Package: twoStageDesignTMLE 1.0.1.2
twoStageDesignTMLE: Targeted Maximum Likelihood Estimation for Two-Stage Study Design
An inverse probability of censoring weighted (IPCW) targeted maximum likelihood estimator (TMLE) for evaluating a marginal point treatment effect from data where some variables were collected on only a subset of participants using a two-stage design (or marginal mean outcome for a single arm study). A TMLE for conditional parameters defined by a marginal structural model (MSM) is also available.
Authors:
twoStageDesignTMLE_1.0.1.2.tar.gz
twoStageDesignTMLE_1.0.1.2.zip(r-4.7)twoStageDesignTMLE_1.0.1.2.zip(r-4.6)twoStageDesignTMLE_1.0.1.2.zip(r-4.5)
twoStageDesignTMLE_1.0.1.2.tgz(r-4.6-any)twoStageDesignTMLE_1.0.1.2.tgz(r-4.5-any)
twoStageDesignTMLE_1.0.1.2.tar.gz(r-4.7-any)twoStageDesignTMLE_1.0.1.2.tar.gz(r-4.6-any)
twoStageDesignTMLE_1.0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
twoStageDesignTMLE/json (API)
NEWS
| # Install 'twoStageDesignTMLE' in R: |
| install.packages('twoStageDesignTMLE', repos = c('https://sg-tlr.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:8939221cf9. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 130 | ||
| source / vignettes | OK | 203 | ||
| linux-release-x86_64 | OK | 159 | ||
| macos-release-arm64 | OK | 160 | ||
| macos-oldrel-arm64 | OK | 150 | ||
| windows-devel | OK | 85 | ||
| windows-release | OK | 83 | ||
| windows-oldrel | OK | 85 | ||
| wasm-release | OK | 99 |
Exports:estimatePievalAugWsetVtwoStageDesignTMLENewstwoStageTMLEtwoStageTMLEmsm
Dependencies:bitopscaToolscodetoolscvAUCdata.tableforeachgamglmnetgplotsgtoolsiteratorsKernSmoothlatticeMatrixnnlsRcppRcppEigenROCRshapeSuperLearnersurvivaltmle
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| estimatePi | estimatePi |
| .evalAugW calls TMLE to use super learner to evalute preliminary predictions for Q(0,W) and Q(1,W) conditioning on stage 1 covariates | evalAugW |
| print.summary.twoStageTMLE | print.summary.twoStageTMLE |
| print.twoStageTMLE | print.twoStageTMLE |
| Utilities setV Set the number of cross-validation folds as a function of effective sample size See Phillips 2023 doi.org/10.1093/ije/dyad023 | setV |
| summary.twoStageTMLE | summary.twoStage |
| summary.twoStageTMLE | summary.twoStageTMLE |
| twoStageDesignTMLENews Get news about recent updates and bug fixes | twoStageDesignTMLENews |
| twoStageTMLE | twoStageTMLE |
| twoStageTMLEmsm | twoStageTMLEmsm |
