The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

ACRO: Tools for the Automatic Checking of Research Outputs

This repository maintains the ACRO R package, which is an interface to the Python ACRO package.

ACRO (Automatic Checking of Research Outputs) is an open source tool for automating the statistical disclosure control (SDC) of research outputs. ACRO assists researchers and output checkers by distinguishing between research output that is safe to publish, output that requires further analysis, and output that cannot be published because of a substantial risk of disclosing private data.

It does this by providing a light-weight ‘skin’ that sits over well-known analysis tools, in a variety of languages researchers might use. This adds functionality to:

• identify potentially disclosive outputs against a range of commonly used disclosure tests;
• suppress outputs where required;
• report reasons for suppression;
• produce simple summary documents TRE staff can use to streamline their workflow.

Installation

Install the acro package from CRAN as follows:

install.packages("acro")

Notes for Python 3.13

ACRO currently depends on numpy version 1.x.x for which no pre-compiled wheels are available within pip for Python 3.13. Therefore, in this scenario, numpy must be built from source. This requires the installation of a C++ compiler before pip installing acro.

For Windows, the Microsoft Visual Studio C++ build tools will likely need to be installed first.

If you are unable to install the C++ tools, but are able to install multiple versions of Python, you can use the following method to explicitly create the Python virtual environment with your desired python.exe.

First identify the location of the python.exe needed, which may be in C:\Program Files\ or in your local C:\Users\username\AppData\Local\Programs\Python\.

Then from R, use reticulate to create the virtual environment and install ACRO; see example below. Change the path provided as a version to your Python path (note that forward slashes are required in the path).

>>> library("reticulate")
>>> reticulate::virtualenv_create(envname = "r-acro-0.4.7", version = "C:/Users/username/AppData/Local/Programs/Python/Python310/python.exe", force = TRUE, packages = NULL)
>>> reticulate::py_install("acro", envname = "r-acro-0.4.7")

Finally, the R ACRO library can be used as normal:

>>> library("acro")
>>> acro_init(suppress = TRUE)

Usage

Before using any function from the package, an acro object should be initialised using the following R code:

>>> library("acro")
>>> acro_init()

Then the functions can be called.

Documentation

The github-pages contains pre-built documentation.

Acknowledgement

This work was funded by UK Research and Innovation under Grant Numbers MC_PC_21033 and MC_PC_23006 as part of Phase 1 of the Data and Analytics Research Environments UK (DARE UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK). The specific projects were Semi-Automatic checking of Research Outputs (SACRO; MC_PC_23006) and Guidelines and Resources for AI Model Access from Trusted Research environments (GRAIMATTER; MC_PC_21033). This project has also been supported by MRC and EPSRC [grant number MR/S010351/1].

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.