Package: tmle3shift 0.2.2
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:
tmle3shift_0.2.2.tar.gz
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tmle3shift.pdf |tmle3shift.html✨
tmle3shift/json (API)
NEWS
# 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
causal-inferencemachine-learningmarginal-structural-modelsstochastic-interventionstargeted-learningtreatment-effectsvariable-importance
Last updated 2 months agofrom:0c3b8f07d8. Checks:OK: 5 NOTE: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 29 2024 |
R-4.5-win | NOTE | Oct 29 2024 |
R-4.5-linux | NOTE | Oct 29 2024 |
R-4.4-win | OK | Oct 29 2024 |
R-4.4-mac | OK | Oct 29 2024 |
R-4.3-win | OK | Oct 29 2024 |
R-4.3-mac | OK | Oct 29 2024 |
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:abindassertthatbackportsbase64encBBmiscbitopsbslibcachemcaretcaToolscheckmateclasscliclockcodetoolscolorspacecpp11crayondata.tabledelayeddiagramdigestdplyre1071evaluatefansifarverfastmapfontawesomeforeachfsfuturefuture.applygenericsggplot2globalsgluegowergplotsgtablegtoolshardhathighrhmshtmltoolshtmlwidgetsigraphipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemgcvmimeModelMetricsmunsellmvtnormnlmennetnumDerivorigamiparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrproxypurrrR.methodsS3R.ooR.utilsR6rappdirsrbibutilsRColorBrewerRcppRdpackrecipesreshape2rlangrmarkdownROCRrpartrstackdequesassscalesshapesl3SQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetinytextmle3tzdbutf8uuidvctrsviridisLitevisNetworkwithrxfunyaml
Targeted Learning with Stochastic Treatment Regimes
Rendered fromshift_tmle.Rmd
usingknitr::rmarkdown
on Oct 29 2024.Last update: 2020-03-13
Started: 2018-06-01
Variable Importance Analysis with Stochastic Interventions
Rendered fromvimshift.Rmd
usingknitr::rmarkdown
on Oct 29 2024.Last update: 2020-03-13
Started: 2018-09-05
Readme and manuals
Help Manual
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 |