DCC 1.2.1

• dcc_check(), dcc_run(), and dcc_template() no longer default their write destination to the working directory. output_dir and path are now required; calling without them raises the package’s own error instead of writing to getwd(). Existing calls that pass a destination are unaffected.
• dcc_run() validates output_dir as one non-empty string before any work starts.
• Documentation and DESCRIPTION follow CRAN’s quoting convention: only third-party software names are single-quoted; file formats such as CSV, JSON, YAML, and HTML are not.

DCC 1.2.0

• Add stable finding code and detector_id; range checks now report INVALID_NUMERIC instead of silently coercing invalid values to missing.
• YAML rules use declared IDs publicly while preserving direct detector IDs; unambiguous legacy action aliases warn for one transition release.
• Add dcc_dispositions() with one terminal state per finding and make reconciliation verify those states against audit evidence.
• Add start/end/outcome/hash/count provenance and preserve legacy timestamp records.
• Preserve NA totals for entirely missing scored rows and validate custom scoring return type and length.
• Publish dcc_run() outputs atomically; manifest and other failures create a failed diagnostic directory and cannot be returned as success.
• Capability contract 1.2 and new disposition/provenance schemas describe the additive machine contract.
• Add a single format-adapter registry and strict dcc_import() canonical layer with dictionaries, explicit missing states, import-plan hashes, and source-aware conversion errors.
• Add declared text/FWF/JSONL/RDS input, bounded gzip and safe explicit-member ZIP input, XLS/XLSX/ODS and limited Experimental XLSB, SPSS/Stata/SAS/XPT, and Parquet/Feather adapters. All formal input backends are now installed with DCC.
• dcc_capabilities()$formats now reports registry status, extensions, backend, semantics, and limitations; dcc_doctor(formats = "all") checks backend versions and platform constraints.
• Add the versioned strict dcc_plan contract and published plan Schema; protected bilingual dcc_template() workbook; exact Excel/JSON parsing with cell coordinates or JSON Pointers; non-mutating dcc_check() diagnostics; additive dcc_run(plan =) support; and synchronized dcc_help() guidance.
• Add dcc_report_model() as a validated single source for three audience renderers: bilingual redacted staff Excel/HTML/text, complete statistical tables and methods with SHA-256 manifests, and deterministic versioned machine JSON/JSONL with bundled schemas.
• dcc_run() can atomically publish selected staff/, statistical/, and machine/ bundles. Run manifests expose report lifecycle states, common counts and hashes, and retain cleaning evidence on renderer failure. PDF is optional and is not generated by the base contract.
• Add Phase E acceptance and release engineering: generated property/fault suites, immutable multi-format fixtures with three-platform CI, reviewed one-million-row time and memory baselines, and strict dependency/install verification with a complete offline source-repository builder.
• Add explicit staff, statistician, and AI-Agent acceptance contracts. The staff path includes a validated facilitator workbook and requires signed real-user evidence for a staff-study pass; automated preparation cannot be reported as usability success, and staff-study status does not block release.
• Add a staff acceptance workbook supporting one or more real participants as an advisory, non-release-blocking usability study, and an executable twenty-task AI-Agent harness with closed per-task JSON, whitelist and validation/preview gates, artifact assertions, and evidence hashes.
• Strict plan validation now surfaces format preflight codes such as IMPORT_SOURCE_MISSING and IMPORT_SHEET_REQUIRED before import, and dcc_rerun() can reproduce manifests whose rules came from strict JSON or Excel plans through the same canonical import compiler.
• Add a closed release-evidence schema and one fail-closed verifier covering R checks, zero-skip tests, coverage, format semantics, benchmarks, all three audiences, dependencies, schemas, versions, freshness, and artifact hashes.
• Classify R CMD check NOTEs with an exact machine-readable allowlist: only the CRAN first-submission NOTE (cran_new_submission) is non-actionable; every other NOTE remains release-blocking.
• Performance CI now distinguishes GitHub-hosted ARM64 from local Apple Silicon and compares against a reviewed three-run hosted baseline with explicit CPU identity and a 45-second execution limit.
• Shared GitHub runner timing regressions are now reported as review advisories, while benchmark correctness, evidence compatibility, memory limits, minimum repetitions, and the 45-second execution budget remain release-blocking. Strict 20% relative comparison remains available for stable like-for-like hardware.

DCC 1.1.0

Additive-contracts release: machine-readable capabilities and formal schemas for AI callers, plus a one-command workflow and structured validators for survey staff. Every change is additive; no 1.0.x call changes shape.

