Package: KernSmoothIRT 6.4

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.

Authors:Angelo Mazza, Antonio Punzo, Brian McGuire

KernSmoothIRT_6.4.tar.gz
KernSmoothIRT_6.4.zip(r-4.5)KernSmoothIRT_6.4.zip(r-4.4)KernSmoothIRT_6.4.zip(r-4.3)
KernSmoothIRT_6.4.tgz(r-4.4-x86_64)KernSmoothIRT_6.4.tgz(r-4.4-arm64)KernSmoothIRT_6.4.tgz(r-4.3-x86_64)KernSmoothIRT_6.4.tgz(r-4.3-arm64)
KernSmoothIRT_6.4.tar.gz(r-4.5-noble)KernSmoothIRT_6.4.tar.gz(r-4.4-noble)
KernSmoothIRT_6.4.tgz(r-4.4-emscripten)KernSmoothIRT_6.4.tgz(r-4.3-emscripten)
KernSmoothIRT.pdf |KernSmoothIRT.html
KernSmoothIRT/json (API)

# Install 'KernSmoothIRT' in R:
install.packages('KernSmoothIRT', repos = c('https://mcguirebc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

14 exports 1 stars 1.31 score 31 dependencies 4 mentions 21 scripts 246 downloads

Last updated 5 years agofrom:aa66d5b51a. Checks:OK: 4 NOTE: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 03 2024
R-4.5-win-x86_64NOTESep 03 2024
R-4.5-linux-x86_64NOTESep 03 2024
R-4.4-win-x86_64NOTESep 03 2024
R-4.4-mac-x86_64NOTESep 03 2024
R-4.4-mac-aarch64NOTESep 03 2024
R-4.3-win-x86_64OKSep 03 2024
R-4.3-mac-x86_64OKSep 03 2024
R-4.3-mac-aarch64OKSep 03 2024

Exports:itemcorksIRTPCAplot.ksIRTprint.ksIRTsubjEISsubjEISDIFsubjETSsubjETSDIFsubjOCCsubjOCCDIFsubjscoresubjscoreMLsubjthetaML

Dependencies:base64encbslibcachemclidigestevaluatefastmapfontawesomefsgluehighrhtmltoolshtmlwidgetsjquerylibjsonliteknitrlifecyclemagrittrmemoisemimeplotrixR6rappdirsRcpprglrlangrmarkdownsasstinytexxfunyaml