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+++ b/DESCRIPTION
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+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