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, 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.
