PatientLevelPrediction/DESCRIPTION at master · k-m-li/PatientLevelPrediction · GitHub

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Package: PatientLevelPrediction
Type: Package
Title: Package for patient level prediction using data in the OMOP Common Data
    Model
Version: 4.3.10
Date: 2021-06-07
Authors@R: c(
    person("Jenna", "Reps", email = "jreps@its.jnj.com", role = c("aut", "cre")),
    person("Martijn", "Schuemie", role = c("aut")),
    person("Marc", "Suchard", role = c("aut")),
    person("Patrick", "Ryan", role = c("aut")),
    person("Peter", "Rijnbeek", role = c("aut"))
    )
Maintainer: Jenna Reps <jreps@its.jnj.com>
Description: A package for creating patient level prediction models. Given a
    cohort of interest and an outcome of interest, the package can use data in the
    OMOP Common Data Model to build a large set of features. These features can then
    be assessed to fit a predictive model using a number of machine learning algorithms.
    Several performance measures are implemented for model evaluation.
License: Apache License 2.0
URL: https://ohdsi.github.io/PatientLevelPrediction, https://github.com/OHDSI/PatientLevelPrediction
BugReports: https://github.com/OHDSI/PatientLevelPrediction/issues
VignetteBuilder: knitr
Depends:
    R (>= 3.3.0),
    FeatureExtraction (>= 3.0.0)
Imports:
    Andromeda,
    benchmarkme,
    bit64,
    Cyclops (>= 3.0.0),
    DatabaseConnector (>= 3.0.0),
    dplyr,
    ggplot2,
    gridExtra,
    Hmisc,
    magrittr,
    Matrix,
    parallel,
    ParallelLogger (>= 2.0.0),
    PRROC,
    Rcpp (>= 0.11.2),
    reshape2,
    reticulate (> 1.16),
    rlang,
    rms,
    RSQLite,
    slam,
    SqlRender (>= 1.1.3),
    survival,
    tibble,
    tidyr,
    tools,
    utils,
    zeallot
Suggests:
    AUC,
    aws.s3,
    BigKnn (>= 1.0.0),
    devtools,
    diagram,
    DT,
    gnm,
    htmlwidgets (> 0.8),
    htmltools,
    keras,
    knitr,
    markdown,
    Metrics,
    officer,
    plotly,
    plyr,
    pROC,
    R6,
    ResourceSelection,
    rmarkdown,
    scoring,
    shiny,
    shinycssloaders,
    shinydashboard,
    SparseM,
    survAUC,
    survminer,
    tensorflow,
    testthat,
    xgboost (> 1.3.2.1)
Remotes:
    ohdsi/FeatureExtraction,
    ohdsi/BigKnn
LinkingTo: Rcpp
NeedsCompilation: yes
RoxygenNote: 7.1.1
Encoding: UTF-8