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ROSE (RObust Semiparametric Efficient) random forests for robust semiparametric efficient estimation in partially parametric models (containing generalised partially linear models). Details can be found in the paper by Young and Shah (2024) <doi:10.48550/arXiv.2410.03471>.
| Version: | 0.1.0 |
| Depends: | R (≥ 4.2.0) |
| Imports: | caret (≥ 6.0.93), glmnet (≥ 4.1.6), keras, mgcv, mlr (≥ 2.19.1), ParamHelpers, ranger (≥ 0.14.1), grf, rpart, stats, tuneRanger (≥ 0.5), xgboost |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2024-10-11 |
| DOI: | 10.32614/CRAN.package.roseRF |
| Author: | Elliot H. Young [aut, cre], Rajen D. Shah [aut] |
| Maintainer: | Elliot H. Young <ey244 at cam.ac.uk> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | roseRF results |
| Reference manual: | roseRF.pdf |
| Package source: | roseRF_0.1.0.tar.gz |
| Windows binaries: | r-devel: roseRF_0.1.0.zip, r-release: roseRF_0.1.0.zip, r-oldrel: roseRF_0.1.0.zip |
| macOS binaries: | r-release (arm64): roseRF_0.1.0.tgz, r-oldrel (arm64): roseRF_0.1.0.tgz, r-release (x86_64): roseRF_0.1.0.tgz, r-oldrel (x86_64): roseRF_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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