pengls

DOI: 10.18129/B9.bioc.pengls    

This package is for version 3.15 of Bioconductor; for the stable, up-to-date release version, see pengls.

Fit Penalised Generalised Least Squares models

Bioconductor version: 3.15

Combine generalised least squares methodology from the nlme package for dealing with autocorrelation with penalised least squares methods from the glmnet package to deal with high dimensionality. This pengls packages glues them together through an iterative loop. The resulting method is applicable to high dimensional datasets that exhibit autocorrelation, such as spatial or temporal data.

Author: Stijn Hawinkel [cre, aut]

Maintainer: Stijn Hawinkel <stijn.hawinkel at psb.ugent.be>

Installation

To install this package, start R (version "4.2") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("pengls")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

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

browseVignettes("pengls")

 

HTML	R Script	Vignette of the pengls package
PDF		Reference Manual
Text		NEWS

Details

biocViews	Regression, Software, TimeCourse, Transcriptomics
Version	1.2.0
In Bioconductor since	BioC 3.14 (R-4.1) (1 year)
License	GPL-2
Depends	R (>= 4.0)
Imports	glmnet, nlme, stats, BiocParallel
LinkingTo
Suggests	knitr, rmarkdown, testthat
SystemRequirements
Enhances
URL
BugReports	https://github.com/sthawinke/pengls
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

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

Source Package	pengls_1.2.0.tar.gz
Windows Binary	pengls_1.2.0.zip
macOS Binary (x86_64)	pengls_1.2.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/pengls
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/pengls
Package Short Url	https://bioconductor.org/packages/pengls/
Package Downloads Report	Download Stats