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]

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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'))

Peer review:

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

On CRAN:

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

4.83 score 16 stars 42 scripts 13 exports 124 dependencies

Last updated 1 months agofrom:0c3b8f07d8. Checks:OK: 5 NOTE: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 29 2024
R-4.5-winNOTEOct 29 2024
R-4.5-linuxNOTEOct 29 2024
R-4.4-winOKOct 29 2024
R-4.4-macOKOct 29 2024
R-4.3-winOKOct 29 2024
R-4.3-macOKOct 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.Rmdusingknitr::rmarkdownon Oct 29 2024.

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

Variable Importance Analysis with Stochastic Interventions

Rendered fromvimshift.Rmdusingknitr::rmarkdownon Oct 29 2024.

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