https://quallmer.r-universe.devRecent package updates in quallmerR-universehttps://github.com/quallmer.pnghttps://quallmer.r-universe.devWed, 10 Jun 2026 00:52:23 GMTseraphine.maerz@unimelb.edu.au (Seraphine F. Maerz)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, <doi:10.4135/9781071878781>) and Fleiss' kappa (Fleiss 1971, <doi:10.1037/h0031619>), as well as gold-standard validation metrics including accuracy, precision, recall, and F1 scores following Sokolova and Lapalme (2009, <doi:10.1016/j.ipm.2009.03.002>). Provides audit trail functionality for documenting coding workflows following Lincoln and Guba's (1985, ISBN:0803924313) framework for establishing trustworthiness in qualitative research.https://github.com/r-universe/quallmer/actions/runs/27249886616Wed, 10 Jun 2026 00:52:23 GMTquallmer0.4.0.9000successhttps://quallmer.r-universe.devhttps://github.com/quallmer/quallmergetting-started.Rmdgetting-started.htmlGetting started with quallmer2025-11-01 03:36:282026-05-06 02:14:00seraphine.maerz@unimelb.edu.au (Seraphine F. Maerz)Companion package to 'quallmer' providing an interactive 'shiny' application for manual coding, reviewing large language model (LLM) generated annotations, and computing inter-rater reliability metrics. Supports three modes: blind manual coding, LLM output validation, and agreement calculation. Computes standard reliability metrics including Krippendorff's alpha (Krippendorff 2019 <doi:10.4135/9781071878781>), Cohen's kappa, Fleiss' kappa (Fleiss 1971 <doi:10.1037/h0031619>), intraclass correlation coefficient (ICC), and percent agreement for nominal, ordinal, interval, and ratio data. Also computes gold-standard validation metrics including accuracy, precision, recall, and F1 scores following Sokolova and Lapalme (2009 <doi:10.1016/j.ipm.2009.03.002>).https://github.com/r-universe/quallmer/actions/runs/27090071115Mon, 09 Mar 2026 00:51:23 GMTquallmer.app0.1.0successhttps://quallmer.r-universe.devhttps://github.com/quallmer/quallmer.app