https://seraphinem.r-universe.devRecent package updates in seraphinemR-universehttps://github.com/seraphinem.pnghttps://seraphinem.r-universe.devThu, 22 Oct 2026 10:06:39 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, audio recordings, and other data, with optional transcription of audio and translation of texts before coding through 'DeepL', 'Google', 'Qwen-MT', or a chat model. 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/39763717631Thu, 22 Oct 2026 10:06:39 GMTquallmer0.6.0.9000successhttps://quallmer.r-universe.devhttps://github.com/quallmer/quallmergetting-started.Rmdgetting-started.htmlGetting started with quallmer2025-11-01 03:36:282026-09-28 09:25:45seraphine.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/39767307272Mon, 09 Mar 2026 00:51:23 GMTquallmer.app0.1.0successhttps://quallmer.r-universe.devhttps://github.com/quallmer/quallmer.app