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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
6.4 |
rolling linux/jammy R-4.5 | KernSmoothIRT_6.4.tar.gz |
213.6 KiB |
6.4 |
rolling linux/noble R-4.5 | KernSmoothIRT_6.4.tar.gz |
214.8 KiB |
6.4 |
rolling source/ R- | KernSmoothIRT_6.4.tar.gz |
83.0 KiB |
6.4 |
latest linux/jammy R-4.5 | KernSmoothIRT_6.4.tar.gz |
213.6 KiB |
6.4 |
latest linux/noble R-4.5 | KernSmoothIRT_6.4.tar.gz |
214.8 KiB |
6.4 |
latest source/ R- | KernSmoothIRT_6.4.tar.gz |
83.0 KiB |
6.4 |
2026-04-26 source/ R- | KernSmoothIRT_6.4.tar.gz |
83.0 KiB |
6.4 |
2026-04-23 source/ R- | KernSmoothIRT_6.4.tar.gz |
83.0 KiB |
6.4 |
2026-04-09 windows/windows R-4.5 | KernSmoothIRT_6.4.zip |
579.5 KiB |
6.4 |
2025-04-20 source/ R- | KernSmoothIRT_6.4.tar.gz |
83.0 KiB |