• dcc_run() runs the whole Detect -> Execute -> Report pipeline from a dcc_config() and writes a fixed output layout (cleaned-data.csv, findings.xlsx, audit-log.csv, the two HTML reports, manifest.yaml, run-summary.txt). Preview is the default mode and the raw input file is never modified in any mode. dcc_run_files() lists what was written.
• dcc_read_config() reads an Excel cleaning-plan workbook into a dcc_config(), so survey staff configure a run in a spreadsheet instead of YAML; dcc_write_config_template() writes a starter workbook.
• dcc_apply_codebook() applies a declarative codebook (rename, recode, missing declaration, type, labels, roles) with a dry_run preview that shares one planner with the apply path, so a change is previewed exactly as applied. The raw input is never overwritten.
• A declarative skip_logic rule marks skipped items as not administered so the missing-items detector no longer counts a legitimately skipped item as missingness.
• dcc_config() bundles rules, actions, an id column, and items.
• dcc_validate_rules(), dcc_validate_data(), dcc_validate_config(), and dcc_doctor() return a structured dcc_validation report (code, severity, field, affected rows, and a suggested fix) and change nothing. dcc_run() validates before it detects or executes.
• dcc_capabilities() returns a versioned, deterministic document of every feature (with Stable/Experimental/Planned status and since), rule type, action type, and input format, plus the operations DCC does not support. The action-type and format lists are the same source of truth the engine uses, so the document cannot drift from the implementation.
• dcc_schema() returns published draft-07 JSON Schemas (installed under inst/schemas/) for a finding, an audit-log row, a rule file, an action map, and a manifest.
• Added public accessors so callers never read hidden attributes: dcc_unhandled() (a result’s unhandled findings), dcc_item_map() and dcc_mapping_findings() (a dcc_map_forms() result’s item map and mapping problems).
• Added AI_USAGE.md: the approved public functions and the safe capabilities -> validate -> preview -> execute -> reconcile -> export -> verify flow for AI systems.

DCC 1.0.1

Audit-correctness and format-reliability release. Every finding now has a stable identity that every audit row and reconciliation joins on exactly, and every supported input format is certified. All changes are additive to the public schemas; no valid dcc_*() call changes shape.

• Every finding carries a deterministic finding_id (run + check + record + variable + occurrence). dcc_detect() and dcc_detect_chunked() derive the run prefix from the rule and source-file hashes, so the same data and rules reproduce the same identities.
• dcc_execute() validates the whole plan before any data changes: unknown action IDs, unmapped recodes, missing or duplicated record IDs, and cell-level actions on group-level findings are now errors instead of silent degradations. Each audit row carries the exact finding_id it came from.
• Findings without an explicit action are returned as result$unhandled rather than being silently auto-flagged, so an unhandled finding can never be reported as handled. (default is retained for call compatibility but no longer auto-dispositions.)
• dcc_reconcile() joins audit rows to findings on finding_id only and assigns each finding one terminal status (changed, excluded, flagged, or unhandled); an audit row with no matching finding raises a dcc_reconcile_error. The loose record_id + check_id matching and the unreconciled_changes attribute are gone.
• dcc_detect_chunked() infers the separator from the extension (sep = NULL gives a tab for .tsv, a comma otherwise) and locks first-chunk column types via a data.table-compatible colClasses map, so chunked runs are warning-free on exact chunk multiples, later all-NA chunks, quoted delimiters, embedded newlines, and latin1 input.
• Certified the full input matrix (CSV, TSV, JSON, Excel, SPSS, Stata, SAS, Parquet, Feather) with a format-matrix test suite and Arrow multi-batch Parquet/Feather parity against in-memory findings. Added writexl to Suggests and to the CI dependency set.

DCC 1.0.0

First stable, CRAN-targeted release. The public API – the exported dcc_* functions and the dcc_findings and audit-log schemas – is now stable; breaking changes to either schema will be major releases.

• Added an end-to-end vignette, vignette("dcc-pipeline"), covering the Detect -> Execute -> Report workflow.
• Completed \examples coverage for every exported function (dcc_detect_chunked(), dcc_manifest(), dcc_rerun()).
• Rewrote the README around the package, licensed the project under GPL (>= 2) (copyright WEIAN DATA TECH (Beijing) Co., Ltd.), recorded authorship, copyright, and funding in DESCRIPTION, and documented external dependency copyright boundaries in inst/COPYRIGHTS.

DCC 0.5.0

First package release, covering the full Detect -> Execute -> Report workflow.

• dcc_read() multi-format, multi-encoding input layer; the dcc_data container with a provenance chain; dcc_l0_diagnose() structural diagnostics.
• dcc_rules() / dcc_detect() declarative YAML rule engine and the five response-quality detectors (missing items, straight-lining, response time, trap items, score anomalies), producing the dcc_findings object.
• dcc_execute() execution engine with a cell-level audit log; dcc_score() answer-key scoring; dcc_map_forms() multi-form item-bank alignment.
• dcc_report() dual-layer HTML reports, dcc_reconcile() closed-loop verification, dcc_trace() cell lineage, and dcc_manifest() / dcc_rerun() manifest-based reproduction.
• dcc_detect_chunked() larger-than-memory detection with an adaptive CSV/Arrow backend, plus CI performance benchmarks.