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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "txshift" in publications use:'
type: software
license: MIT
title: 'txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions'
version: 0.3.9
doi: 10.5281/zenodo.4070042
identifiers:
- type: doi
  value: 10.32614/CRAN.package.txshift
abstract: 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) <https://doi.org/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) <https://doi.org/10.1111/biom.13375>. The software
  package implementation is described in NS Hejazi and DC Benkeser (2020) <https://doi.org/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")'.
authors:
- family-names: Hejazi
  given-names: Nima
  email: nh@nimahejazi.org
  orcid: https://orcid.org/0000-0002-7127-2789
- family-names: Benkeser
  given-names: David
  email: benkeser@emory.edu
  orcid: https://orcid.org/0000-0002-1019-8343
preferred-citation:
  type: manual
  title: 'txshift: Efficient estimation of the causal effects of stochastic interventions'
  authors:
  - family-names: Hejazi
    given-names: Nima S
  - family-names: Benkeser
    given-names: David
    email: benkeser@emory.edu
    orcid: https://orcid.org/0000-0002-1019-8343
  year: '2024'
  notes: R package version 0.3.9
  doi: 10.5281/zenodo.4070042
  url: https://CRAN.R-project.org/package=txshift
repository: https://nhejazi.r-universe.dev
repository-code: https://github.com/nhejazi/txshift
commit: b51b2b42fb783c71c151f1c8844ac3bd77f9171b
url: https://github.com/nhejazi/txshift
contact:
- family-names: Hejazi
  given-names: Nima
  email: nh@nimahejazi.org
  orcid: https://orcid.org/0000-0002-7127-2789
references:
- type: article
  title: 'txshift: Efficient estimation of the causal effects of stochastic interventions
    in R'
  authors:
  - family-names: Hejazi
    given-names: Nima S
  - family-names: Benkeser
    given-names: David
  year: '2020'
  journal: Journal of Open Source Software
  publisher:
    name: The Open Journal
  doi: 10.21105/joss.02447
  url: https://doi.org/10.21105/joss.02447
- type: article
  title: Efficient nonparametric inference on the effects of stochastic interventions
    under two-phase sampling, with applications to vaccine efficacy trials
  authors:
  - family-names: Hejazi
    given-names: Nima S
  - family-names: Laan
    given-names: Mark J
    name-particle: van der
  - family-names: Janes
    given-names: Holly E
  - family-names: Gilbert
    given-names: Peter B
  - family-names: Benkeser
    given-names: David C
  year: '2020'
  journal: Biometrics
  publisher:
    name: Wiley Online Library
  doi: 10.1111/biom.13375
  url: https://doi.org/10.1111/biom.13375