IRTC 1.1.1

Packaging-only release addressing the CRAN incoming pre-test results for 1.1.0. No user-visible behaviour, no API and no estimation results change.

Documentation

• The Rd sources reaching LaTeX are now ASCII, so the PDF reference manual builds without errors. Chinese column-name aliases are still documented: the new \zh Rd macro shows the Chinese characters in the HTML and text help and the equivalent \uxxxx escape in the PDF manual.

• DESCRIPTION gains a Date field, so the package banner reads IRTC 1.1.1 (2026-07-17) instead of IRTC 1.1.1 ().

Internal

• tests/testthat/test-print-session.R no longer assumes the released R wording "R version", which does not hold on r-devel ("R Under development (unstable)"). It now compares against R.version.string.

IRTC 1.1.0

Usability release focused on the GPCM / multidimensional workflow. The estimation core (irtc.mml / irtc.mml.2pl) is unchanged; all new behaviour lives in the usability layer and is backward compatible. New optional dependencies: none.

New features

• irtc_read() gains sampling-weight import: a weights= argument plus automatic detection of common weight column names (English and Chinese). Weights are validated (positive numbers; missing set to 1 with a warning), kept aligned when empty rows are dropped, and shown by the print method. irtc() forwards them as pweights.
• irtc_read_q() and irtc_align_q(): read a Q (item-by-dimension) matrix from any supported file format or an R object, with an optional partial-credit / maximum-score declaration column. Dimension column headers become the dimension names used in all person-level output. Alignment against the response data warns on item mismatches and keeps the shared items by default, or stops with on_mismatch = "error".
• irtc(q = , on_mismatch = ): supply a Q matrix to irtc() directly; it is aligned and passed to the estimation.
• irtc_score() / irtc(): key and rules now also accept file paths in any supported format. Answer-key files may carry a partial-answer column, giving partial-credit scoring (full = 2, partial = 1, other = 0). Consistency between the Q-matrix partial-credit declaration and the applied scoring is checked.
• irtc(rare_categories = ): robust handling of score categories that nobody reached. "collapse" (default) merges unobserved categories and annotates the mapping; "prior" keeps the category structure by stabilising the affected thresholds. Items nobody answered keep an annotated row in irtc_results() instead of silently disappearing.
• GPCM item output now labels the partial-correct and full-correct difficulties (b_partial / b_full, or b_step1..b_stepK). Person output uses the Q dimension names for ability / standard-error headers. irtc_results() schema advances to 1.1 (additive only).
• irtc_report() gains a Model-diagnostics section (convergence, information criteria, EAP reliability bands, item-fit reading) and a Data-processing-transparency section (weights, Q alignment, category collapses, dropped items, scoring summary, cleaning log).

Refinements

• irtc_report() now creates any missing parent directories of the output file, matching irtc_excel().
• Automatic sampling-weight detection no longer treats a bare w column as weights; it was an undocumented alias that could silently consume a binary item column named w. Explicit weights = "w" still works.

IRTC 1.0.0

First CRAN release. The estimation core is unchanged from 0.1.0; this release adds a usability layer for four audiences: survey staff without statistical training, professional statisticians, AI agents / automated pipelines, and decision makers receiving the results.

New features

• irtc(): one-stop estimation. Accepts a file path (.xlsx, .xls, .csv, .tsv, .txt, .dat, .sav, .por, .dta, .sas7bdat, .xpt) or a data frame/matrix; cleans, optionally scores raw responses against an answer key, checks the data, estimates the requested model (model is required: "1PL"/"Rasch", "2PL", "PCM", "PCM2", "RSM", "GPCM") and attaches classical statistics, item fit and quality ratings. All extra arguments pass through to irtc.mml() / irtc.mml.2pl(), which are unchanged.
• irtc_read(): unified import with automatic delimiter and UTF-8/GBK encoding detection, person-ID detection (English and Chinese column names), missing-code recoding with a range guard, category recoding to consecutive 0-based scores, and a bilingual cleaning log.
• irtc_score(): answer-key (0/1) and partial-credit rules scoring with normalisation of case, whitespace and full-width characters.
• irtc_check_data(): pre-estimation diagnostics; returns a machine-readable issue table (code / severity / where / bilingual message / fix).
• irtc_ctt(): item difficulty, corrected item-total correlations, Cronbach’s alpha and alpha-if-item-deleted.
• irtc_itemfit(): infit/outfit mean squares with Wilson-Hilferty t statistics, for both the grid and the streaming engine.
• irtc_quality(): four-level plain-language item quality ratings (good / acceptable / review / revise) with bilingual reasons and advice; thresholds are configurable via irtc_quality_thresholds().
• plain_summary(): layered plain-language summary (conclusion first).
• irtc_excel(): writes three separate Excel workbooks - a plain-language item quality table (colour-coded), an item difficulty/discrimination table with a frozen schema for cross-year anchor linking, and a flat, paste-ready person ability table. Requires the optional ‘openxlsx’.
• irtc_report(): audience-specific reports (decision, survey, stat) as self-contained HTML or Word (optional ‘officer’), with Wright map, ability distribution, quality summary and ICC figures.
• plot.irtc(): wright, ability, quality and icc plot types.
• irtc_results() / irtc_json(): machine-readable results with a stable documented schema (see inst/llms.txt); JSON export via the optional ‘jsonlite’.
• Structured conditions: all errors/warnings of the usability layer carry classes (irtc_error, domain classes) and fields code, reason, fix, data, enabling programmatic recovery.
• Bilingual output (Chinese default, English via options(irtc.lang = "en")); machine-readable schemas are language-independent.
• inst/llms.txt: compact API and schema reference for AI agents.

Notes

• All new dependencies are optional (Suggests) and requested with an actionable installation hint when needed; the estimation core adds no hard dependencies beyond 0.1.0 (only ‘tools’, ‘graphics’, ‘grDevices’ from base R).

IRTC 0.1.0

• Initial version: MML estimation for Rasch/1PL, PCM, PCM2, RSM, 2PL and GPCM models, unidimensional and between-item multidimensional, with latent regression, multiple groups, case weights, EAP person estimates, parallel grid engine, bounded-memory streaming engine and an opt-in controlled-accuracy quadrature mode with a measured error report.