### Setup: Clone and Install

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/CONTRIBUTING.md

Clone the repository and install the package with development dependencies using pip install -e ".[dev]".

```bash
git clone https://github.com/simonplmak-cloud/intangible-valuation.git
cd intangible-valuation
pip install -e ".[dev]"
```

--------------------------------

### Install MCP server and run

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/index.md

Install the library with the MCP extra and start the MCP server. This exposes 49 valuation tools for AI agents.

```bash
pip install intangible-valuation[mcp]
intangible-valuation-mcp
```

--------------------------------

### Install and run the stdio server

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/mcp_server/README.md

Install the package with MCP extras and start the server. Requires Python and pip.

```bash
pip install -e "..[mcp]"
python server.py
```

--------------------------------

### Development setup and test commands

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/index.md

Install the package in editable mode with dev dependencies, run tests with coverage, and check code style with ruff.

```bash
pip install -e ".[dev]"
pytest --cov=src --cov-report=term-missing
ruff check .
```

--------------------------------

### Run the installed console script

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/mcp.md

Equivalent command to start the MCP server using the installed console script.

```bash
intangible-valuation-mcp
```

--------------------------------

### Install and Run MCP Server from Registry (pip and uvx)

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/README.md

Install and run the stdio MCP server from the MCP Registry or Glama. The first command uses pip, the second uses uvx without installation.

```bash
pip install "intangible-valuation[mcp]" && intangible-valuation-mcp   # pip
uvx --from intangible-valuation intangible-valuation-mcp              # uvx (no install)
```

--------------------------------

### Install intangible-valuation via pip

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/overrides/main.html

Install the intangible-valuation Python library from PyPI.

```bash
pip install intangible-valuation
```

--------------------------------

### Install the MCP package

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/mcp.md

Install the package with the MCP extra using pip.

```bash
pip install intangible-valuation[mcp]
```

--------------------------------

### Install intangible-valuation with pip extras

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/README.md

Install the library alone, with the MCP server, or with development tools (pytest, ruff, mypy). The [mcp] extra adds the MCP server; [dev] adds testing and linting dependencies.

```bash
pip install intangible-valuation          # library only
pip install intangible-valuation[mcp]     # + MCP server
pip install intangible-valuation[dev]     # + pytest, ruff, mypy
```

--------------------------------

### Example tool call for relief_from_royalty

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/mcp.md

Example JSONC payload for the tools/call endpoint, showing required method and parameters.

```jsonc
// tools/call
{
  "method": "relief_from_royalty",
  "revenue_projections": [1000000, 1100000, 1200000, 1300000, 1400000],
  "royalty_rate": 0.05,
  "discount_rate": 0.12,
  "tax_rate": 0.25,
  "useful_life": 5
}
```

--------------------------------

### Run MCP Server Locally (stdio)

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/README.md

Installs the MCP extra and runs the stdio server locally. This is the local entrypoint for AI agents.

```bash
pip install "intangible-valuation[mcp]"
python mcp_server/server.py
```

--------------------------------

### Example result for relief_from_royalty

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/mcp.md

Example JSON response showing the structured result with value, method, formula_reference, steps, and assumptions.

```json
{
  "value": 194163.77,
  "method": "Relief from Royalty",
  "formula_reference": "...",
  "steps": ["..."],
  "assumptions": ["..."]
}
```

--------------------------------

### Use present_value and relief_from_royalty for valuation

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/overrides/main.html

Demonstrates the core present_value and relief_from_royalty functions. The present_value example computes the present value of $500,000 at a 10% discount rate over 8 periods, printing $233,253.69. The relief_from_royalty example values a patent using revenue projections, a 5% royalty rate, 12% discount rate, 25% tax rate, and a 5-year useful life.

```python
from intangible_valuation.core.time_value import present_value
from intangible_valuation.income_methods.relief_from_royalty import relief_from_royalty

# Present Value
result = present_value(future_value=500_000, discount_rate=0.10, periods=8)
print(f"PV: ${result.value:,.2f}")  # $233,253.69

# Relief from Royalty — Patent Valuation
value = relief_from_royalty(
    revenue_projections=[1_000_000, 1_100_000, 1_200_000, 1_300_000, 1_400_000],
    royalty_rate=0.05, discount_rate=0.12, tax_rate=0.25, useful_life=5,
)
print(f"Patent value: ${value.value:,.2f}")
```

