{"skill":{"slug":"biostatistics","displayName":"Biostatistics: Actuarial-Level Statistical Analysis","summary":"Provides advanced actuarial-level biostatistical analyses including Bayesian inference, Monte Carlo simulation, machine learning, survival models, and health...","tags":{"latest":"1.0.0"},"stats":{"comments":0,"downloads":318,"installsAllTime":0,"installsCurrent":0,"stars":1,"versions":1},"createdAt":1774194514757,"updatedAt":1774194711154},"latestVersion":{"version":"1.0.0","createdAt":1774194514757,"changelog":"Initial release with comprehensive biostatistical and computational analytics capabilities:\n\n- Provides stochastic, chaos-theoretic, and propensity modeling frameworks for clinical data analysis.\n- Supports Bayesian analysis, Monte Carlo simulation, and classic statistical methods (e.g., survival analysis, mixed models).\n- Includes machine learning and deep learning tools for both structured and unstructured biomedical data.\n- Features robust multiple comparisons handling, actuarial modeling, and advanced health economics analytics.\n- Enforces rigorous statistical reporting standards (interval estimates, assumption checks, clinical relevance).\n- Python environment includes core data science and statistical libraries; PyMC installable if full Bayesian MCMC needed.","license":"MIT-0"},"metadata":null,"owner":{"handle":"cryptoreumd","userId":"publishers:cryptoreumd","displayName":"CryptoReuMD","image":"https://avatars.githubusercontent.com/u/106553699?v=4"},"moderation":null}