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| author | Ricardo Wurmus <rekado@elephly.net> | 2021-09-07 14:39:03 +0200 |
|---|---|---|
| committer | Ricardo Wurmus <rekado@elephly.net> | 2021-09-07 14:47:57 +0200 |
| commit | b63fb6a2e64c46653c9888d072d5f46c09f52fdd (patch) | |
| tree | de9d25a0d1dbf9d8fcac4126eba6b26804de86de | |
| parent | ccc1b9e5a1d2e61ba6518df07415752388e4a478 (diff) | |
| download | guix-b63fb6a2e64c46653c9888d072d5f46c09f52fdd.tar.gz guix-b63fb6a2e64c46653c9888d072d5f46c09f52fdd.zip | |
gnu: Add r-biotmle.
* gnu/packages/bioconductor.scm (r-biotmle): New variable.
| -rw-r--r-- | gnu/packages/bioconductor.scm | 44 |
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") |