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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.