--------------------------------

### Software Valuation with software_valuation

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Technology-Valuation

Uses a cost and income hybrid for software assets. The example includes development and maintenance costs, user base, revenue model, useful life, and discount rate.

```python
from intangible_valuation.asset_types.technology_valuation import software_valuation
result = software_valuation(
    development_cost=1_500_000, maintenance_cost=200_000,
    user_base=50000, revenue_model={"type": "subscription", "revenue_per_user": 20},
    useful_life=5, discount_rate=0.15,
)
```

--------------------------------

### Python Library Quick Start: present_value, build_up_discount_rate, relief_from_royalty

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/README.md

Shows the core Python API for present value, build-up discount rate, and relief-from-royalty valuation. The printed outputs are included as comments. Use this to quickly test the library's main functions.

```python
from intangible_valuation.core.time_value import present_value
from intangible_valuation.core.discount_rates import build_up_discount_rate
from intangible_valuation.income_methods.relief_from_royalty import relief_from_royalty

# Present Value
result = present_value(future_value=500_000, discount_rate=0.10, periods=8)
print(f"PV: ${result.value:,.2f}")  # $233,253.69

# Build-Up Discount Rate
rate = build_up_discount_rate(
    risk_free_rate=0.04, equity_risk_premium=0.06,
    size_premium=0.02, industry_risk_premium=0.01, specific_risk_premium=0.03,
)
print(f"Discount rate: {rate.value:.2%}")  # 16.00%

# Relief from Royalty — Patent Valuation
value = relief_from_royalty(
    revenue_projections=[1_000_000, 1_100_000, 1_200_000, 1_300_000, 1_400_000],
    royalty_rate=0.05, discount_rate=0.12, tax_rate=0.25, useful_life=5,
)
print(f"Patent value: ${value.value:,.2f}")
```

--------------------------------

### Python Implementation of PPA and Goodwill Calculation

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/ASC-805-IFRS-3

Python code demonstrating the full PPA waterfall using purchase_price_allocation and goodwill calculation from the intangible_valuation package. The goodwill example shows a result of $2,000,000.

```python
from intangible_valuation.advanced.purchase_price_alloc import purchase_price_allocation
from intangible_valuation.advanced.goodwill import goodwill

# Full PPA waterfall
result = purchase_price_allocation(
    purchase_price=10_000_000,
    tangible_assets_fv=3_000_000,
    identified_intangibles=[
        {"name": "Patent Portfolio", "value": 2_500_000, "method": "RFR"},
        {"name": "Customer Relationships", "value": 1_800_000, "method": "MPEEM"},
        {"name": "Brand", "value": 1_200_000, "method": "RFR"},
    ],
    liabilities_fv=500_000,
)

# Goodwill calculation
g = goodwill(purchase_price=10_000_000, fair_value_net_identifiable_assets=8_000_000)
# Goodwill: $2,000,000
```

--------------------------------

### Data Asset Valuation with data_asset_valuation

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Technology-Valuation

Uses quality-adjusted revenue contribution for data assets. The example includes acquisition cost, quality score, revenue contribution, useful life, and discount rate.

```python
from intangible_valuation.asset_types.technology_valuation import data_asset_valuation
result = data_asset_valuation(
    acquisition_cost=500_000, quality_score=0.85,
    revenue_contribution=2_000_000, useful_life=7, discount_rate=0.15,
)
```

--------------------------------

### Contributory Asset Charges (CACs) calculation example

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Income-Methods

Calculates required returns on supporting assets. Uses the contributory_asset_charges function with a list of asset dictionaries containing type, value, and return rate. The example output shows a total CAC of $140,000.00.

```python
from intangible_valuation.income_methods.excess_earnings import contributory_asset_charges
assets = [
    {"type": "working_capital", "value": 500000, "return_rate": 0.08},
    {"type": "fixed_assets", "value": 1000000, "return_rate": 0.10},
]
result = contributory_asset_charges(assets)
# Total CAC: $140,000.00
```

