Other packages > Find by keyword >

txshift  

Efficient Estimation of the Causal Effects of Stochastic Interventions
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]
All associated links for this package
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 06 Mar. 2026.
Previous versions and publish date:
0.3.4 (2020-09-25 15:50), 0.3.5 (2021-02-07 21:10), 0.3.6 (2021-10-18 20:10), 0.3.7 (2022-01-28 11:50)
Other packages that cited txshift R package
View txshift citation profile
Other R packages that txshift depends, imports, suggests or enhances
Complete documentation for txshift
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

lbfgs  
Limited-memory BFGS Optimization
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implem ...
Download / Learn more Package Citations See dependency  
DatabionicSwarm  
Swarm Intelligence for Self-Organized Clustering
Algorithms implementing populations of agents that interact with one another and sense their environ ...
Download / Learn more Package Citations See dependency  
solitude  
An Implementation of Isolation Forest
Isolation forest is anomaly detection method introduced by the paper Isolation based Anomaly Detecti ...
Download / Learn more Package Citations See dependency  
mlr3viz  
Visualizations for 'mlr3'
Visualization package of the 'mlr3' ecosystem. It features plots for mlr3 objects such as tasks, le ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  
EMVS  
The Expectation-Maximization Approach to Bayesian Variable Selection
An efficient expectation-maximization algorithm for fitting Bayesian spike-and-slab regularization p ...
Download / Learn more Package Citations See dependency  

26,264

R Packages

223,360

Dependencies

70,244

Author Associations

26,265

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA