Many data science problems reduce to operations on very tall, skinny matrices. However, sometimes these matrices can be so tall that they are difficult to work with, or do not even fit into main memory. One strategy to deal with such objects is to distribute their rows across several processors. To this end, we offer an 'S4' class for tall, skinny, distributed matrices, called the 'shaq'. We also provide many useful numerical methods and statistics operations for operating on these distributed objects. The naming is a bit "tongue-in-cheek", with the class a play on the fact that 'Shaquille' 'ONeal' ('Shaq') is very tall, and he starred in the film 'Kazaam'.
|Depends:||R (≥ 3.0.0), pbdMPI (≥ 0.3-0)|
|Author:||Drew Schmidt [aut, cre], Wei-Chen Chen [aut], Mike Matheson [aut], George Ostrouchov [aut], ORNL [cph]|
|Maintainer:||Drew Schmidt <wrathematics at gmail.com>|
|MailingList:||Please send questions and comments regarding pbdR to RBigData@gmail.com|
|License:||BSD 2-clause License + file LICENSE|
|Citation:||kazaam citation info|
|CRAN checks:||kazaam results|
Guide to the kazaam Package
|Windows binaries:||r-devel: kazaam_0.1-0.zip, r-release: kazaam_0.1-0.zip, r-oldrel: kazaam_0.1-0.zip|
|macOS binaries:||r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available|
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