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@ -14466,6 +14466,50 @@ optimised for high performance.")
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help unravel disease regulatory trajectory.")
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help unravel disease regulatory trajectory.")
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(license license:gpl2)))
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(license license:gpl2)))
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(define-public r-biotmle
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(package
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(name "r-biotmle")
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(version "1.16.0")
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(source
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(origin
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(method url-fetch)
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(uri (bioconductor-uri "biotmle" version))
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(sha256
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(base32
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"01smkmbv40yprgrgi2gjnmi8ncqyrlkfdxsh33ki20amcx32nc7f"))))
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(properties `((upstream-name . "biotmle")))
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(build-system r-build-system)
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(propagated-inputs
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`(("r-assertthat" ,r-assertthat)
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("r-biocgenerics" ,r-biocgenerics)
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("r-biocparallel" ,r-biocparallel)
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("r-dofuture" ,r-dofuture)
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("r-dplyr" ,r-dplyr)
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("r-drtmle" ,r-drtmle)
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("r-future" ,r-future)
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("r-ggplot2" ,r-ggplot2)
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("r-ggsci" ,r-ggsci)
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("r-limma" ,r-limma)
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("r-s4vectors" ,r-s4vectors)
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("r-summarizedexperiment" ,r-summarizedexperiment)
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("r-superheat" ,r-superheat)
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("r-tibble" ,r-tibble)))
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(native-inputs
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`(("r-knitr" ,r-knitr)))
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(home-page "https://code.nimahejazi.org/biotmle/")
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(synopsis "Targeted learning with moderated statistics for biomarker discovery")
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(description
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"This package provides tools for differential expression biomarker
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discovery based on microarray and next-generation sequencing data that
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leverage efficient semiparametric estimators of the average treatment effect
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for variable importance analysis. Estimation and inference of the (marginal)
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average treatment effects of potential biomarkers are computed by targeted
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minimum loss-based estimation, with joint, stable inference constructed across
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all biomarkers using a generalization of moderated statistics for use with the
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estimated efficient influence function. The procedure accommodates the use of
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ensemble machine learning for the estimation of nuisance functions.")
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(license license:expat)))
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(define-public r-tximeta
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(define-public r-tximeta
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(package
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(package
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(name "r-tximeta")
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(name "r-tximeta")
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