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treemisc

Miscellaneous data sets and functions to accompany An Introduction to Tree-Based Methods (with Examples in R), a forthcoming title to be published by Chapman & Hall/CRC Data Science Series.

Installation

You can install the development version of treemisc from GitHub with:

# install.packages("remotes")
remotes::install_github("bgreenwell/treemisc")

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Version

Install

install.packages('treemisc')

Monthly Downloads

16

Version

0.0.1

License

GPL (>= 2)

Maintainer

Brandon Greenwell

Last Published

October 19th, 2022

Functions in treemisc (0.0.1)

lift

Gain and lift charts
load_eslmix

Gaussian mixture data
mushroom

Mushroom edibility
predict.rftree

Model predictions
lsboost

Gradient tree boosting with least squares (LS) loss
ladboost

Gradient tree boosting with least absolute deviation (LAD) loss
predict.rforest

Random forest predictions
proximity

Proximity matrix
isle_post

Importance sampled learning ensemble
prune_se

Prune an rpart object
rforest

Random forest
rftree

Random forest tree
wilson_hilferty

Modified Wilson-Hilferty approximation
xy_grid

Create a Cartesian product from evenly spaced values of two variables
rrm

Random rotation matrix
tree_diagram

Tree diagram
wine

Wine quality
treemisc-package

tools:::Rd_package_title("treemisc")
suppressRegressionWarning

Suppress randomForest() warning message
decision_boundary

Add decision boundary to a scatterplot.
calibrate

External probability calibration
hitters

Baseball data (corrected)
cummean

Cumulative means
gbm_2way

Two-way interactions
guide_setup

Generate GUIDE input files
gen_friedman1

Friedman benchmark data
gen_mease

Generate data from the Mease model
banknote2

Swiss banknote data (UCI version)
banknote

Swiss banknote data