What ACRO-R Supports
This page provides a comprehensive overview of ACRO’s capabilities. 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.
- 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.