quallmer: Qualitative Analysis with Large Language Models
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.
| Version: |
0.3.0 |
| Depends: |
R (≥ 3.5.0), ellmer (≥ 0.4.0) |
| Imports: |
cli, dplyr, tidyr, digest, irr, lifecycle, rlang, stats, yardstick |
| Suggests: |
ggplot2, janitor, knitr, rmarkdown, testthat (≥ 3.0.0), kableExtra, mockery, quanteda, quanteda.tidy, tibble, withr |
| Published: |
2026-02-16 |
| DOI: |
10.32614/CRAN.package.quallmer |
| Author: |
Seraphine F. Maerz
 [aut, cre],
Kenneth Benoit
[aut] |
| Maintainer: |
Seraphine F. Maerz <seraphine.maerz at unimelb.edu.au> |
| License: |
GPL-3 |
| URL: |
https://quallmer.github.io/quallmer/ |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
quallmer results |
Documentation:
Downloads:
Reverse dependencies:
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