# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "quallmer" in publications use:' type: software license: GPL-3.0-or-later title: 'quallmer: Qualitative Analysis with Large Language Models' version: 0.4.0.9000 doi: 10.32614/CRAN.package.quallmer abstract: Tools for AI-assisted qualitative data coding using large language models ('LLMs') via the 'ellmer' package, supporting providers including 'OpenAI', 'Anthropic', 'Google', 'Azure', and local models via 'Ollama'. Provides a 'codebook'-based workflow for defining coding instructions and applying them to texts, images, and other data. Includes built-in 'codebooks' for common applications such as sentiment analysis and policy coding, and functions for creating custom 'codebooks' for specific research questions. Supports systematic replication across models and settings, computing inter-coder reliability statistics including Krippendorff's alpha (Krippendorff 2019, ) and Fleiss' kappa (Fleiss 1971, ), as well as gold-standard validation metrics including accuracy, precision, recall, and F1 scores following Sokolova and Lapalme (2009, ). Provides audit trail functionality for documenting coding workflows following Lincoln and Guba's (1985, ISBN:0803924313) framework for establishing trustworthiness in qualitative research. authors: - family-names: Maerz given-names: Seraphine F. email: seraphine.maerz@unimelb.edu.au orcid: https://orcid.org/0000-0002-7173-9617 - family-names: Benoit given-names: Kenneth email: kbenoit@smu.edu.sg orcid: https://orcid.org/0000-0002-0797-564X repository: https://quallmer.r-universe.dev commit: b2b9141497d3c6a656f6f27d0589be677acecaf4 url: https://quallmer.github.io/quallmer/ date-released: '2026-06-10' contact: - family-names: Maerz given-names: Seraphine F. email: seraphine.maerz@unimelb.edu.au orcid: https://orcid.org/0000-0002-7173-9617