Various methods for targeted and semiparametric inference including
augmented inverse probability weighted (AIPW) estimators for missing data and
causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>),
variable importance and conditional average treatment effects (CATE)
(van der Laan (2006) <doi:10.2202/1557-4679.1008>),
estimators for risk differences and relative risks (Richardson et al. (2017)
<doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized
linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).
| Version: |
0.5 |
| Depends: |
R (≥ 4.0), lava (≥ 1.7.0) |
| Imports: |
data.table, digest, futile.logger, future.apply, optimx, progressr, methods, mets, R6, Rcpp (≥ 1.0.0), survival |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
grf, mgcv, testthat (≥ 0.11), rmarkdown, scatterplot3d, SuperLearner (≥ 2.0-28), knitr, xgboost, viridisLite |
| Published: |
2024-02-22 |
| DOI: |
10.32614/CRAN.package.targeted |
| Author: |
Klaus K. Holst [aut, cre],
Andreas Nordland [aut] |
| Maintainer: |
Klaus K. Holst <klaus at holst.it> |
| BugReports: |
https://github.com/kkholst/targeted/issues |
| License: |
Apache License (== 2.0) |
| NeedsCompilation: |
yes |
| Materials: |
README, NEWS |
| In views: |
MissingData |
| CRAN checks: |
targeted results |