# gbm v2.1.8

Monthly downloads

## 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.

## Readme

# gbm

## Overview

The gbm package, which stands
for **g**eneralized **b**oosted **m**odels, provides extensions to
Freund and Schapire’s AdaBoost algorithm and Friedman’s gradient
boosting machine. It
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
(i.e.,
LambdaMart).

## Installation

```
# The easiest way to get gbm is to it install from CRAN:
install.packages("gbm")
# Alternatively, you can install the development version from GitHub:
if (!requireNamespace("remotes")) {
install.packages("remotes")
}
remotes::install_github("gbm-developers/gbm")
```

## Lifecycle

The gbm package is retired and no longer under active development. We will only make the necessary changes to ensure that gbm remains on CRAN. For the most part, no new features will be added, and only the most critical of bugs will be fixed.

This is a maintained version of
gbm back compatible to CRAN
versions of gbm 2.1.x. It
exists mainly for the purpose of reproducible research and data analyses
performed with the 2.1.x versions of
gbm. ~~For newer development,
and a more consistent API, try out the
gbm3 package!~~

## Functions in gbm

Name | Description | |

gbm.fit | Generalized Boosted Regression Modeling (GBM) | |

guessDist | gbm internal functions | |

gbm.perf | GBM performance | |

gbm | Generalized Boosted Regression Modeling (GBM) | |

gbm.more | Generalized Boosted Regression Modeling (GBM) | |

calibrate.plot | Calibration plot | |

gbm.object | Generalized Boosted Regression Model Object | |

basehaz.gbm | Baseline hazard function | |

gbm-package | Generalized Boosted Regression Models (GBMs) | |

pretty.gbm.tree | Print gbm tree components | |

gbm.roc.area | Compute Information Retrieval measures. | |

reconstructGBMdata | Reconstruct a GBM's Source Data | |

summary.gbm | Summary of a gbm object | |

relative.influence | Methods for estimating relative influence | |

quantile.rug | Quantile rug plot | |

print.gbm | Print model summary | |

plot.gbm | Marginal plots of fitted gbm objects | |

predict.gbm | Predict method for GBM Model Fits | |

gbmCrossVal | Cross-validate a gbm | |

interact.gbm | Estimate the strength of interaction effects | |

test.gbm | Test the gbm package. | |

No Results! |

## Vignettes of gbm

Name | ||

gbm-concordance.tex | ||

gbm.Rnw | ||

gbm.bib | ||

oobperf2.pdf | ||

shrinkage-v-iterations.pdf | ||

srcltx.sty | ||

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## Last month downloads

## Details

License | GPL (>= 2) | file LICENSE |

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

BugReports | https://github.com/gbm-developers/gbm/issues |

Encoding | UTF-8 |

RoxygenNote | 7.1.1 |

VignetteBuilder | knitr |

NeedsCompilation | yes |

Packaged | 2020-07-13 15:15:55 UTC; b780620 |

Repository | CRAN |

Date/Publication | 2020-07-15 10:00:02 UTC |

suggests | covr , gridExtra , knitr , pdp , RUnit , splines , tinytest , vip , viridis |

imports | lattice , parallel , survival |

depends | R (>= 2.9.0) |

Contributors | Bradley Boehmke, Jay Cunningham, GBM Developers |

#### Include our badge in your README

```
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```