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cff-version: 1.2.0
message: 'To cite package "petrographer" in publications use:'
type: software
license: MIT
title: 'petrographer: Petrographic Thin Section Analysis with Deep Learning'
version: 0.0.0.9000
doi: 10.32614/CRAN.package.petrographer
abstract: Automated petrographic analysis of thin section images using deep learning
  models. Provides tools for training RF-DETR detection and segmentation models, running
  predictions with SAHI (Slicing Aided Hyper Inference), calculating morphological
  properties, and analyzing results. Supports both local and HPC training workflows
  with seamless R-Python integration via reticulate.
authors:
- family-names: Gauthier
  given-names: Nicolas
  email: nicolas.gauthier@ufl.edu
  orcid: https://orcid.org/0000-0002-2225-5827
- family-names: Rutkoski
  given-names: Ashley
  email: arutkoski@ufl.edu
repository: https://flmnh-ai.r-universe.dev
repository-code: https://github.com/flmnh-ai/petrographer
commit: cdaf5711967f2c2016b52ff728a364e441166f8e
url: https://flmnh-ai.github.io/petrographer/
date-released: '2026-04-21'
contact:
- family-names: Gauthier
  given-names: Nicolas
  email: nicolas.gauthier@ufl.edu
  orcid: https://orcid.org/0000-0002-2225-5827