The R Journal: article published in 2021, volume 13:2

PAsso: an R Package for Assessing Partial Association between Ordinal Variables
Shaobo Li, Xiaorui Zhu, Yuejie Chen and Dungang Liu , The R Journal (2021) 13:2, pages 239-252.

Abstract Partial association, the dependency between variables after adjusting for a set of covariates, is an important statistical notion for scientific research. However, if the variables of interest are ordered categorical data, the development of statistical methods and software for assessing their partial association is limited. Following the framework established by Liu et al. (2021), we develop an R package PAsso for assessing Partial Associations between ordinal variables. The package provides various functions that allow users to perform a wide spectrum of assessments, including quantification, visualization, and hypothesis testing. In this paper, we discuss the implementation of PAsso in detail and demonstrate its utility through an analysis of the 2016 American National Election Study.

Received: 2020-10-19; online 2021-10-19, supplementary material, (1 KiB)
CRAN packages: PAsso, sure, MASS, stats, pcaPP, copBasic, rms, ordinal, VGAM, GGally, ggplot2, plotly
CRAN Task Views implied by cited CRAN packages: Econometrics, Psychometrics, Distributions, Multivariate, SocialSciences, Environmetrics, Robust, Survival, TeachingStatistics, ChemPhys, ExtremeValue, NumericalMathematics, Phylogenetics, ReproducibleResearch, WebTechnologies

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-088,
  author = {Shaobo Li and Xiaorui Zhu and Yuejie Chen and Dungang Liu},
  title = {{PAsso: an R Package for Assessing Partial Association
          between Ordinal Variables}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-088},
  url = {https://doi.org/10.32614/RJ-2021-088},
  pages = {239--252},
  volume = {13},
  number = {2}
}