bigIRT - Item Response Theory for Large Sparse Data
Fits binary item response theory (IRT) models for large
and sparse data in long format, by marginal maximum likelihood
with a Laplace approximation to the integral over person
abilities, or by the joint posterior mode over items and
persons. Supports 1PL to 4PL models, multidimensional and
confirmatory loading structures with an estimated latent
correlation, covariate effects on ability and on each item
parameter, empirical-Bayes prior adaptation, and
post-estimation tools for information, reliability, item fit,
held-out prediction and simulation.
