To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("pmm")

In most cases, you don't need to download the package archive at all.

pmm

DOI: 10.18129/B9.bioc.pmm    

Parallel Mixed Model

Bioconductor version: Release (3.5)

The Parallel Mixed Model (PMM) approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines.

Author: Anna Drewek

Maintainer: Anna Drewek <adrewek at stat.math.ethz.ch>

Citation (from within R, enter citation("pmm")):

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("pmm")

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("pmm")

 

PDF R Script User manual for R-Package PMM
PDF   Reference Manual
Text   NEWS

Details

biocViews Regression, Software, SystemsBiology
Version 1.8.0
In Bioconductor since BioC 3.1 (R-3.2) (2.5 years)
License GPL-3
Depends R (>= 2.10)
Imports lme4, splines
LinkingTo
Suggests
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Package Source pmm_1.8.0.tar.gz
Windows Binary pmm_1.8.0.zip
Mac OS X 10.11 (El Capitan) pmm_1.8.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/pmm
Package Short Url http://bioconductor.org/packages/pmm/
Package Downloads Report Download Stats

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