--- title: "What ACRO-R Supports" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{What ACRO-R Supports} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- # 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