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txshift
View on CRAN: Click
here
Download and install txshift package within the R console
Install from CRAN:
install.packages("txshift")
Install from Github:
library("remotes")
install_github("cran/txshift")
Install by package version:
library("remotes")
install_version("txshift", "0.3.8")
Attach the package and use:
library("txshift")
Maintained by
Nima Hejazi
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.txshift . https://github.com/nhejazi/txshift/issues . https://github.com/nhejazi/txshift . txshift citation info . txshift results . txshift.pdf . txshift_0.3.8.tar.gz . txshift_0.3.8.zip . txshift_0.3.8.zip . txshift_0.3.8.zip . txshift_0.3.8.tgz . txshift_0.3.8.tgz . txshift_0.3.8.tgz . txshift_0.3.8.tgz . txshift_0.3.8.tgz . txshift_0.3.8.tgz . txshift archive . https://CRAN.R-project.org/package=txshift .
First Published: 2020-09-25
Latest Update: 2022-02-09
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")'.
How to cite:
Nima Hejazi (2020). txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions. R package version 0.3.8, https://cran.r-project.org/web/packages/txshift. Accessed 30 Mar. 2025.
Previous versions and publish date:
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Complete documentation for txshift
Functions, R codes and Examples using
the txshift R package
Some associated functions: bound_precision . bound_propensity . confint.txshift . eif . est_Hn . est_Q . est_g_cens . est_g_exp . est_samp . fit_fluctuation . ipcw_eif_update . msm_vimshift . onestep_txshift . plot.txshift_msm . print.txshift . print.txshift_msm . scale_to_original . scale_to_unit . shift_additive . tmle_txshift . txshift .
Some associated R codes: bound.R . confint.R . eifs.R . fit_mechanisms.R . msm.R . onestep_txshift.R . plots.R . shift_funs.R . tmle_txshift.R . txshift.R . utils.R . zzz.R . Full txshift package functions and examples
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