cpm-engine

The forensically-defensible CPM engine. AACE-canonical. Daubert-disclosed. Bit-identical between JavaScript and Python.

Maintained by Critical Path Partners — a forensic-scheduling consultancy.

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Quick start

npm install @critical-path-partners/cpm-engine

const E = require('@critical-path-partners/cpm-engine');

const result = E.computeCPM(
    [
        { code: 'A', duration_days: 5, early_start: '2026-01-05', clndr_id: 'MF' },
        { code: 'B', duration_days: 3, clndr_id: 'MF' },
        { code: 'C', duration_days: 4, clndr_id: 'MF' },
    ],
    [
        { from_code: 'A', to_code: 'B', type: 'FS', lag_days: 0 },
        { from_code: 'B', to_code: 'C', type: 'FS', lag_days: 0 },
    ],
    {
        dataDate: '2026-01-05',
        calMap: { MF: { work_days: [1, 2, 3, 4, 5], holidays: [] } },
    }
);

console.log('Project finish:', result.projectFinish);     // 2026-01-21
console.log('Critical path:', result.criticalCodesArray); // ['A', 'B', 'C']
console.log('Engine version:', result.manifest.engine_version); // 2.9.8

That's it. Forward pass, backward pass, total float, free float, calendar arithmetic, P6-conventional date math, multi-jurisdiction holidays — all done.

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Why this engine?

Capability	cpm-engine	SmartPM	Acumen Fuse	Phoenix
Open source	yes	no	no	no
AACE-canonical method labels (29R-03 / 49R-06 / 52R-06)	yes	partial	partial	partial
FRE 707 / Daubert disclosure (built-in)	yes	no	no	no
JS-Python bit-identical parity	yes	n/a	n/a	n/a
Topology fingerprint hash (SHA-256, copy-detection)	yes	no	no	no
Kinematic delay dynamics (velocity / accel / jerk)	yes	no	no	no
Bayesian update with hierarchical pooling	yes	no	no	no
Multi-jurisdiction holiday calendars (66 jurisdictions)	yes	partial	partial	partial
MIT licensed	yes	no	no	no

The engine math is a commodity. The competitive moat in forensic scheduling is the workflow, the discipline, and the Daubert posture — not the forward pass. Critical Path Partners open-sources the engine so any academic, any solo forensic, any contractor's internal scheduler can build on a defensible foundation.

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What you can build

• Forensic delay analysis — windows analysis (AACE MIP 3.3), collapsed as-built (MIP 3.8), prospective TIA (MIP 3.6 Single Base or MIP 3.7 Multiple Base, depending on mode)
• Claim packages — owner-submission EOT bundles with cover letter, exhibits, mitigation logs
• Daubert disclosures — FRCP 26(a)(2)(B) reports, FRE 702/707 four-prong methodology statements
• Schedule risk analysis — Monte Carlo P10/P50/P80/P90, sensitivity tornadoes, Bayesian updates
• Schedule health — DCMA-14 assessment, A-F auto-grade, baseline-vs-current diff
• Multi-jurisdiction calendars — 66 jurisdictions (CA-FED + 13 provinces, US-FED + 50 states + DC)

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AACE alignment

The engine implements the math behind these AACE Recommended Practices:

RP	Title	Method labels emitted
29R-03	Forensic Schedule Analysis	MIP 3.3 / 3.6 / 3.7 / 3.8
49R-06	Identifying the Critical Path	LPM, TFM, MFP
52R-06	Prospective Time Impact Analysis	MIP 3.6 (Single Base) / MIP 3.7 (Multiple Base)
122R-22	Quantitative Risk Analysis Maturity Model (QRAMM)	(badge surface)
PPG #20 (2nd Ed 2024)	Forensic Schedule Analysis Practice Guide	(general acceptance)

Method labels are emitted in result.manifest.methodology — exactly the strings AACE peer-reviewers and opposing experts expect.

