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  "Description": "Provides a flexible, simulation-based toolkit for\nexploring how much data are needed to develop reliable\nprediction models. It works by repeatedly generating data,\nfitting models, and evaluating performance to show how sample\nsize affects predictive accuracy, calibration, and overfitting.\nThe package supports continuous, binary, and time-to-event\noutcomes and can be used with both regression-based modelling\napproaches and machine-learning methods. It is designed to help\nresearchers plan studies, assess feasibility, and build more\nrobust and generalisable models. The methods are described in\nOlaniran et al. (2026) <doi:10.1186/s12874-026-02935-9> and\nShamsutdinova et al. (2026) <doi:10.48550/arXiv.2602.23507>.",
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    }
  ]
}