This is the development version of biotmle; for the stable release version, see biotmle.
Targeted Learning with Moderated Statistics for Biomarker DiscoveryBioconductor version: Development (3.22)
Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions.
Author: Nima Hejazi [aut, cre, cph] ORCID: 0000-0002-7127-2789 , Alan Hubbard [aut, ths] ORCID: 0000-0002-3769-0127 , Mark van der Laan [aut, ths] ORCID: 0000-0003-1432-5511 , Weixin Cai [ctb] ORCID: 0000-0003-2680-3066 , Philippe Boileau [ctb] ORCID: 0000-0002-4850-2507
Maintainer: Nima Hejazi <nh at nimahejazi.org>
Citation (from within R, entercitation("biotmle")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("biotmle")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("biotmle")Follow Installation instructions to use this package in your R session.
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