Standard and Nonstandard Statistical Models and Methods for Test Equating
Automatic selection of the bandwidth parameter h
Pre-smoothing using beta4 models.
Prediction step for Bayesian non-parametric model for test equating
Bayesian non-parametric model for test equating
Pre-smoothing using discrete kernels.
The equipercentile method of equating
Functions to assess model fitting.
IRT methods for Test Equating
IRT parameter linking methods
The Kernel method of test equating
Local equating methods
The linear method of equating
Pre-smoothing using log-linear models.
The mean method of equating
Percent relative error
Take a matrix and sum blocks of rows
Standard error of equating difference
Simulate test scores.
Standard and Nonstandard Statistical Models and Methods for Test Equat...
Contains functions to perform various models and methods for test equating (Kolen and Brennan, 2014 <doi:10.1007/978-1-4939-0317-7> ; Gonzalez and Wiberg, 2017 <doi:10.1007/978-3-319-51824-4> ; von Davier et. al, 2004 <doi:10.1007/b97446>). It currently implements the traditional mean, linear and equipercentile equating methods. Both IRT observed-score and true-score equating are also supported, as well as the mean-mean, mean-sigma, Haebara and Stocking-Lord IRT linking methods. It also supports newest methods such that local equating, kernel equating (using Gaussian, logistic, Epanechnikov, uniform and adaptive kernels) with presmoothing, and IRT parameter linking methods based on asymmetric item characteristic functions. Functions to obtain both standard error of equating (SEE) and standard error of equating differences between two equating functions (SEED) are also implemented for the kernel method of equating.