DESCRIPTION

Package: gridisl
Title: Discrete Super Learner with Grid-Search for Longitudinal Data
Version: 0.0.7.9000
Authors@R: c(
    person("Oleg", "Sofrygin", role=c("aut", "cre"), email="oleg.sofrygin@gmail.com"),
    person(c("Mark", "J."), "van der Laan", role="aut", email="laan@berkeley.edu"))
Description: Machine Learning Toolkit with Grid-Search Discrete Super Learner for Longitudinal Data. Provides access to machine learning algorithms implemented in xgboost or h2o (RandomForests, Gradient Boosting Machines, Deep Neural Nets). Simple syntax for specifying large grids of tuning parameters, including random grid search over parameter space. Model selection can be performed via V-fold cross-validation or random validation splits.
URL: https://github.com/osofr/gridisl
BugReports: https://github.com/osofr/gridisl/issues
SystemRequirements: pandoc (http://pandoc.org) for generating and exporting
    markdown reports to other formats.
Depends:
    R (>= 3.2.1),
    ggplot2 (>= 2.2.0)
Imports:
    assertthat,
    data.table,
    dplyr,
    ggplot2,
    h2o,
    magrittr,
    methods,
    origami,
    R6,
    Rcpp,
    rmarkdown,
    pander,
    purrr,
    speedglm,
    SuperLearner,
    stats,
    tibble,
    tidyr,
    xgboost
LinkingTo: Rcpp
Suggests:
    brokenstick (>= 0.49),
    face (>= 0.1-2),
    doParallel,
    foreach,
    ggiraph,
    knitr,
    RUnit,
    testthat,
    mockery
License: MIT + file LICENSE
LazyData: true
RoxygenNote: 6.0.1