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title: "What ACRO-R Supports"
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# What ACRO-R Supports

This page provides a comprehensive overview of  the capabilities ACRO supports. ACRO supports a wide range of statistical analysis functions with automated disclosure control.

## Supported Data Analysis Functions

### Table Creation & Cross-tabulation

**For Researchers:** Create frequency tables and cross-tabulations with automatic cell suppression for small counts.

**What ACRO Supports:**

* **`crosstab()`** - Cross-tabulation of two or more variables with frequency counting
* **`pivot_table()`** - Spreadsheet-style pivot tables with aggregation functions
* **`table()`** - Simple frequency tables for categorical data (R interface only)

**Technical Details:**

- ACRO suppresses, and reports the reason why, the value of an aggregation statistic (mean, median, variance, etc.) for any cell is deemed to be sensitive.
- The current version of ACRO supports the three most common tests for sensitivity: ensuring the number of contributors is above a frequency threshold, and testing for dominance via N-K rules.
    - **N-K Rule**: A dominance test where if the top N contributors account for more than K% of the total, the cell is considered disclosive.
    - **Frequency Threshold**: Cells with fewer than a specified number of contributors are suppressed.
- All thresholds are configurable via YAML configuration files.
- For detailed methodology, see our [research paper](https://doi.org/10.1109/TP.2025.3566052).
- Automatic flagging of negative or missing values for human review.

**Example Use Cases:**
- Survey response analysis by demographics
- Clinical trial outcome tables
- Market research cross-tabulations
- Educational assessment reporting

### Statistical Modeling

**For Researchers:** Run regression analyses with automated checks on model outputs and residual degrees of freedom.

**What ACRO Supports:**

* **`ols()`** - Ordinary Least Squares linear regression
* **`logit()`** - Logistic regression for binary outcomes
* **`probit()`** - Probit regression for binary outcomes

**Technical Details:**
- For regressions such as linear, probit, and logit, the tests verify that the number of residual degrees of freedom exceeds a threshold.
- The functionality acts as a wrapper around standard statistical packages.

**Example Use Cases:**
- Economic modeling and policy analysis
- Medical research and clinical studies
- Social science research
- Business analytics and forecasting

## Disclosure Control Features

### Automated Sensitivity Testing

**What ACRO Checks:**

**For Tables:**
- Minimum cell counts (frequency thresholds)
- Dominance rules (N-K rules for concentration)
- Presence of negative or missing values

**For Statistical Models:**
- Residual degrees of freedom thresholds
- Model fit diagnostics
- Parameter significance testing

**For Non-Technical Users:**
ACRO automatically identifies when research outputs might reveal sensitive information about individuals or organizations, applying industry-standard privacy protection rules without requiring manual review of every result.

### Output Management

**What ACRO Provides:**

* **Suppression Masks** - Clear indication of which results are hidden and why
* **Summary Reports** - Detailed explanation of all disclosure checks performed
* **Audit Trails** - Complete record of all analysis steps and decisions
* **Exception Handling** - Process for requesting release of flagged outputs

**Workflow Integration:**
The `finalise()` function will:
1. Check that each output with “fail” or “review” status has an exception (if not you will be asked to enter one).
2. Write the outputs to a directory. This directory contains everything that the output checkers need to make a decision.

## Supported Environments

### Research Environments

**Where ACRO Works:**
- Trusted Research Environments (TREs)
- Data safe havens
- Secure data centers
- Academic research computing facilities
- Government statistical offices
- Healthcare research environments
