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DESCRIPTION
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DESCRIPTION
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Package: txshift
Title: Efficient Estimation of the Causal Effects of Stochastic Interventions
Version: 0.3.9
Authors@R: c(
person("Nima", "Hejazi", email = "[email protected]",
role = c("aut", "cre", "cph"),
comment = c(ORCID = "0000-0002-7127-2789")),
person("David", "Benkeser", email = "[email protected]",
role = "aut",
comment = c(ORCID = "0000-0002-1019-8343")),
person("Iván", "Díaz", email = "[email protected]",
role = "ctb",
comment = c(ORCID = "0000-0001-9056-2047")),
person("Jeremy", "Coyle", email = "[email protected]",
role = "ctb",
comment = c(ORCID = "0000-0002-9874-6649")),
person("Mark", "van der Laan", email = "[email protected]",
role = c("ctb", "ths"),
comment = c(ORCID = "0000-0003-1432-5511"))
)
Maintainer: Nima Hejazi <[email protected]>
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