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  "Description": "Item response and response time models fitted from their\nfull conditionals. A built-in model is chosen with ecm(data,\nmodel = ...) or gibbs(data, model = ...): the two-parameter\nlogistic model, alone or with log-normal response times (van\nder Linden, 2007, <doi:10.1007/s11336-006-1478-z>), latent\nregressions of ability and speed, cross-relations between\nability and the log response times, and their quantile\n(asymmetric Laplace) versions. The main estimator is ECM\n(ecm(); Meng and Rubin, 1993, <doi:10.1093/biomet/80.2.267>):\none conditional maximization per block of parameters,\nPolya-Gamma bounds for logistic likelihoods (Polson, Scott and\nWindle, 2013, <doi:10.1080/01621459.2013.829001>), adaptive or\nBock-Aitkin quadrature (Bock and Aitkin, 1981,\n<doi:10.1007/BF02293801>) with normal-linear latent variables\nintegrated exactly, and Louis' standard errors; quantile models\nmaximize the asymmetric Laplace working likelihood, with\nsandwich standard errors. The same steps drive Gibbs sampling\n(gibbs(); conjugate updates and Metropolis-adjusted Langevin\nsteps), with samplers in C++ ('RcppArmadillo') that port the\n'Julia' package 'ExtendedRtIrtModeling.jl' (its function names\nare kept as aliases). Both estimators have a check:\ncheck_ecm(), and check_sampler() with the Geweke (2004,\n<doi:10.1198/016214504000001132>) test and simulation-based\ncalibration. equations() and algorithm() write out the model\nand every step, before or after fitting.",
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