<?xml version="1.0" encoding="UTF-8"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Efficient Estimation of the Causal Effects of Stochastic
Interventions</dc:title>
  <dc:title>R package txshift version 0.3.8</dc:title>
  <dc: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) &lt;doi:10.1111/j.1541-0420.2011.01685.x&gt;, 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) &lt;doi:10.1111/biom.13375&gt;. The software package
    implementation is described in NS Hejazi and DC Benkeser (2020)
    &lt;doi:10.21105/joss.02447&gt;. 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")'.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 3.2.0)</dc:relation>
  <dc:relation>Imports: stats, stringr, data.table, assertthat, mvtnorm, hal9001 (&gt;=
0.4.1), haldensify (&gt;= 0.2.1), lspline, ggplot2, scales,
latex2exp, Rdpack</dc:relation>
  <dc:relation>Suggests: testthat, knitr, rmarkdown, covr, future, future.apply,
origami (&gt;= 1.0.3), ranger, Rsolnp, nnls</dc:relation>
  <dc:relation>Enhances: sl3 (&gt;= 1.4.3)</dc:relation>
  <dc:creator>Nima Hejazi &lt;nh@nimahejazi.org&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Nima Hejazi [aut, cre, cph] (ORCID:
    &lt;https://orcid.org/0000-0002-7127-2789&gt;),
  David Benkeser [aut] (ORCID: &lt;https://orcid.org/0000-0002-1019-8343&gt;),
  Iván Díaz [ctb] (ORCID: &lt;https://orcid.org/0000-0001-9056-2047&gt;),
  Jeremy Coyle [ctb] (ORCID: &lt;https://orcid.org/0000-0002-9874-6649&gt;),
  Mark van der Laan [ctb, ths] (ORCID:
    &lt;https://orcid.org/0000-0003-1432-5511&gt;)</dc:contributor>
  <dc:rights>MIT + file LICENSE (https://CRAN.R-project.org/package=txshift/LICENSE)</dc:rights>
  <dc:date>2022-02-09</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=txshift</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.txshift</dc:identifier>
</oai_dc:dc>