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
tmle3shift_0.2.2.zip(r-4.7)tmle3shift_0.2.2.zip(r-4.6)tmle3shift_0.2.2.zip(r-4.5)
tmle3shift_0.2.2.tgz(r-4.6-any)tmle3shift_0.2.2.tgz(r-4.5-any)
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tmle3shift_0.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tmle3shift/json (API)

# Install 'tmle3shift' in R:
install.packages('tmle3shift', repos = c('https://ictml-project.r-universe.dev', 'https://cloud.r-project.org'))

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

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

On CRAN:

Conda:

causal-inferencemachine-learningmarginal-structural-modelsstochastic-interventionstargeted-learningtreatment-effectsvariable-importance

5.49 score 17 stars 1 packages 61 scripts 13 exports 122 dependencies

Last updated from:0c3b8f07d8. Checks:7 ERROR, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR242
source / vignettesOK665
linux-release-x86_64ERROR223
macos-release-arm64ERROR182
macos-oldrel-arm64ERROR157
windows-develERROR224
windows-releaseERROR175
windows-oldrelERROR164
wasm-releaseOK143

Exports:LF_shiftParam_MSM_linearshift_additiveshift_additive_boundedshift_additive_bounded_invshift_additive_invtmle_shifttmle_vimshift_deltatmle_vimshift_msmtmle3_Spec_shifttmle3_Spec_vimshift_deltatmle3_Spec_vimshift_msmtrend_msm

Dependencies:abindassertthatbackportsbase64encBBmiscbitopsbslibcachemcaretcaToolscheckmateclasscliclockcodetoolscpp11crayondata.tabledelayeddiagramdigestdplyre1071evaluatefarverfastmapfontawesomeforeachfsfuturefuture.applygenericsggplot2globalsgluegowergplotsgtablegtoolshardhathighrhmshtmltoolshtmlwidgetsigraphipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemimeModelMetricsmvtnormnlmennetnumDerivorigamiparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrproxypurrrR.methodsS3R.ooR.utilsR6rappdirsrbibutilsRColorBrewerRcppRdpackrecipesreshape2rlangrmarkdownROCRrpartrstackdequeS7sassscalesshapesl3sparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetinytextmle3tzdbutf8uuidvctrsviridisLitevisNetworkwithrxfunyaml

Targeted Learning with Stochastic Treatment Regimes
Introduction | Data and Notation | Simulate Data | Methodology | Interlude: Constructing Optimal Stacked Regressions with sl3 | Targeted Estimation of Stochastic Interventions Effects | Statistical Inference for Targeted Maximum Likelihood Estimates | References
Rendered fromshift_tmle.Rmdusingknitr::rmarkdown

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

Variable Importance Analysis with Stochastic Interventions
Introduction | Data and Notation | Simulate Data | Methodology | Defining a grid of counterfactual interventions | Interlude: Constructing Optimal Stacked Regressions with sl3 | Initializing vimshift through its tmle3_Spec | Targeted Estimation of Stochastic Interventions Effects | Inference with Marginal Structural Models | Directly Targeting the MSM Parameter $\beta$ | References
Rendered fromvimshift.Rmdusingknitr::rmarkdown

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