PEMM: A Penalized EM algorithm incorporating missing-data mechanism

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.

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

View on CRAN

Files

PEMM
PEMM/NAMESPACE
PEMM/data
PEMM/data/sim_dat.rda
PEMM/R
PEMM/R/PEMM_fun.R
PEMM/MD5
PEMM/DESCRIPTION
PEMM/man
PEMM/man/PEMM_fun.Rd PEMM/man/PEMM.Rd PEMM/man/sim_dat.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.