Package: biotmle

Title: Targeted Learning with Moderated Statistics for Biomarker Discovery

Version: 1.17.1

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

LazyData: false

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