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drglm

Fitting Linear and Generalized Linear Models in "Divide and Recombine" Approach to Large Data Sets

v1.1 · Jul 30, 2024 · GPL (>= 3)

Description

To overcome the memory limitations for fitting linear (LM) and Generalized Linear Models (GLMs) to large data sets, this package implements the Divide and Recombine (D&R) strategy. It basically divides the entire large data set into suitable subsets manageable in size and then fits model to each subset. Finally, results from each subset are aggregated to obtain the final estimate. This package also supports fitting GLMs to data sets that cannot fit into memory and provides methods for fitting GLMs under linear regression, binomial regression, Poisson regression, and multinomial logistic regression settings. Respective models are fitted using different D&R strategies as described by: Xi, Lin, and Chen (2009) <doi:10.1109/TKDE.2008.186>, Xi, Lin and Chen (2006) <doi:10.1109/TKDE.2006.196>, Zuo and Li (2018) <doi:10.4236/ojs.2018.81003>, Karim, M.R., Islam, M.A. (2019) <doi:10.1007/978-981-13-9776-9>.

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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies nnet speedglm drglm

Version History

new 1.1 Mar 10, 2026
updated 1.1 ← 1.0 diff Jul 29, 2024
new 1.0 Jul 11, 2024