rdacca.hp: Hierarchical and Variation Partitioning for Canonical Analysis

This function conducts variation partitioning and hierarchical partitioning to calculate the unique, shared (referred as to "common") and individual contributions of each predictor (or matrix) towards explained variation (R-square and adjusted R-square) on canonical analysis (RDA,CCA and db-RDA), 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:	1.0-8
Depends:	R (≥ 3.4.0), vegan, ggplot2
Published:	2022-04-05
Author:	Jiangshan Lai,Pedro Peres-Neto,Kim Nimon
Maintainer:	Jiangshan Lai <lai at ibcas.ac.cn>
License:	GPL-2 | GPL-3 [expanded from: GPL]
URL:	https://github.com/laijiangshan/rdacca.hp
NeedsCompilation:	no
Citation:	rdacca.hp citation info
CRAN checks:	rdacca.hp results

Documentation:

Reference manual:

rdacca.hp.pdf

Downloads:

Package source:	rdacca.hp_1.0-8.tar.gz
Windows binaries:	r-devel: rdacca.hp_1.0-8.zip, r-release: rdacca.hp_1.0-8.zip, r-oldrel: rdacca.hp_1.0-8.zip
macOS binaries:	r-release (arm64): rdacca.hp_1.0-8.tgz, r-oldrel (arm64): rdacca.hp_1.0-8.tgz, r-release (x86_64): rdacca.hp_1.0-8.tgz, r-oldrel (x86_64): rdacca.hp_1.0-8.tgz
Old sources:	rdacca.hp archive

Reverse dependencies:

Reverse imports:

UpSetVP

Linking:

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