Package: biotmle
Title: Targeted Learning with Moderated Statistics for Biomarker
        Discovery
Version: 1.37.0
Authors@R: c(
    person("Nima", "Hejazi", email = "nh@nimahejazi.org",
           role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0002-7127-2789")),
    person("Alan", "Hubbard", email = "hubbard@berkeley.edu",
           role = c("aut", "ths"),
           comment = c(ORCID = "0000-0002-3769-0127")),
    person("Mark", "van der Laan", email = "laan@stat.berkeley.edu",
           role = c("aut", "ths"),
           comment = c(ORCID = "0000-0003-1432-5511")),
    person("Weixin", "Cai", email = "wcai@berkeley.edu",
           role = "ctb",
           comment = c(ORCID = "0000-0003-2680-3066")),
    person("Philippe", "Boileau", email = "philippe_boileau@berkeley.edu",
           role = "ctb",
           comment = c(ORCID = "0000-0002-4850-2507"))
  )
Description: 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.
Depends: R (>= 4.0)
License: MIT + file LICENSE
URL: https://code.nimahejazi.org/biotmle
BugReports: https://github.com/nhejazi/biotmle/issues
Encoding: UTF-8
Imports: stats, methods, dplyr, tibble, ggplot2, ggsci, superheat,
        assertthat, drtmle (>= 1.0.4), S4Vectors, BiocGenerics,
        BiocParallel, SummarizedExperiment, limma
Suggests: testthat, knitr, rmarkdown, BiocStyle, arm, earth, ranger,
        SuperLearner, Matrix, DBI, biotmleData (>= 1.1.1)
VignetteBuilder: knitr
RoxygenNote: 7.1.2
biocViews: Regression, GeneExpression, DifferentialExpression,
        Sequencing, Microarray, RNASeq, ImmunoOncology
Config/pak/sysreqs: make zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-04-28 12:45:34 UTC
RemoteUrl: https://github.com/bioc/biotmle
RemoteRef: HEAD
RemoteSha: 637bce4128bc312b8c73484dbf8387eba1ee1a6e
NeedsCompilation: no
Packaged: 2026-05-29 08:35:26 UTC; root
Author: Nima Hejazi [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0002-7127-2789>),
  Alan Hubbard [aut, ths] (ORCID:
    <https://orcid.org/0000-0002-3769-0127>),
  Mark van der Laan [aut, ths] (ORCID:
    <https://orcid.org/0000-0003-1432-5511>),
  Weixin Cai [ctb] (ORCID: <https://orcid.org/0000-0003-2680-3066>),
  Philippe Boileau [ctb] (ORCID: <https://orcid.org/0000-0002-4850-2507>)
Maintainer: Nima Hejazi <nh@nimahejazi.org>