文献摘要：
Item response theory (IRT) models are a class of statistical models used to describe
the response behaviors of individuals to a set of items having a certain number of options.
They are adopted by researchers in social science, particularly in the analysis of perfor-
mance or attitudinal data, in psychology, education, medicine, marketing and other fields
where the aim is to measure latent constructs. Most IRT analyses use parametric models
that rely on assumptions that often are not satisfied. In such cases, a nonparametric
approach might be preferable; nevertheless, there are not many software implementations
allowing to use that.
To address this gap, this paper presents the R package KernSmoothIRT. It implements
kernel smoothing for the estimation of option characteristic curves, and adds several plot-
ting and analytical tools to evaluate the whole test/questionnaire, the items, and the
subjects. In order to show the package’s capabilities, two real datasets are used, one
employing multiple-choice responses, and the other scaled responses.


该文献偏向于实践应用方面，供参考。