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  "Title": "Hierarchical Bayesian Inference for Choice Response Time Models",
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  "Description": "Provides tools for fitting hierarchical Bayesian models of\nchoice and response time using Differential Evolution Markov\nChain Monte Carlo (DE-MCMC) sampling. Designed for cognitive\nscientists and psychologists, it supports models including the\nLinear Ballistic Accumulator (LBA) and Diffusion Decision Model\n(DDM), offering flexible parameter mapping and\ncondition-specific modelling. Implements fast, parallelised C++\nroutines for large-scale applications in decision modelling.\nCore functionality includes parameter sampling, simulation,\nmodel building, posterior recovery, and convergence\ndiagnostics. Sampling methods follow Turner et al. (2013)\n<doi:10.1037/a0032222>, Braak (2006)\n<doi:10.1007/s11222-006-8769-1>, and Hu & Tsui (2010)\n<doi:10.1016/j.csda.2008.10.025>. The parameter mapping and\ncondition-specific structure are based on Heathcote et al.\n(2018) <doi:10.3758/s13428-018-1067-y>.",
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        "setDEInput"
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        "gelman,posterior-method",
        "gelman-methods"
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        "ggdmc"
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