updated index of all metrics

docs/EconomicImpact/index.md

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+# EconomicImpact Class
+
+## Overview
+
+The `EconomicImpact` class is designed to evaluate the economic impact of a given reform on a specified country. It provides methods to calculate various economic metrics, such as inequality and income distribution, before and after the reform.
+
+## Class Initialization
+
+### `EconomicImpact(reform, country)`
+
+Initializes the `EconomicImpact` class with a specified reform and country.
+
+**Parameters:**
+- `reform` (dict): A dictionary defining the economic reform. The structure of the dictionary should match the expected format for the `Reform` class.
+- `country` (str): The country for which the reform's impact is to be evaluated. Supported values are `"uk"` and `"us"`.
+
+**Example:**
+```python
+from policyengine import EconomicImpact
+
+impact = EconomicImpact(
+    reform={"gov.hmrc.income_tax.rates.uk[0].rate": {"2024-01-01": 0.25}},
+    country="uk"
+)
+```
+
+
+## Example Usage
+
+```python
+from policyengine import EconomicImpact
+
+# Initialize the EconomicImpact class
+impact = EconomicImpact(
+    reform={"gov.hmrc.income_tax.rates.uk[0].rate": {"2024-01-01": 0.25}},
+    country="uk"
+)
+
+# Calculate the Gini coefficient
+result_gini = impact.calculate("inequality/gini")
+print(result_gini)
+# Output: {'baseline': 0.4288962129322326, 'reform': 0.42720356179075414, 'change': -0.001692651141478485}
+
+# Calculate the top 1% income share
+result_top_1 = impact.calculate("inequality/top_1_pct_share")
+print(result_top_1)
+# Output: {'baseline': 0.09121853588608866, 'reform': 0.09301056461517446, 'change': 0.0017920287290857928}
+
+# Calculate the top 10% income share
+result_top_10 = impact.calculate("inequality/top_10_pct_share")
+print(result_top_10)
+# Output: {'baseline': 0.3101681771998754, 'reform': 0.31214840219992496, 'change': 0.0019802250000495736}
+```

docs/Poverty/deep/index.md

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+# Deep poverty
+
+## Overview
+
+Deep poverty metrics focus on measuring severe economic hardship, typically defined as living below 50% of the poverty line. These metrics provide critical information about the most economically vulnerable segments of the population.
+
+## Available Metrics
+
+Our deep poverty analysis includes the following categories:
+
+1. **Deep Poverty by Age Group**
+   - Child Deep Poverty
+   - Adult Deep Poverty
+   - Senior Deep Poverty
+   - Overall Deep Poverty
+
+2. **Deep Poverty by Gender**
+   - Male Deep Poverty
+   - Female Deep Poverty
+   - Overall Deep Poverty
+
+## Significance
+
+Deep poverty metrics are crucial for:
+- Identifying the most economically vulnerable populations
+- Assessing the severity of poverty beyond standard poverty measures
+- Evaluating the impact of policies on extreme poverty
+- Guiding targeted interventions for those in severe economic distress
+
+## Usage
+
+These metrics can be accessed using the `EconomicImpact` class. For detailed information on each metric, please refer to the respective documentation pages.

docs/Poverty/index.md

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+# Poverty impact

docs/contributing/index.md

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+# How to contribute
+
+Any and all contributions are welcome to this project. You can help by:
+
+* Filing issues. Tell us about bugs you've found, or features you'd like to see.
+* Fixing issues. File a pull request to fix an issue you or someone else has filed.
+
+If you file an issue or a pull request, one of the maintainers (primarily [@nikhilwoodruff](https://github.com/nikhilwoodruff)) will respond to it within at least a week. If you don't hear back, feel free to ping us on the issue or pull request.
+
+## Changelog Entries
+
+Before you send out a pull request, make sure to add a description of your changes to [changelog_entry.yaml](../../changelog_entry.yaml).
+For example,
+```yaml
+- bump: patch
+  changes:
+    fixed:
+    - Fixed a bug causing Windows tests to fail.
+```
+You can find more examples in [changelog.yaml](../../changelog.yaml). Note that you do not need to add the date field.
+That field is automatically populated by `make changelog`. Also, note that **you should not run `make changelog` 
+yourself**, as [our GitHub workflows](../../.github/workflows) will do this for you as part of the build process.
+
+## Pull requests
+
+Each pull request should:
+* Close an issue. If there isn't an issue that the pull request completely addresses, please file one.
+* Have a description that makes sense to a layperson. If you're fixing a bug, describe what the bug is and how you fixed it. If you're adding a feature, describe what the feature is and why you added it.
+* Have tests. If you're fixing a bug, write a test that fails without your fix and passes with it. If you're adding a feature, write tests that cover the feature. Sometimes this isn't necessary (for example, documentation changes), but if in doubt, err on the side of including tests.
+* Pass all GitHub actions. If you're not sure why a GitHub action is failing, feel free to ask for help in the issue or pull request.
+

docs/index.md

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+# Introduction
+
+The PolicyEngine Python package serves as a unified framework for conducting comprehensive policy simulations and analyses across different countries. Building on the foundation of [OpenFisca-Core](https://github.com/openfisca/openfisca-core), PolicyEngine provides a consistent API that supports diverse functionalities such as microsimulations, individual household analyses, and cross-country comparisons. 
+
+PolicyEngine Core enables users to:
+
+* Simulate and analyze various tax-benefit policies with tailored models for each country.
+* Use two main simulation classes—Microsimulation for broad-scale microdata simulations and IndividualSim for detailed single household calculations.
+* Generate interactive and visually appealing charts using Plotly, facilitating budgetary breakdowns, income decile analyses, poverty breakdowns by demographic groups, and impacts on income inequality.
+* Reproduce visualizations from the PolicyEngine web interface programmatically, allowing for in-depth analysis and customization.
+
+The package aims to streamline policy evaluation by offering a user-friendly API, comprehensive documentation, and tools for generating insightful visual representations of simulation results.