Package: txshift
Title: Efficient Estimation of the Causal Effects of Stochastic
        Interventions
Version: 0.3.5
Authors@R: c(
    person("Nima", "Hejazi", email = "nh@nimahejazi.org",
           role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0002-7127-2789")),
    person("David", "Benkeser", email = "benkeser@emory.edu",
           role = "aut",
           comment = c(ORCID = "0000-0002-1019-8343")),
    person("Iván", "Díaz", email = "ild2005@med.cornell.edu",
           role = "ctb",
           comment = c(ORCID = "0000-0001-9056-2047")),
    person("Jeremy", "Coyle", email = "jeremy.coyle@gmail.com",
           role = "ctb",
           comment = c(ORCID = "0000-0002-9874-6649")),
    person("Mark", "van der Laan", email = "laan@berkeley.edu",
           role = c("ctb", "ths"),
           comment = c(ORCID = "0000-0003-1432-5511"))
  )
Maintainer: Nima Hejazi <nh@nimahejazi.org>
Description: Efficient estimation of the population-level causal effects of
    stochastic interventions on a continuous-valued exposure. Both one-step and
    targeted minimum loss estimators are implemented for a causal parameter
    defined as the counterfactual mean of an outcome of interest under a
    stochastic intervention that may depend on the natural value of the
    exposure (i.e., a modified treatment policy). To accommodate settings in
    which two-phase sampling is employed, procedures for making use of inverse
    probability of censoring weights are provided to facilitate construction of
    inefficient and efficient one-step and targeted minimum loss estimators.
    The causal parameter and its estimation were first described by Díaz and van
    der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply
    robust estimation procedure and its application to data arising in two-phase
    sampling designs was detailed in NS Hejazi, MJ van der Laan, HE Janes, PB
    Gilbert, and DC Benkeser (2020) <doi:10.1111/biom.13375>. Estimation of
    nuisance parameters may be enhanced through the Super Learner ensemble model
    in 'sl3', available for download from GitHub using
    'remotes::install_github("tlverse/sl3")'.
Depends: R (>= 3.2.0)
Imports: stats, stringr, data.table, assertthat, mvtnorm, hal9001 (>=
        0.2.6), haldensify (>= 0.0.6), lspline, ggplot2, tibble,
        scales, latex2exp, Rdpack, cli
Suggests: testthat, knitr, rmarkdown, covr, future, future.apply,
        origami (>= 1.0.3), ranger, Rsolnp, nnls, rlang
Enhances: sl3 (>= 1.3.7)
License: MIT + file LICENSE
URL: https://github.com/nhejazi/txshift
BugReports: https://github.com/nhejazi/txshift/issues
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 7.1.1
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2021-02-07 01:07:36 UTC; nsh
Author: Nima Hejazi [aut, cre, cph] (<https://orcid.org/0000-0002-7127-2789>),
  David Benkeser [aut] (<https://orcid.org/0000-0002-1019-8343>),
  Iván Díaz [ctb] (<https://orcid.org/0000-0001-9056-2047>),
  Jeremy Coyle [ctb] (<https://orcid.org/0000-0002-9874-6649>),
  Mark van der Laan [ctb, ths] (<https://orcid.org/0000-0003-1432-5511>)
Repository: CRAN
Date/Publication: 2021-02-07 20:10:05 UTC
