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>.

Includes a collection of shiny applications to demonstrate
  or to explore fundamental item response theory (IRT) concepts
  such as estimation, scoring, and multidimensional IRT models.

Item response theory (IRT) parameter estimation using marginal maximum likelihood and expectation-maximization algorithm 
    (Bock \& Aitkin, 1981 <doi:10.1007/BF02293801>). 
    Within parameter estimation algorithm, several methods for latent distribution estimation are available.
    Reflecting some features of the true latent distribution, these latent distribution estimation methods can possibly enhance the estimation accuracy and free the normality assumption on the latent distribution.

Performing Item Response Theory analysis such as parameter estimation, ability estimation, data generation, item and model fit analyse, local independence assumption, dimensionality assumption, wright map, characteristic and information curves under various models with a user-friendly Graphic User Interface.