CRAN/E | bartMan

bartMan

Create Visualisations for BART Models

Installation

About

Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) doi:10.1214/09-AOAS285 model fits. We construct conventional plots to analyze a model’s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our visualisations are designed to work with the most popular BART R packages available, namely 'BART' Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 doi:10.18637/jss.v097.i01, 'dbarts' (Vincent Dorie 2023) , and 'bartMachine' (Adam Kapelner and Justin Bleich 2016) doi:10.18637/jss.v070.i04.

Key Metrics

Version 0.1.0
R ≥ 3.5.0
Published 2024-04-15 20 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks bartMan results

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Maintainer

Maintainer

Alan Inglis

alan.inglis@mu.ie

Authors

Alan Inglis

aut / cre

Andrew Parnell

aut

Catherine Hurley

aut

Claus Wilke

ctb

(Developer of VSUP script)

Material

README
Reference manual
Package source

macOS

r-prerel

arm64

r-release

arm64

r-oldrel

arm64

r-prerel

x86_64

r-release

x86_64

Windows

r-prerelnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Depends

R ≥ 3.5.0

Imports

colorspace
cowplot
DendSer
dplyr
ggiraph
ggnewscale
ggplot2
ggraph
grid
grDevices
gtable
igraph
patchwork
purrr
rlang
rrapply
scales
stats
tidybayes
tidygraph
tidyr
tidytreatment
utils
tibble
BART
bartMachine
dbarts
rJava
cli

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