--------------------------------

### Developed Technology Valuation with developed_technology_valuation

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Technology-Valuation

Uses the cost-income approach with life-cycle risk adjustment. The example passes R&D costs, life cycle stage, competitive advantage, discount rate, and cash flow projections.

```python
from intangible_valuation.asset_types.technology_valuation import developed_technology_valuation
result = developed_technology_valuation(
    rd_costs=2_000_000, life_cycle_stage="growth",
    competitive_advantage=5, discount_rate=0.18,
    cash_flow_projections=[300000, 500000, 800000, 1_000_000, 1_200_000],
)
```

--------------------------------

### Incremental Cash Flow valuation example

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Income-Methods

Values an asset as the present value of additional cash flows with versus without the asset. Uses the incremental_cashflow function with cash flow projections and a discount rate.

```python
from intangible_valuation.income_methods.incremental_cashflow import incremental_cashflow
result = incremental_cashflow(
    cash_flows_with=[500000, 550000, 600000, 650000, 700000],
    cash_flows_without=[400000, 420000, 440000, 460000, 480000],
    discount_rate=0.10,
)
```

--------------------------------

### Key Person Value with key_person_value

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Human-Capital

Calculates the value of a key person using the income approach. Requires the `intangible_valuation` package and the `key_person_value` function. The example uses a $2.5M revenue contribution, $500,000 replacement cost, 0.15 departure probability, and 0.18 discount rate.

```python
from intangible_valuation.asset_types.human_capital import key_person_value
result = key_person_value(
    revenue_contribution=2_500_000, replacement_cost=500_000,
    departure_probability=0.15, discount_rate=0.18,
)
```

--------------------------------

### Method Selection Decision Tree

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/skills/discount-rate-construction/SKILL.md

Decision tree for selecting the discount rate method based on public/private status and debt financing. Use this to guide the method selection step.

```text
Is the entity publicly traded with available beta?
├── YES → Use CAPM or WACC
│   └── Does the entity use debt financing?
│       ├── YES → Use WACC
│       └── NO  → Use CAPM
└── NO  → Use Build-Up Method
```

--------------------------------

### Platform Valuation with Network Effects in Python

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Case-Study-Fintech

Use this Python snippet to value a fintech platform with network effects. It calls the platform_valuation function with parameters for network size, network effects coefficient, revenue per user, growth rate, and discount rate. The example uses PayFlow's metrics: 2M active users, $15 ARPU, 40% YoY growth, and a 20% discount rate.

```python
from intangible_valuation.asset_types.technology_valuation import platform_valuation
value = platform_valuation(
    network_size=2_000_000,  # Active users
    network_effects_coefficient=0.3,  # Each new user adds 30% more value
    revenue_per_user=15.0,  # ARPU
    growth_rate=0.40,  # 40% YoY user growth
    discount_rate=0.20,  # VC-level risk for growth fintech
)
```

--------------------------------

### Assembled Workforce Valuation with assembled_workforce_valuation

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Human-Capital

Calculates the value of an assembled workforce using the replacement cost approach. Requires the `intangible_valuation` package and the `assembled_workforce_valuation` function. The example uses 500 employees, a $75,000 average replacement cost, $15,000 training cost, a 0.70 productivity factor, and a 0.12 attrition rate.

```python
from intangible_valuation.asset_types.human_capital import assembled_workforce_valuation
result = assembled_workforce_valuation(
    employee_count=500, avg_replacement_cost=75000,
    training_cost=15000, productivity_factor=0.70,
    attrition_rate=0.12,
)
```

--------------------------------

### Goodwill Calculation from Acquisition

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/examples/advanced-methods.md

Use to calculate goodwill from a business acquisition. Provide the purchase price and the fair value of net identifiable assets. The example shows a goodwill of $3,000,000.00 for a $10,000,000 purchase price and $7,000,000 net assets.

```python
from intangible_valuation.advanced.goodwill import goodwill
result = goodwill(
    purchase_price=10_000_000,
    fair_value_net_identifiable_assets=7_000_000,
)
print(f"Goodwill: ${result.value:,.2f}")
# Goodwill: $3,000,000.00
```

