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authorRicardo Wurmus <rekado@elephly.net>2021-09-07 14:39:03 +0200
committerRicardo Wurmus <rekado@elephly.net>2021-09-07 14:47:57 +0200
commitb63fb6a2e64c46653c9888d072d5f46c09f52fdd (patch)
treede9d25a0d1dbf9d8fcac4126eba6b26804de86de /gnu/packages/bioconductor.scm
parentccc1b9e5a1d2e61ba6518df07415752388e4a478 (diff)
gnu: Add r-biotmle.
* gnu/packages/bioconductor.scm (r-biotmle): New variable.
Diffstat (limited to 'gnu/packages/bioconductor.scm')
-rw-r--r--gnu/packages/bioconductor.scm44
1 files changed, 44 insertions, 0 deletions
diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm
index 0d9344f165..7391afa6d3 100644
--- a/gnu/packages/bioconductor.scm
+++ b/gnu/packages/bioconductor.scm
@@ -14466,6 +14466,50 @@ optimised for high performance.")
help unravel disease regulatory trajectory.")
(license license:gpl2)))
+(define-public r-biotmle
+ (package
+ (name "r-biotmle")
+ (version "1.16.0")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (bioconductor-uri "biotmle" version))
+ (sha256
+ (base32
+ "01smkmbv40yprgrgi2gjnmi8ncqyrlkfdxsh33ki20amcx32nc7f"))))
+ (properties `((upstream-name . "biotmle")))
+ (build-system r-build-system)
+ (propagated-inputs
+ `(("r-assertthat" ,r-assertthat)
+ ("r-biocgenerics" ,r-biocgenerics)
+ ("r-biocparallel" ,r-biocparallel)
+ ("r-dofuture" ,r-dofuture)
+ ("r-dplyr" ,r-dplyr)
+ ("r-drtmle" ,r-drtmle)
+ ("r-future" ,r-future)
+ ("r-ggplot2" ,r-ggplot2)
+ ("r-ggsci" ,r-ggsci)
+ ("r-limma" ,r-limma)
+ ("r-s4vectors" ,r-s4vectors)
+ ("r-summarizedexperiment" ,r-summarizedexperiment)
+ ("r-superheat" ,r-superheat)
+ ("r-tibble" ,r-tibble)))
+ (native-inputs
+ `(("r-knitr" ,r-knitr)))
+ (home-page "https://code.nimahejazi.org/biotmle/")
+ (synopsis "Targeted learning with moderated statistics for biomarker discovery")
+ (description
+ "This package provides 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.")
+ (license license:expat)))
+
(define-public r-tximeta
(package
(name "r-tximeta")