Marginal Maximum Likelihood Estimation for Item Response Models R package IRTC version 1.1.1 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>. Software Depends: R (>= 3.5.0) Imports: Rcpp, stats, utils, methods, MASS, tools, graphics, grDevices LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), mvtnorm, sfsmisc, readxl, writexl, haven, openxlsx, officer, jsonlite Kunxiang Ma <makunxiang@weiandata.com> Comprehensive R Archive Network (CRAN) Kunxiang Ma [aut, cre], WEIAN DATA TECH (Beijing) Co., Ltd. [cph, fnd] GPL (>= 2) 2026-07-24 application/tgz https://CRAN.R-project.org/package=IRTC doi:10.32614/CRAN.package.IRTC