UpSetVP: An Alternative Visualization of VPA and HP in Canonical Analysis

Using matrix layout to visualize the unique, common, or individual contribution of each predictor (or matrix of predictors) towards explained variation on canonical analysis. These contributions were derived from variance partitioning analysis (VPA) and hierarchical partitioning (HP), applying the algorithm of Lai J., Zou Y., Zhang J., Peres-Neto P. (2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution, 13: 782-788 <doi:10.1111/2041-210X.13800>.

Version:	1.0.0
Depends:	R (≥ 3.6.0)
Imports:	rdacca.hp, ggplot2, patchwork, grDevices
Suggests:	adespatial
Published:	2022-05-03
DOI:	10.32614/CRAN.package.UpSetVP
Author:	Yao Liu
Maintainer:	Yao Liu <lyao222lll at nwafu.edu.cn>
BugReports:	https://github.com/LiuXYh/UpSetVP/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/LiuXYh/UpSetVP
NeedsCompilation:	no
CRAN checks:	UpSetVP results

Documentation:

Reference manual:

UpSetVP.pdf

Downloads:

Package source:	UpSetVP_1.0.0.tar.gz
Windows binaries:	r-devel: UpSetVP_1.0.0.zip, r-release: UpSetVP_1.0.0.zip, r-oldrel: UpSetVP_1.0.0.zip
macOS binaries:	r-release (arm64): UpSetVP_1.0.0.tgz, r-oldrel (arm64): UpSetVP_1.0.0.tgz, r-release (x86_64): UpSetVP_1.0.0.tgz, r-oldrel (x86_64): UpSetVP_1.0.0.tgz

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