gbm

Generalized Boosted Regression Models

An implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway.

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Description file content

Package
gbm
Version
2.1.5
Title
Generalized Boosted Regression Models
Depends
R (>= 2.9.0)
Imports
gridExtra, lattice, parallel, survival
Suggests
knitr, pdp, RUnit, splines, viridis
Description
An implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway.
License
GPL (>= 2) | file LICENSE
URL
BugReports
https://github.com/gbm-developers/gbm/issues
RoxygenNote
6.1.1
VignetteBuilder
knitr
NeedsCompilation
yes
Packaged
2019-01-14 14:21:52 UTC; bgreenwell
Author
Brandon Greenwell [aut, cre] (), Bradley Boehmke [aut] (), Jay Cunningham [aut], GBM Developers [aut] (https://github.com/gbm-developers)
Maintainer
Brandon Greenwell
Repository
CRAN
Date/Publication
2019-01-14 15:00:03 UTC

install.packages('gbm')

2.1.5

4 months ago

https://github.com/gbm-developers/gbm

Brandon Greenwell

GPL (>= 2) | file LICENSE

Depends on

R (>= 2.9.0)

Imports

gridExtra, lattice, parallel, survival

Suggests

knitr, pdp, RUnit, splines, viridis

Discussions