PPLasso-package {PPLasso}

Prognostic Predictive Lasso for Biomarker Selection

Description

We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper: Zhu et al. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso. BMC Bioinformatics. 2023 Jan 23;24(1):25.

Details

The DESCRIPTION file:

Package:	PPLasso
Type:	Package
Title:	Prognostic Predictive Lasso for Biomarker Selection
Version:	2.0
Date:	2023-02-26
Authors@R:	c(person("Wencan", "Zhu", email = "[e-mail]", role = c("aut", "cre")), person("Celine","Levy-Leduc", email="[e-mail]", role = "ctb"), person("Nils", "Ternes", email="[e-mail]", role = "ctb"))
Author:	Wencan Zhu [aut, cre], Celine Levy-Leduc [ctb], Nils Ternes [ctb]
Maintainer:	Wencan Zhu <[e-mail]>
Description:	We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper: Zhu et al. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso. BMC Bioinformatics. 2023 Jan 23;24(1):25.
License:	GPL-2
Imports:	genlasso, ggplot2, cvCovEst, glmnet, MASS
VignetteBuilder:	knitr
Suggests:	knitr, rmarkdown
NeedsCompilation:	no
Packaged:	2023-02-26 08:33:08 UTC; Wencan
Depends:	R (>= 3.5.0)

Index of help topics:

Correction1Vect         Correction on two vectors
Correction2Vect         Correction on two vectors
PPLasso-package         Prognostic Predictive Lasso for Biomarker
                        Selection
ProgPredLasso           Identification of prognostic and predictive
                        biomarkers
top                     Thresholding to 0
top_thresh              Thresholding to a given threshold of the
                        smallest values

This package provide usufull tool for the identification of prognostics and predictive biomarkers.

Author(s)

Wencan Zhu [aut, cre], Celine Levy-Leduc [ctb], Nils Ternes [ctb]

Maintainer: Wencan Zhu <[e-mail]>

References

W. Zhu, C. Levy-Leduc, N. Ternes. "A variable selection approach for highly correlated predictors in high-dimensional genomic data". (2020)