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.
