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) ().


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("PAsso")

0.1.8 by Xiaorui (Jeremy) Zhu, 3 months ago


GitHub: https://github.com/XiaoruiZhu/PAsso


Report a bug at https://github.com/XiaoruiZhu/PAsso/issues


Browse source code at https://github.com/cran/PAsso


Authors: Xiaorui (Jeremy) Zhu [aut, cre] , Shaobo Li [aut] , Yuejie Chen [ctb] , Dungang Liu [ctb]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports VGAM, copBasic, pcaPP, methods, doParallel, foreach, tidyverse, MASS, GGally, goftest, gridExtra, utils, progress, plotly, copula

Depends on stats, ggplot2, dplyr

Suggests faraway, ordinal, rms, testthat, mgcv, PResiduals, knitr, rmarkdown

Linking to Rcpp


See at CRAN