Self-contained marginal maximum likelihood (MML) estimation for
unidimensional and multidimensional item response models, including the
Rasch / one-parameter logistic, partial credit, rating scale, two-parameter
logistic and generalised partial credit models, with latent regression,
multiple groups and case weights. A parallelised, dimension-factorised
streaming estimation engine supports large between-item (simple-structure)
multidimensional models with bounded memory and an opt-in controlled-accuracy
quadrature mode that reports a measured approximation error. A usability
layer serves non-specialists and automated pipelines: one-stop estimation
from common file formats ('Excel', delimited text, 'SPSS', 'Stata', 'SAS')
with automatic cleaning and answer-key scoring, pre-estimation data checks,
classical item statistics and item fit, plain-language quality ratings,
bilingual (English/Chinese) output, spreadsheet exports for item banking and
cross-year linking, audience-specific 'Word'/'HTML' reports, and
machine-readable results with structured error conditions. Methods follow
Adams, Wilson and Wang (1997)