--------------------------------

### Calculate Future Value (FV) with future_value

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Time-Value-of-Money

Compounds a present amount to its future value using the formula FV = PV × (1 + r)^n. The example uses a present value of 233,253.69, a discount rate of 10%, and 8 periods, resulting in a future value of $500,000.00.

```python
from intangible_valuation.core.time_value import future_value
result = future_value(present_value=233253.69, discount_rate=0.10, periods=8)
# FV: $500,000.00
```

--------------------------------

### Calculate Present Value (PV) with present_value

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Time-Value-of-Money

Discounts a single future cash flow to its present value using the formula PV = FV / (1 + r)^n. The example uses a future value of 500,000, a discount rate of 10%, and 8 periods, resulting in a present value of $233,253.69.

```python
from intangible_valuation.core.time_value import present_value
result = present_value(future_value=500_000, discount_rate=0.10, periods=8)
# PV: $233,253.69
```

--------------------------------

### Relief from Royalty Patent Valuation with TAB

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/examples/advanced-methods.md

Use to value a patent portfolio with the Relief from Royalty method, including the Tax Amortization Benefit (TAB). The example uses a 5% royalty rate, 12% discount rate, 25% tax rate, and 5-year useful life. The output shows a patent value of $194,163.77 with TAB; step-by-step proof is available in value.steps.

```python
from intangible_valuation.income_methods.relief_from_royalty import relief_from_royalty
value = relief_from_royalty(
    revenue_projections=[1_000_000, 1_100_000, 1_200_000, 1_300_000, 1_400_000],
    royalty_rate=0.05,      # 5% arm's length royalty rate
    discount_rate=0.12,     # 12% discount rate
    tax_rate=0.25,          # 25% corporate tax rate
    useful_life=5,          # 5-year useful life
    tab_enabled=True,       # Include Tax Amortization Benefit
)
print(f"Patent Value: ${value.value:,.2f}")
# Patent Value: $194,163.77 (with TAB)
```

--------------------------------

### Development workflow commands

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/README.md

Commands for setting up the development environment, running tests with coverage, linting, type checking, and regenerating the MCP stdio server. Use after cloning the repository to verify the project and prepare for contributions.

```bash
# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=src --cov-report=term-missing

# Lint
ruff check .

# Type check
mypy src/

# Regenerate the MCP stdio server from the canonical surface
python scripts/generate_mcp.py
```

--------------------------------

### Relief from Royalty (RFR) valuation example

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Income-Methods

Calculates the present value of after-tax royalty payments avoided by ownership. Uses the relief_from_royalty function with revenue projections, royalty rate, discount rate, tax rate, and useful life. The example output shows a patent value of $194,163.77 with TAB.

```python
from intangible_valuation.income_methods.relief_from_royalty import relief_from_royalty
value = relief_from_royalty(
    revenue_projections=[1_000_000, 1_100_000, 1_200_000, 1_300_000, 1_400_000],
    royalty_rate=0.05, discount_rate=0.12, tax_rate=0.25, useful_life=5,
)
# Patent value: $194,163.77 (with TAB)
```

--------------------------------

### Hosted endpoint URL

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/mcp.md

URL for the hosted Streamable HTTP endpoint, requiring no install or API key.

```text
https://intangible-valuation.simonmak.com/api/mcp
```

--------------------------------

### Build-Up Discount Rate Calculation

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Discount-Rate-Construction

Calculates a discount rate using the build-up method: r = Rf + ERP + Size Premium + Industry RP + Specific RP. Appropriate for private companies where beta is unavailable. Returns 16.00% for the given inputs.

```python
from intangible_valuation.core.discount_rates import build_up_discount_rate
rate = build_up_discount_rate(
    risk_free_rate=0.04, equity_risk_premium=0.06,
    size_premium=0.02, industry_risk_premium=0.01, specific_risk_premium=0.03,
)
# 16.00%
```

