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  "Title": "Fitting Linear and Generalized Linear Models in \"Divide and\nRecombine\" Approach to Large Data Sets",
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  "Authors@R": "c(person(given = \"Md. Mahadi Hassan\", family = \"Nayem\", email = \"mhnayem.cu.stat@outlook.com\", role = c(\"aut\", \"cre\")))",
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  "Description": "To overcome the memory limitations for fitting linear (LM)\nand Generalized Linear Models (GLMs) to large data sets, this\npackage implements the Divide and Recombine (D&R) strategy. It\nbasically divides the entire large data set into suitable\nsubsets manageable in size and then fits model to each subset.\nFinally, results from each subset are aggregated to obtain the\nfinal estimate. This package also supports fitting GLMs to data\nsets that cannot fit into memory and provides methods for\nfitting GLMs under linear regression, binomial regression,\nPoisson regression, and multinomial logistic regression\nsettings. Respective models are fitted using different D&R\nstrategies as described by: Xi, Lin, and Chen (2009)\n<doi:10.1109/TKDE.2008.186>, Xi, Lin and Chen (2006)\n<doi:10.1109/TKDE.2006.196>, Zuo and Li (2018)\n<doi:10.4236/ojs.2018.81003>, Karim, M.R., Islam, M.A. (2019)\n<doi:10.1007/978-981-13-9776-9>.",
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