Package: quallmer Type: Package Title: Qualitative Analysis with Large Language Models Version: 0.4.0.9000 Authors@R: c( person( "Seraphine F.", "Maerz", email = "seraphine.maerz@unimelb.edu.au", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7173-9617") ), person( "Kenneth", "Benoit", email = "kbenoit@smu.edu.sg", role = c("aut"), comment = c(ORCID = "0000-0002-0797-564X") ) ) Description: 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. License: GPL (>= 3) URL: https://quallmer.github.io/quallmer/ Depends: R (>= 3.5.0), ellmer (>= 0.4.0) Imports: cli, digest, lifecycle, rlang, stats, tibble, vctrs Encoding: UTF-8 LazyData: true Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.3 Suggests: ggplot2, janitor, knitr, rmarkdown, testthat (>= 3.0.0), kableExtra, mockery, quanteda, quanteda.tidy, withr, yardstick Config/testthat/edition: 3 VignetteBuilder: knitr Config/pak/sysreqs: libssl-dev Repository: https://quallmer.r-universe.dev Date/Publication: 2026-06-24 03:37:38 UTC RemoteUrl: https://github.com/quallmer/quallmer RemoteRef: HEAD RemoteSha: f68dad9840184675d800551c7ab51e6e8225d68f NeedsCompilation: no Packaged: 2026-06-24 07:05:13 UTC; root Author: Seraphine F. Maerz [aut, cre] (ORCID: ), Kenneth Benoit [aut] (ORCID: ) Maintainer: Seraphine F. Maerz