DESCRIPTION

Package: sherlock
Title: Causal Machine Learning for Segment Discovery and Analysis
Version: 0.2.0
Authors@R: c(
    person("Nima", "Hejazi", email = "nh@nimahejazi.org",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-7127-2789")),
    person("Wenjing", "Zheng", email = "wzheng@netflix.com",
           role = "aut",
           comment = c(ORCID = "0000-0002-6584-1359")),
    person("Netflix, Inc.", role = c("fnd", "cph"))
  )
Maintainer: Nima Hejazi <nh@nimahejazi.org>
Description: Population segment discovery and analysis using state-of-the-art,
    doubly robust estimation techniques rooted in causal inference and machine
    learning. Using data from randomized A/B experiments or quasi-experiments
    (observational studies), a set of candidate segmentation variables are used
    to discover segments of the study population based on estimated treatment
    response heterogeneity, characterized by the conditional average treatment
    effect.  Implemented procedures for estimation of the conditional average
    treatment effect incorporate ensemble machine learning (or the user's
    choice of regression algorithms) via the Super Learner ensemble modeling
    procedure in 'sl3', available for download from GitHub using
    'remotes::install_github("tlverse/sl3")'.
Depends: R (>= 3.6.0)
Imports:
    stats,
    data.table,
    R6,
    origami (>= 1.0.3),
    sl3 (>= 1.4.3),
    assertthat,
    stringr,
    tibble,
    dplyr,
    adagio,
    Rsolnp,
    ggplot2
Suggests:
    knitr,
    rmarkdown,
    testthat (>= 3.0.0),
    covr,
    conflicted,
    speedglm,
    ranger,
    xgboost,
    glmnet
Remotes:
    github::tlverse/sl3@devel
License: Apache License (>= 2)
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE, old_usage = TRUE, r6 = FALSE)
RoxygenNote: 7.1.2
Config/testthat/edition: 3
VignetteBuilder: knitr