dcc_report_machine

Writes deterministic JSON and JSONL artifacts, an SHA-256 manifest, and the
versioned schemas an AI agent or external system needs to validate them.

Rule-driven, auditable cleaning of survey and assessment
response data, implementing the WeianData Detect-Execute-Report
workflow. Provides a multi-format, multi-encoding input layer
(CSV, 'Excel', 'SPSS', 'Stata', 'SAS', Parquet, JSON), the
dcc_data container with a provenance chain, level-0 structural
diagnostics, five built-in response-quality detectors (missing
items, straight-lining, response time, trap items, score
anomalies), a declarative YAML rule engine, an execution engine
with a cell-level audit log, answer-key scoring, multi-form to
master item bank mapping, a normalized report model rendered as bilingual
staff workbooks and HTML, complete statistical bundles, and versioned
machine JSON/JSONL with findings-to-changes reconciliation,
cell-level lineage tracing, and
manifest-based one-command reproduction. Includes a protected bilingual
strict project workbook and matching JSON contract with cell-addressed
validation, non-mutating preflight, preview-first execution, and localized
staff guidance. All formally supported input backends install with the
package; PDF is optional rather than a fixed report output.

Kunxiang Ma

Data Cleaning Center for Survey and Assessment Data

WEIAN DATA TECH (Beijing) Co., Ltd. 

dcc_report_machine function

<dl><dt>model</dt>
<dd>A validated <code>dcc_report_model</code>.</dd><dt>output_dir</dt>
<dd>Existing or new directory for machine artifacts.</dd></dl>

Arguments

Render the machine report bundle — dcc_report_machine

<dl>

<dt>model</dt>
<dd>A validated <code>dcc_report_model</code>.</dd>

<dt>output_dir</dt>
<dd>Existing or new directory for machine artifacts.</dd>

</dl>

dcc_report_machine: Render the machine report bundle

Description

Usage

Value

Arguments