73 lines (72 with data), 2.3 kB

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