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.

References

LiR documentation: comprehensive overview, terminology and more on developing LR systems
Practitioner Guide (branch | paper | notebook): case study using LiR to develop an LR system using LiR
Quick Start: selecting / designing the proper LR system based on your data

Installation

LIR is compatible with Python 3.12 / 3.13/ 3.14. 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

LiR can be launched from the command line as follows:

lir --help

Or, alternatively:

python -m lir

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

define your data, LR system and experiments in a YAML file;
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

Setting up the validation schema

A schema that validates lir configuration yaml files is available, namely lir.schema.json. This schema can be used to validate a specific yaml file by using

lir --validate your_experiment.yaml

Next to this, most modern IDEs support JSON/YAML schema validation.

PyCharm

To specify the schema in PyCharm, see this official documentation entry.

Visual Studio Code

Install RedHat's YAML extension.
Point the extension to the validation schema. This can be done in two ways:
1. Add
```
  "yaml.schemas": {
      "lir.schema.json": "*.yaml"
  }
```
  to settings.json.
2. Open settings (ctrl + ,), find the Yaml: Schemas settings and create an entry with lir.schema.json as item and *.yaml as value.

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.

Release files for lir 1.8.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for lir 1.8.0
File	Interpreter	ABI	Platform	Reset
lir-1.8.0-py3-none-any.whl 190.5 kB Sep 17, 2026	Python 3	none	any	Details

Release files / lir-1.8.0-py3-none-any.whl

Download URL	lir-1.8.0-py3-none-any.whl
Size	190.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`b4ec012d0f0bd18548daed138f00ac4dc4a109a247d18f7327c97b52e1fb730d`
BLAKE2b-256 checksum How to use checksums	`8e938848fc6e818594eac525426def1121d09759de2545fa8b9f2d96aa7fe22f`
Upload date	Sep 17, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/NetherlandsForensicInstitute/lir
Publishing commit: github.com/NetherlandsForensicInstitute/lir/tree/34412cbb39e6e7a96af865ab5d56b77831ea2032

Workflow

Publishing configuration: github.com/NetherlandsForensicInstitute/lir/blob/34412cbb39e6e7a96af865ab5d56b77831ea2032/.github/workflows/release.yaml
Publishing logs: github.com/NetherlandsForensicInstitute/lir/actions/runs/35228141834/attempts/1

Artifact

Subject: lir-1.8.0-py3-none-any.whl
SHA-256 checksum: b4ec012d0f0bd18548daed138f00ac4dc4a109a247d18f7327c97b52e1fb730d Verifying attestations