OrdPens: An R package for Selection, Smoothing and Principal Components Analysis for Ordinal Variables

Aisouda Hoshiyar

doi:10.21105/joss.03828

Abstract

Hoshiyar, A., (2021). ordPens: An R package for Selection, Smoothing and Principal Components Analysis for Ordinal Variables. Journal of Open Source Software, 6(68), 3828, https://doi.org/10.21105/joss.03828

Highlights

Ordinal data are a common case in applied statistics
In order to incorporate the ordinal scale level, among other things, regularization techniques are often suggested in the literature (Tutz & Gertheiss, 2014, 2016)
Penalization approaches for smoothing and selection when dealing with Likert-type data – which are by no means restricted to Likert scale – are commonly proposed. ordPens is a package in the R programming language (R Core Team, 2021) and provides several penalty approaches for ordinal predictors in regression models and ordinal variables for principal component analysis (PCA)

Summary

Introduction

Penalization approaches for smoothing and selection when dealing with Likert-type data – which are by no means restricted to Likert scale – are commonly proposed. OrdPens is a package in the R programming language (R Core Team, 2021) and provides several penalty approaches for ordinal predictors in regression models and ordinal variables for principal component analysis (PCA). Different types of penalization can be considered, depending on whether to achieve smoothing, selection or clustering of variables.

Results

Conclusion