gnu: Add r-biotmle.
* gnu/packages/bioconductor.scm (r-biotmle): New variable.
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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") | ||||
|  |  | |||
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