--------------------------------

### Currency and Country Risk Adjustment

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/skills/discount-rate-construction/SKILL.md

Adjusts the discount rate for currency and country risk in cross-border valuations. The example returns 17.00%.

```python
currency_adjusted_discount_rate(base_rate=0.12, currency_risk_premium=0.02, country_risk_premium=0.03)
# Result: 17.00%
```

--------------------------------

### WACC Calculation

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/skills/discount-rate-construction/SKILL.md

Calculates the weighted average cost of capital (WACC) for enterprise-level valuation with debt financing. The example returns 9.75%.

```python
wacc(equity_value=700, debt_value=300, cost_of_equity=0.12, cost_of_debt=0.06, tax_rate=0.25)
# Result: 9.75%
```

--------------------------------

### Development commands for MCP server

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/mcp.md

Commands to run the server in development mode, regenerate the stdio server, and lint the tool surface.

```bash
# Run the server in development mode
python -m mcp_server.server
```

```bash
# Regenerate the stdio server from the canonical surface
python scripts/generate_mcp.py
```

```bash
# Lint the tool surface (Glama TDQS)
npx --yes mcp-tdqs@0.2.0 lint --command "python mcp_server/server.py" --fail-on error
```

--------------------------------

### Configure MCP server in AI agent (stdio)

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/mcp.md

JSON configuration for adding the MCP server to an AI agent, specifying the command and arguments.

```json
{
  "mcpServers": {
    "intangible-valuation": {
      "command": "python",
      "args": ["mcp_server/server.py"]
    }
  }
}
```

--------------------------------

### Incremental Cash Flow Technology Valuation

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/examples/advanced-methods.md

Use to value proprietary technology by comparing cash flows with and without the technology. Provide the cash flow projections for both scenarios and a discount rate (10% in this example).

```python
from intangible_valuation.income_methods.incremental_cashflow import incremental_cashflow
result = incremental_cashflow(
    cash_flows_with=[500000, 550000, 600000, 650000, 700000],
    cash_flows_without=[400000, 420000, 440000, 460000, 480000],
    discount_rate=0.10,
)
print(f"Technology Value: ${result.value:,.2f}")
```

--------------------------------

### Build-Up Discount Rate Tool Call

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/skills/asset-valuation/SKILL.md

Calls the build_up_discount_rate tool with risk-free rate, equity risk premium, size premium, industry risk premium, and specific risk premium. The result is a 19% discount rate.

```python
build_up_discount_rate(
    risk_free_rate=0.04,
    equity_risk_premium=0.06,
    size_premium=0.04,
    industry_risk_premium=0.02,
    specific_risk_premium=0.03
)
```

--------------------------------

### Run intangible asset impairment test (ASC 350)

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/skills/impairment-testing/SKILL.md

Tests an intangible asset for impairment under ASC 350 using fair value. The example shows an impairment of $800,000 when fair value is below carrying value.

```python
intangible_impairment_test(
    carrying_value=5_000_000,
    fair_value=4_200_000,
    standard="ASC350",
)
# Impairment: $800,000
```

--------------------------------

### Monte Carlo Valuation with monte_carlo_valuation

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Monte-Carlo

Calls the monte_carlo_valuation tool with input distributions for revenue (normal) and cost (uniform), 10000 iterations, and a fixed seed for reproducibility.

```python
from mcp_server.tools import monte_carlo_valuation
result = monte_carlo_valuation(
    input_distributions=[
        {"name": "revenue", "distribution": "normal", "params": {"mean": 1000000, "std": 100000}},
        {"name": "cost", "distribution": "uniform", "params": {"low": 200000, "high": 400000}},
    ],
    iterations=10000, seed=42,
)
```

--------------------------------

### OpenCode MCP Configuration (opencode.json)

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/README.md

Configuration snippet for OpenCode to add the remote MCP server. Add this to your opencode.json file.

```json
"intangible-valuation": {
  "type": "remote",
  "url": "https://intangible-valuation.simonmak.com/api/mcp",
  "timeout": 60000
}
```

