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  "Title": "Implements Generalized Meta-Analysis Using Iterated Reweighted\nLeast Squares Algorithm",
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  "Authors@R": "c(person(\"Prosenjit\", \"Kundu\", role = c(\"aut\", \"cre\"),\nemail = \"28pkundu1992@gmail.com\"),\nperson(\"Runlong\", \"Tang\", role = \"aut\"),\nperson(\"Nilanjan\", \"Chatterjee\", role = \"aut\"))",
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  "Description": "Generalized meta-analysis is a technique for estimating\nparameters associated with a multiple regression model through\nmeta-analysis of studies which may have information only on\npartial sets of the regressors. It estimates the effects of\neach variable while fully adjusting for all other variables\nthat are measured in at least one of the studies. Using\nalgebraic relationships between regression parameters in\ndifferent dimensions, a set of moment equations is specified\nfor estimating the parameters of a maximal model through\ninformation available on sets of parameter estimates from a\nseries of reduced models available from the different studies.\nThe specification of the equations requires a reference dataset\nto estimate the joint distribution of the covariates. These\nequations are solved using the generalized method of moments\napproach, with the optimal weighting of the equations taking\ninto account uncertainty associated with estimates of the\nparameters of the reduced models. The proposed framework is\nimplemented using iterated reweighted least squares algorithm\nfor fitting generalized linear regression models. For more\ndetails about the method, please see pre-print version of the\nmanuscript on generalized meta-analysis by Prosenjit Kundu,\nRunlong Tang and Nilanjan Chatterjee (2018)\n<doi:10.1093/biomet/asz030>.The current version (0.2.0) is\nupdated to address some of the stability issues in the previous\nversion (0.1).",
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