hypogenic

To run hypothesis generation, execute the command line interface with a YAML configuration file, specifying the generation method and desired number of hypotheses. The system requires training and testing datasets to execute the automated discovery workflow.

Automated hypothesis testing requires a Python environment managed by pip or uv, and uses Redis for caching. You must also install s2orc-doc2json and grobid dependencies to parse scientific literature for the AI-driven discovery process.

Integrating scientific literature into empirical data analysis involves using automated tools to extract insights from papers and combine them with observational data. This approach complements raw data patterns with established research context for more robust hypothesis generation.

AI-driven discovery supports content analysis and deception detection by generating hypotheses from observational data patterns. You provide the training and testing datasets, and the system iteratively refines hypotheses based on the empirical evidence and integrated literature.

Limitations of automated hypothesis generation include strict dependency on configuration files, training datasets, and testing datasets. The process also requires external services like Redis, grobid, and s2orc-doc2json to successfully parse literature and execute the discovery workflow.