--------------------------------

### Calculate Perpetuity Present Value with perpetuity_pv

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Time-Value-of-Money

Computes the present value of infinite periodic payments using the formula PV = PMT / r. The example uses a payment of 10,000 and a discount rate of 8%, resulting in a present value of $125,000.00.

```python
from intangible_valuation.core.time_value import perpetuity_pv
result = perpetuity_pv(payment=10_000, discount_rate=0.08)
# PV: $125,000.00
```

--------------------------------

### BibTeX citation for the project

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/README.md

BibTeX entry for citing the Intangible Asset Valuation Engine software in academic work. Includes authors, title, year, URL, and MIT license.

```bibtex
@software{intangible_valuation_engine,
  author = {Mak, Simon and Yuen, William and Wu, Paul and Hu, Wayne},
  title = {Intangible Asset Valuation Engine},
  year = {2026},
  url = {https://github.com/simonplmak-cloud/intangible-valuation},
  license = {MIT},
}
```

--------------------------------

### MPEEM valuation example

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Income-Methods

Values an asset as the present value of cash flows after deducting contributory asset charges (CACs). Uses the mpeem function with cash flows and a list of CAC dictionaries, along with discount rate and tax rate.

```python
from intangible_valuation.income_methods.excess_earnings import mpeem
cfs = [200000, 220000, 240000, 260000, 280000]
cacs = [{"total_cac": 50000}, {"total_cac": 52000}, {"total_cac": 54000}, {"total_cac": 56000}, {"total_cac": 58000}]
result = mpeem(cfs, cacs, discount_rate=0.12, tax_rate=0.25)
```

--------------------------------

### Estimate Useful Life for Patent

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Case-Study-Pharma

Estimates the useful life of a patent asset using the estimate_useful_life function. The example sets legal_life to 12 and obsolescence_rate to 0.06, resulting in an economic life of about 15 years, but limited by the legal life to 12 years.

```python
from intangible_valuation.utils.formulas import estimate_useful_life
life = estimate_useful_life(asset_type="patent", legal_life=12, obsolescence_rate=0.06)
# Economic life: ~15 years, limited by legal life → 12 years
```

--------------------------------

### Run a PPA waterfall allocation

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Goodwill-and-PPA

Performs a full purchase price allocation (PPA) waterfall, distributing the purchase price to tangible assets, identified intangibles, and liabilities, with the residual allocated to goodwill.

```python
from intangible_valuation.advanced.purchase_price_alloc import purchase_price_allocation
result = purchase_price_allocation(
    purchase_price=10_000_000,
    tangible_assets_fv=3_000_000,
    identified_intangibles=[
        {"name": "Patents", "value": 2_500_000, "method": "RFR"},
        {"name": "Customer List", "value": 1_800_000, "method": "MPEEM"},
    ],
    liabilities_fv=500_000,
)
```

--------------------------------

### Benchmark Royalty Rate for Pharmaceutical Patent

Source: https://github.com/simonplmak-cloud/intangible-valuation/wiki/Case-Study-Pharma

Benchmarks the royalty rate for a pharmaceutical patent using the royalty_rate_benchmark function. The example specifies ip_type as "patent" and industry as "pharmaceutical", returning a benchmark range of 3-15% with a median around 8%.

```python
from intangible_valuation.advanced.royalty_benchmark import royalty_rate_benchmark
benchmark = royalty_rate_benchmark(ip_type="patent", industry="pharmaceutical")
# Benchmark range: 3-15% (median ~8%)
```

--------------------------------

### DLOM (Finnerty Model)

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/docs/examples/core-methods.md

Calculates the discount for lack of marketability using the Finnerty model with a 1-year restriction period and 35% volatility.

```python
from intangible_valuation.core.discount_rates import dlom_finnerty
result = dlom_finnerty(restricted_period=1.0, volatility=0.35, risk_free_rate=0.04)
print(f"DLOM: {result.value:.2%}")
```

--------------------------------

### Verify with pytest

Source: https://github.com/simonplmak-cloud/intangible-valuation/blob/main/CONTRIBUTING.md

Run the test suite with pytest to verify changes before opening a pull request.

```bash
pytest
```