data
data/sim_dat.rda
R
R/PEMM_fun.R
MD5
DESCRIPTION
man
man/PEMM_fun.Rd
man/PEMM.Rd
man/sim_dat.Rd
PEMM documentation built on May 19, 2017, 6:17 p.m.