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  "Title": "Post-Linkage Data Analysis Based on Mixture Modelling",
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  "Description": "Perform inference in the secondary analysis setting with\nlinked data potentially containing mismatch errors. Only the\nlinked data file may be accessible and information about the\nrecord linkage process may be limited or unavailable.\nImplements the 'General Framework for Regression with\nMismatched Data' developed by Slawski et al. (2025)\n<doi:10.1093/jrsssa/qnae083>. The framework uses a mixture\nmodel for pairs of linked records whose two components reflect\ndistributions conditional on match status, i.e., correct match\nor mismatch. Inference is based on composite likelihood and the\nExpectation-Maximization (EM) algorithm. The package currently\nsupports Cox Proportional Hazards Regression (right-censored\ndata only) and Generalized Linear Regression Models (Gaussian,\nGamma, Poisson, and Logistic (binary models only)). Information\nabout the underlying record linkage process can be incorporated\ninto the method if available (e.g., assumed overall mismatch\nrate, safe matches, predictors of match status, or predicted\nprobabilities of correct matches).",
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