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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 gmail.com> |
| 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: KernSmoothIRT_6.4.zip, r-release: KernSmoothIRT_6.4.zip, r-oldrel: KernSmoothIRT_6.4.zip |
| 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.