KernSmoothIRT: Nonparametric Item Response Theory

Fits nonparametric item and option characteristic curves using kernel smoothing. It allows for optimal selection of the smoothing bandwidth using cross-validation and a variety of exploratory plotting tools. The kernel smoothing is based on methods described in Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall, London.

Version: 6.4
Imports: Rcpp, plotrix, rgl, methods
LinkingTo: Rcpp
Published: 2020-02-17
DOI: 10.32614/CRAN.package.KernSmoothIRT
Author: Angelo Mazza, Antonio Punzo, Brian McGuire
Maintainer: Brian McGuire <mcguirebc at>
License: GPL-2
NeedsCompilation: yes
Citation: KernSmoothIRT citation info
CRAN checks: KernSmoothIRT results


Reference manual: KernSmoothIRT.pdf


Package source: KernSmoothIRT_6.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): KernSmoothIRT_6.4.tgz, r-oldrel (arm64): KernSmoothIRT_6.4.tgz, r-release (x86_64): KernSmoothIRT_6.4.tgz, r-oldrel (x86_64): KernSmoothIRT_6.4.tgz
Old sources: KernSmoothIRT archive


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