DOI: 10.18129/B9.bioc.biotmle    

This is the development version of biotmle; to use it, please install the devel version of Bioconductor.

Targeted Learning with Moderated Statistics for Biomarker Discovery

Bioconductor version: Development (3.10)

This package facilitates the discovery of biomarkers from biological sequencing data (e.g., microarrays, RNA-seq) based on the associations of potential biomarkers with exposure and outcome variables by implementing an estimation procedure that combines a generalization of moderated statistics with targeted minimum loss-based estimates (TMLE) of causal parameters (e.g., Average Treatment Effect) whose estimators admit asymptotically linear representations (in terms of influence functions).

Author: Nima Hejazi [aut, cre, cph], Alan Hubbard [aut, ths], Mark van der Laan [ctb, ths], Weixin Cai [ctb]

Maintainer: Nima Hejazi <nhejazi at>

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


To install this package, start R (version "3.6") and enter:

if (!requireNamespace("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:



HTML R Script Identifying Biomarkers from an Exposure Variable
HTML R Script Processing and Analyzing RNA-Seq Data
PDF   Reference Manual
Text   NEWS


biocViews DifferentialExpression, GeneExpression, ImmunoOncology, Microarray, RNASeq, Sequencing, Software
Version 1.9.0
In Bioconductor since BioC 3.5 (R-3.4) (2.5 years)
License file LICENSE
Depends R (>= 3.4)
Imports stats, methods, dplyr, tibble, ggplot2, ggsci, superheat, assertthat, future, doFuture, S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, limma, tmle
Suggests testthat, knitr, rmarkdown, BiocStyle, SuperLearner, Matrix, DBI, biotmleData(>= 1.1.1)
Depends On Me
Imports Me
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Source Package biotmle_1.9.0.tar.gz
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