Forensic Statistics Tools
Open-Source Tools for Objective Forensic Evidence Analysis
CSAFE develops and supports open-source statistical tools designed to advance forensic science through transparency, reproducibility, and data-driven methods.
These tools help researchers, forensic practitioners, and analysts apply statistical and computational approaches to evidence evaluation across disciplines including firearms examination, handwriting analysis, footwear impressions, digital forensics, and temporal event analysis.
Many of these tools are available as R packages or standalone applications and are intended to support objective, scientifically grounded forensic workflows.
Explore the tools below to learn more and access each application or package directly.
Core Apps
bulletAnalyzr
bulletAnalyzr uses 3D imaging and computational methods to compare bullets and evaluate whether they were fired from the same firearm.
The application analyzes high-resolution bullet surface scans to identify and compare unique markings left by a gun barrel, supporting objective and data-driven firearm evidence analysis workflows.
Handwriter
Handwriter assists forensic document examination by analyzing handwritten documents against a closed set of candidate writers and estimating the probability that each individual authored the document.
The package includes tools for identifying letters and extracting handwriting features, providing an automated and statistically informed approach to handwriting comparison.
HyperSample
HyperSample is an application designed to support statistical inference and sampling decisions in seized drug analysis. After downloading and unzipping the installation package, users can run the application locally.
It provides tools for estimating the composition of positive items, such as items containing a target drug, and negative items, such as items not containing the target drug, in a population. It also supports sample-size calculation and evaluation of confidence levels under various sampling scenarios.
shoecomp / shoecomp++
shoecomp and shoecomp++ provide tools for working with shoeprint images, including marking points of interest, aligning images, and exporting comparisons to additional viewing platforms.
These tools support footwear examination workflows by improving image registration and facilitating more consistent comparative analysis.
ShoeprintAnalyzr
ShoeprintAnalyzr is designed to support the comparison of footwear impressions through image analysis and visualization tools.
The application assists examiners and researchers in evaluating similarities and differences between shoeprint evidence using computational methods.
Additional Apps
Assocr
Assocr provides statistical tools for determining whether two temporal event streams were generated by the same source.
The package implements score-based likelihood ratio and coincidental match probability methods, supporting objective analysis of time-based event data.
BulletCP
BulletCP uses Bayesian change point detection to automatically identify groove locations on bullets as part of the preprocessing stage for bullet matching algorithms.
The method detects rapid changes in surface data to determine where bullet lands begin and end, supporting more accurate and automated firearm evidence analysis workflows.
cmcR
The cmcR package provides an open-source implementation of the Congruent Matching Cells (CMC) method for cartridge case identification. The tool compares cartridge case surface scans by dividing images into small regions, or “cells,” and identifying matching patterns between scans.
Researchers and examiners can use cmcR to evaluate whether two cartridge cases were fired from the same firearm using objective, repeatable statistical methods.
cmpsR
The cmpsR package implements the Congruent Matching Profile Segments (CMPS) method for comparing striated toolmarks, including bullet signatures.
Designed to support objective forensic comparisons, cmpsR analyzes profile segments across toolmarks to identify similarities and quantify comparison results using statistical approaches.
EviHunter
EviHunter identifies files on Android devices likely to contain evidentiary data such as GPS locations, visited URLs, or text messages.
Using static analysis of application code, EviHunter builds mappings between apps and the files where evidence-related information may be stored, helping investigators more efficiently locate relevant digital evidence.