update README

README.md

@@ -1,11 +1,12 @@
+# SACRO-ML
+
 [![License](https://img.shields.io/badge/license-MIT-blue.svg?style=flat)](https://opensource.org/licenses/MIT)
 [![Latest Version](https://img.shields.io/github/v/release/AI-SDC/SACRO-ML?style=flat)](https://github.com/AI-SDC/SACRO-ML/releases)
 [![DOI](https://zenodo.org/badge/518801511.svg)](https://zenodo.org/badge/latestdoi/518801511)
 [![codecov](https://codecov.io/gh/AI-SDC/SACRO-ML/branch/main/graph/badge.svg?token=AXX2XCXUNU)](https://codecov.io/gh/AI-SDC/SACRO-ML)
+[![PyPI package](https://img.shields.io/pypi/v/sacroml.svg)](https://pypi.org/project/sacroml)
 [![Python versions](https://img.shields.io/pypi/pyversions/sacroml.svg)](https://pypi.org/project/sacroml)
 
-# SACRO-ML
-
 A collection of tools and resources for managing the [statistical disclosure control](https://en.wikipedia.org/wiki/Statistical_disclosure_control) of trained [machine learning](https://en.wikipedia.org/wiki/Machine_learning) models. For a brief introduction, see [Smith et al. (2022)](https://doi.org/10.48550/arXiv.2212.01233).
 
 The `sacroml` package provides:
@@ -14,8 +15,6 @@ The `sacroml` package provides:
 
 ## Installation
 
-[![PyPI package](https://img.shields.io/pypi/v/sacroml.svg)](https://pypi.org/project/sacroml)
-
 Install `sacroml` and manually copy the [`examples`](examples/).
 
 To install only the base package, which includes the attacks used for assessing privacy:
@@ -35,10 +34,14 @@ Note: macOS users may need to install libomp due to a dependency on XGBoost:
 $ brew install libomp
 ```
 
-## Running
+## Usage
 
 See the [`examples`](examples/).
 
+## Documentation
+
+See [API documentation](https://ai-sdc.github.io/SACRO-ML/).
+
 ## 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 [DARE UK](https://dareuk.org.uk) (Data and Analytics Research Environments 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]: PICTURES.