Package: txshift
Title: Efficient Estimation of the Causal Effects of Stochastic
        Interventions
Version: 0.3.9
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 (>=
        0.4.6), haldensify (>= 0.2.3), lspline, ggplot2, scales,
        latex2exp, Rdpack
Suggests: testthat, knitr, rmarkdown, covr, future, future.apply,
        origami (>= 1.0.7), ranger, Rsolnp, nnls
Enhances: sl3 (>= 1.4.5)
License: MIT + file LICENSE
URL: https://github.com/nhejazi/txshift
BugReports: https://github.com/nhejazi/txshift/issues
Encoding: UTF-8
VignetteBuilder: knitr
RoxygenNote: 7.3.2
RdMacros: Rdpack
Config/pak/sysreqs: libicu-dev
Repository: https://nhejazi.r-universe.dev
Date/Publication: 2024-09-21 20:33:00 UTC
RemoteUrl: https://github.com/nhejazi/txshift
RemoteRef: HEAD
RemoteSha: 06ce36d94009e99d21051978b71897139fc06610
NeedsCompilation: no
Packaged: 2026-06-21 09:06:50 UTC; root
Author: Nima Hejazi [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0002-7127-2789>),
  David Benkeser [aut] (ORCID: <https://orcid.org/0000-0002-1019-8343>),
  Iván Díaz [ctb] (ORCID: <https://orcid.org/0000-0001-9056-2047>),
  Jeremy Coyle [ctb] (ORCID: <https://orcid.org/0000-0002-9874-6649>),
  Mark van der Laan [ctb, ths] (ORCID:
    <https://orcid.org/0000-0003-1432-5511>)