Package: tmle3shift 0.2.2

Nima Hejazi

tmle3shift: Targeted Learning of the Causal Effects of Stochastic Interventions

Targeted maximum likelihood estimation (TMLE) of population-level causal effects under stochastic treatment regimes and related nonparametric variable importance analyses. Tools are provided for TML estimation of the counterfactual mean under a stochastic intervention characterized as a modified treatment policy, such as treatment policies that shift the natural value of the exposure. The causal parameter and estimation were described in Díaz and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x> and an improved estimation approach was given by Díaz and van der Laan (2018) <doi:10.1007/978-3-319-65304-4_14>.

Authors:Nima Hejazi [aut, cre, cph], Jeremy Coyle [aut], Mark van der Laan [aut, ths]

tmle3shift_0.2.2.tar.gz
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tmle3shift_0.2.2.tgz(r-4.6-any)tmle3shift_0.2.2.tgz(r-4.5-any)
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manual.pdf |manual.html✨
card.svg |card.png
tmle3shift/json (API)
NEWS

# Install 'tmle3shift' in R:

install.packages('tmle3shift', repos = c('https://tlverse.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tlverse/tmle3shift/issues

Pkgdown/docs site:https://tlverse.org

On CRAN:

causal-inference machine-learning marginal-structural-models stochastic-interventions targeted-learning treatment-effects variable-importance

5.46 score 17 stars 1 packages 56 scripts 13 exports 122 dependencies

Last updated from:0c3b8f07d8. Checks:7 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	ERROR	237
source / vignettes	OK	645
linux-release-x86_64	ERROR	234
macos-release-arm64	ERROR	170
macos-oldrel-arm64	ERROR	128
windows-devel	ERROR	187
windows-release	ERROR	167
windows-oldrel	ERROR	169
wasm-release	OK	220

Exports:LF_shift Param_MSM_linear shift_additive shift_additive_bounded shift_additive_bounded_inv shift_additive_inv tmle_shift tmle_vimshift_delta tmle_vimshift_msm tmle3_Spec_shift tmle3_Spec_vimshift_delta tmle3_Spec_vimshift_msm trend_msm

Dependencies:abind assertthat backports base64enc BBmisc bitops bslib cachem caret caTools checkmate class cli clock codetools cpp11 crayon data.table delayed diagram digest dplyr e1071 evaluate farver fastmap fontawesome foreach fs future future.apply generics ggplot2 globals glue gower gplots gtable gtools hardhat highr hms htmltools htmlwidgets igraph ipred isoband iterators jquerylib jsonlite KernSmooth knitr labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix memoise mime ModelMetrics mvtnorm nlme nnet numDeriv origami parallelly pillar pkgconfig plyr prettyunits pROC prodlim progress progressr proxy purrr R.methodsS3 R.oo R.utils R6 rappdirs rbibutils RColorBrewer Rcpp Rdpack recipes reshape2 rlang rmarkdown ROCR rpart rstackdeque S7 sass scales shape sl3 sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tinytex tmle3 tzdb utf8 uuid vctrs viridisLite visNetwork withr xfun yaml

Targeted Learning with Stochastic Treatment Regimes

Nima Hejazi, Jeremy Coyle, and Mark van der Laan

Rendered fromshift_tmle.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2020-03-13
Started: 2018-06-01

Variable Importance Analysis with Stochastic Interventions

Nima Hejazi, Jeremy Coyle, and Mark van der Laan

Rendered fromvimshift.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2020-03-13
Started: 2018-09-05

Help page	Topics
Shifted Likelihood Factor	LF_shift
Parameter for Linear Working Marginal Structural Model	Param_MSM_linear
Additive Shifts of Continuous-Valued Interventions Without Bounds	shift_additive shift_additive_inv
Outcome under Shifted Treatment	tmle_shift
Outcome Under a Grid of Shifted Interventions via Delta Method	tmle_vimshift_delta
Outcome Under a Grid of Shifted Interventions via Targeted Working MSM	tmle_vimshift_msm
Defines a TML Estimator for the Outcome under a Shifted Treatment	tmle3_Spec_shift
Defines a TML Estimator for Variable Importance for Continuous Interventions	tmle3_Spec_vimshift_delta
Defines a TML Estimator for Variable Importance for Continuous Interventions	tmle3_Spec_vimshift_msm
Test for a trend in the effect of shift interventions via working MSM	trend_msm

Package: tmle3shift 0.2.2

tmle3shift: Targeted Learning of the Causal Effects of Stochastic Interventions

Targeted Learning with Stochastic Treatment Regimes

Variable Importance Analysis with Stochastic Interventions

Citation

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)