Frequentist and Bayesian linear regression for large data sets. Useful when the data does not fit into memory (for both frequentist and Bayesian regression), to make running time manageable (mainly for Bayesian regression), and to reduce the total running time because of reduced or less severe memory-spillover into the virtual memory. This is an implementation of Merge & Reduce for linear regression as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020). 'Streaming statistical models via Merge & Reduce'. International Journal of Data Science and Analytics, 1-17, <doi:10.1007/s41060-020-00226-0>.
|Author||Esther Denecke [aut], Leo N. Geppert [aut, cre], Steffen Maletz [ctb], R Core Team [ctb]|
|Maintainer||Leo N. Geppert <firstname.lastname@example.org>|
|License||GPL-2 | GPL-3|
|Package repository||View on CRAN|
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