PAsso: Assessing the Partial Association Between Ordinal Variables

An implementation of the unified framework for assessing partial association between ordinal variables after adjusting a set of covariates (Dungang Liu, Shaobo Li, Yan Yu and Irini Moustaki (2020). Accepted by JASA). This package provides a set of tools to quantify partial association, conduct hypothesis testing for partial association, visualize partial regression models, and diagnose the specifications of each fitted model. This framework is based on the surrogate approach described in Liu and Zhang (2017) (<doi:10.1080/01621459.2017.1292915>).

Version: 0.1.8
Depends: R (≥ 3.5.0), stats (≥ 3.5.0), ggplot2 (≥ 2.2.1), dplyr
Imports: VGAM, copBasic, pcaPP (≥ 1.9-73), methods, doParallel (≥ 1.0.11), foreach (≥ 1.4.8), tidyverse, MASS (≥ 7.3-51.0), GGally, goftest, gridExtra, utils (≥ 3.5.3), progress (≥ 1.2.0), plotly, copula
LinkingTo: Rcpp
Suggests: faraway, ordinal, rms, testthat, mgcv, PResiduals, knitr, rmarkdown
Published: 2020-07-13
Author: Xiaorui (Jeremy) Zhu [aut, cre], Shaobo Li [aut], Yuejie Chen [ctb], Dungang Liu [ctb]
Maintainer: Xiaorui (Jeremy) Zhu <zhuxiaorui1989 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: GitHub:
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: PAsso results


Reference manual: PAsso.pdf
Package source: PAsso_0.1.8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: PAsso_0.1.8.tgz, r-oldrel: PAsso_0.1.8.tgz


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