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Verifiable provenance

Every computation emits a manifest:

result.manifest = {
    engine_version: '2.9.8',
    method_id: 'computeCPM',
    activity_count: 3,
    relationship_count: 2,
    data_date: '2026-01-05',
    calendar_count: 1,
    computed_at: '2026-05-10T14:32:01.847Z',
}

Plus, for forensic provenance, every input carries a SHA-256 topology hash:

const hash = E.computeTopologyHash(activities, relationships);
console.log(hash.topology_hash);  // 64-char hex over canonical (code, duration, sorted preds)
// Two XERs with identical hashes ARE the same schedule, regardless of UID rotation.

This is the single most important forensic feature in the engine. Bid-collusion detection, retroactive-manipulation detection, and copy-detection across XERs all rely on it. It is also the foundation that lets opposing counsel verify a CPP analysis post-hoc.

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JavaScript - Python parity

The engine has a Python sibling (_cpp_common/scripts/cpm.py) used by every CPP forensic skill. The two implementations are kept bit-identical via cross-validation:

npm run crossval
# 25 fixtures × 281 checks. 0 deviations as of v2.9.8.

Plus a 282-activity real-XER stress test reports 0 mismatches.

This means a forensic analysis run in JavaScript (browser, Node) produces the same numbers as one run in Python (claims-preparation skill, MCP server, batch pipeline). Every CPP deliverable carries the same manifest regardless of which surface produced it.

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Production use

The engine runs live at mcp.criticalpathpartners.ca — try it in your browser. The same cpm-engine.js file is served over the wire and embedded inline in every report CPP produces.

The CPP forensic suite (forensic-delay-analysis, claims-preparation, claim-workbench, time-impact-analysis, schedule-risk-analysis, collapsed-as-built, counter-claim-analysis) all consume this engine — the JS port for browser/MCP, the Python sibling for batch pipelines.

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Citation

If you use this engine in academic work or expert-witness reports, please cite:

Fitkowski, D. (2026). cpm-engine: A forensically-defensible critical-path-method engine with AACE-canonical method labels and Daubert disclosure. Critical Path Partners. Version 2.9.8. https://github.com/danafitkowski/cpp-cpm-engine

Algorithm citations are in docs/citations.md. All citations have been verified against primary sources.

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License

MIT — see LICENSE.

You can use this engine in commercial forensic consulting, in academic research, in your own scheduling product, in court-filed expert reports. Just keep the copyright notice. No support is implied; no warranty is provided. You are responsible for the conclusions you draw with the engine. A Daubert disclosure is built in (DAUBERT.md) — you may use it as a starting point for your own FRCP 26(a)(2)(B) report.

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Release notes

See CHANGELOG.md for the full release history through v2.9.8.

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Contributing

See CONTRIBUTING.md. Forensic correctness is enforced — every commit must pass 685 unit tests and 281 cross-validation checks. New citations require WebSearch-verified URLs. No fabricated case names. No LLM-generated narratives in core engine paths.

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Companion repositories

Two companion repositories are public and consume this engine:

• cpp-xer-parser — Canonical Primavera P6 XER parser and generator
• cpp-critical-path-validator — Critical path validation and logic health assessment

Additional CPP skills (forensic-delay-analysis, claims-preparation, claim-workbench, time-impact-analysis, collapsed-as-built, counter-claim-analysis, schedule-risk-analysis) are private; contact Critical Path Partners for access.

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Strategic note

CPP is a forensic-scheduling consultancy. The engine is open-source not as a loss-leader but as a deliberate inversion of the competitive landscape: the math is a commodity, the workflow and discipline are not. Every academic, every solo forensic, every contractor's internal scheduler now has a reason to install CPP and a citation pathway. The closed-engine competitors — SmartPM, ALICE, Nodes&Links, Acumen — cannot match this move because their valuations require the engine stay proprietary.

If you ship something built on this engine, we'd love to hear about it: [email protected].