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KernSmoothIRT (version 6.4)

Nonparametric Item Response Theory

Description

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.

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Version

Install

install.packages('KernSmoothIRT')

Monthly Downloads

245

Version

6.4

License

GPL-2

Maintainer

Last Published

February 17th, 2020

Functions in KernSmoothIRT (6.4)

subjEISDIF

ksIRT - kernel smoothing in Item Response Theory
HIV

The HIV Data
itemcor

ksIRT - kernel smoothing in Item Response Theory
PCA

ksIRT - kernel smoothing in Item Response Theory
subjEIS

ksIRT - kernel smoothing in Item Response Theory
KernSmoothIRT-package

KernSmoothIRT Package
plot.ksIRT

Plot Method for ksIRT - kernel smoothing in Item Response Theory
Psych101

The Introductory Psychology Data
BDI

The Beck Depression Inventory Data
ksIRT

ksIRT - kernel smoothing in Item Response Theory
subjETS

ksIRT - kernel smoothing in Item Response Theory
subjthetaML

ksIRT - kernel smoothing in Item Response Theory
subjscore

ksIRT - kernel smoothing in Item Response Theory
subjscoreML

ksIRT - kernel smoothing in Item Response Theory
subjOCCDIF

ksIRT - kernel smoothing in Item Response Theory
subjOCC

ksIRT - kernel smoothing in Item Response Theory
subjETSDIF

ksIRT - kernel smoothing in Item Response Theory