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gnu: Add r-biotmle.

* gnu/packages/bioconductor.scm (r-biotmle): New variable.
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Ricardo Wurmus 2021-09-07 14:39:03 +02:00
parent ccc1b9e5a1
commit b63fb6a2e6
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@ -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")