Package: erboost 1.4
erboost: Nonparametric Multiple Expectile Regression via ER-Boost
Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression, proposed by Yang, Y., Qian, W. and Zou, H. (2018) <doi:10.1080/00949655.2013.876024>. The code is based on the 'gbm' package originally developed by Greg Ridgeway.
Authors:
erboost_1.4.tar.gz
erboost_1.4.zip(r-4.5)erboost_1.4.zip(r-4.4)erboost_1.4.zip(r-4.3)
erboost_1.4.tgz(r-4.4-x86_64)erboost_1.4.tgz(r-4.4-arm64)erboost_1.4.tgz(r-4.3-x86_64)erboost_1.4.tgz(r-4.3-arm64)
erboost_1.4.tar.gz(r-4.5-noble)erboost_1.4.tar.gz(r-4.4-noble)
erboost_1.4.tgz(r-4.4-emscripten)erboost_1.4.tgz(r-4.3-emscripten)
erboost.pdf |erboost.html✨
erboost/json (API)
| # Install 'erboost' in R: |
| install.packages('erboost', repos = c('https://archer-yang-lab.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 10 months agofrom:056c604cd5. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 21 2024 |
| R-4.5-win-x86_64 | OK | Oct 22 2024 |
| R-4.5-linux-x86_64 | OK | Oct 22 2024 |
| R-4.4-win-x86_64 | OK | Nov 21 2024 |
| R-4.4-mac-x86_64 | OK | Nov 21 2024 |
| R-4.4-mac-aarch64 | OK | Nov 21 2024 |
| R-4.3-win-x86_64 | OK | Nov 21 2024 |
| R-4.3-mac-x86_64 | OK | Nov 21 2024 |
| R-4.3-mac-aarch64 | OK | Nov 21 2024 |
Exports:erboosterboost.fiterboost.losserboost.moreerboost.perfpermutation.test.erboostplot.erboostpredict.erboostrelative.influencesummary.erboost
Dependencies:lattice
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| ER-Boost Expectile Regression Modeling | erboost erboost.fit erboost.more |
| ER-Boost Expectile Regression Model Object | erboost.object |
| erboost performance | erboost.perf |
| Marginal plots of fitted erboost objects | plot.erboost |
| Predict method for erboost Model Fits | predict.erboost |
| Methods for estimating relative influence | erboost.loss permutation.test.erboost relative.influence |
| Summary of a erboost object | summary.erboost |
