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.