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.

Version: 1.4
Depends: R (≥ 2.12.0), lattice, splines
Published: 2024-01-19
Author: Yi Yang [aut, cre] (http://www.math.mcgill.ca/yyang/), Hui Zou [aut] (http://users.stat.umn.edu/~zouxx019/), Greg Ridgeway [ctb, cph]
Maintainer: Yi Yang <yi.yang6 at mcgill.ca>
License: GPL-3
NeedsCompilation: yes
Materials: ChangeLog
CRAN checks: erboost results

Documentation:

Reference manual: erboost.pdf

Downloads:

Package source: erboost_1.4.tar.gz
Windows binaries: r-devel: erboost_1.4.zip, r-release: erboost_1.4.zip, r-oldrel: erboost_1.4.zip
macOS binaries: r-release (arm64): erboost_1.4.tgz, r-oldrel (arm64): erboost_1.4.tgz, r-release (x86_64): erboost_1.4.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=erboost to link to this page.