Package: txshift
Title: Efficient Estimation of the Causal Effects of Stochastic
        Interventions
Version: 0.3.8
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 the counterfactual mean
    value of an outcome of interest under an additive modified treatment policy,
    a stochastic intervention that may depend on the natural value of the
    exposure. To accommodate settings with outcome-dependent two-phase
    sampling, procedures incorporating inverse probability of censoring
    weighting are provided to facilitate the 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 from two-phase sampling
    designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert,
    and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package
    implementation is described in NS Hejazi and DC Benkeser (2020)
    <doi:10.21105/joss.02447>. 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 (>=
        0.4.1), haldensify (>= 0.2.1), lspline, ggplot2, scales,
        latex2exp, Rdpack
Suggests: testthat, knitr, rmarkdown, covr, future, future.apply,
        origami (>= 1.0.3), ranger, Rsolnp, nnls
Enhances: sl3 (>= 1.4.3)
License: MIT + file LICENSE
URL: https://github.com/nhejazi/txshift
BugReports: https://github.com/nhejazi/txshift/issues
Encoding: UTF-8
VignetteBuilder: knitr
RoxygenNote: 7.1.2
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2022-02-09 21:44:37 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: 2022-02-09 22:30:02 UTC
Built: R 4.6.0; ; 2026-04-26 15:41:09 UTC; unix
