lir 1.3.1

Package for optimising and evaluating Likelihood Ratio (LR) systems.

LIR Python Likelihood Ratio Toolkit

Toolkit for developing, optimising and evaluating Likelihood Ratio (LR) systems. This allows benchmarking of LR systems on different datasets, investigating impact of different sampling schemes or techniques, and doing case-based validation and computation of case LRs.

LIR was first released in 2020 and redesigned from scratch in 2025, replacing the previous repository.

Documentation

Please consult the dedicated documentation for a comprehensive overview of LiR, terminology and more on developing LR systems.

Practitioner's Guide notebook

• There is a dedicated Jupyter Notebook which uses LiR to develop LR systems for the comparison of glass measurements, accompanying the paper "From data to a validated score-based LR system: A practitioner’s guide" - Leegwater et al.
• The notebook resides on the practitioner_guide branch.
• The notebook is also available on Google Colab

Installation

LIR is compatible with Python 3.11 and later. The easiest way to install LIR is to use pip:

pip install lir

Usage

This repository offers both a Python API and a command-line interface.

Command-line interface

Evaluate an LR system using the command-line interface as follows:

1. define your data, LR system and experiments in a YAML file;
2. run lir <yaml file>.

The examples folder may be a good starting point for setting up an experiment.

The elements of the experiment configuration YAML are looked up in the registry. The following lists all available elements in the registry.

lir --list-registry

Datasets

There are currently a number of datasets implemented for this project:

• glass: LA-ICP-MS measurements of elemental concentration from floatglass. The data will be downloaded automatically from https://github.com/NetherlandsForensicInstitute/elemental_composition_glass when used in the pipeline for the first time.

Simulations

It is straightforward to simulate data for experimentation. Currently two very simple simulations synthesized_normal_binary and synthesized_normal_multiclass are available, with sources and measurements drawn from normal distributions.

Contributing / Development

Contributions are highly welcomed. If you'd like to contribute to the LiR package, please follow the steps as described in the CONTRIBUTING.md file.