Marginal Maximum Likelihood Estimation for Item Response Models // 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) doi:10.1177/0146621697211001.