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data_science_for_sensory
data_science_for_sensory PublicData Science for Sensory and Consumer Scientists. A practical guide to statistical methods for sensory evaluation and consumer research.
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Keep Claude's capability, strip the personality: shared CLAUDE.md, /stop-the-charade skill, optional enforcement hooks
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Colab-ready fork of Karpathy's autoresearch. Autonomous AI-driven LLM training experiments on a single GPU.
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FactoMineR-compatible multivariate exploratory data analysis in pure Python: PCA, CA, MCA, FAMD, MFA/HMFA/DMFA, GPA, HCPC, CaGalt, regression (LinearModel/AovSum/RegBest), textual, predict/reconst/estim_ncp, dimdesc/catdes/condes — numerically parity-verified against R FactoMineR 2.14.
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Python port of the sensR package for sensory discrimination and thurstonian modeling. Statistical methods for difference testing, signal detection, and sensory analysis.
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Python tools for reducing compact letter displays while preserving pairwise statistical relationships
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Multi-agent conversation relay skill for Claude Code. Run multi-turn brainstorming sessions between coding agent CLIs (Codex, Gemini CLI, etc.) using a shared markdown scratchpad.
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