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This is the development version of biotmle; for the stable release version, see biotmle.

Targeted Learning with Moderated Statistics for Biomarker Discovery

Bioconductor version: Development (3.20)

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] , Alan Hubbard [aut, ths] , Mark van der Laan [aut, ths] , Weixin Cai [ctb] , Philippe Boileau [ctb]

Maintainer: Nima Hejazi <nh at>

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


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


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


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

Identifying Biomarkers from an Exposure Variable HTML R Script
Reference Manual PDF


biocViews DifferentialExpression, GeneExpression, ImmunoOncology, Microarray, RNASeq, Regression, Sequencing, Software
Version 1.29.0
In Bioconductor since BioC 3.5 (R-3.4) (7 years)
License MIT + file LICENSE
Depends R (>= 4.0)
Imports stats, methods, dplyr, tibble, ggplot2, ggsci, superheat, assertthat, drtmle (>= 1.0.4), S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, limma
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Suggests testthat, knitr, rmarkdown, BiocStyle, arm, earth, ranger, SuperLearner, Matrix, DBI, biotmleData(>= 1.1.1)
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