Designed with a dual purpose of accurately estimating the mode (or modes) as well as characterizing the modality of data. The specific application area includes complex or mixture distributions particularly in a big data environment. The heterogeneous nature of (big) data may require deep introspective statistical and machine learning techniques, but these statistical tools often fail when applied without first understanding the data. In small datasets, this often isn't a big issue, but when dealing with large scale data analysis or big data thoroughly inspecting each dimension typically yields an O(n^n-1) problem. As such, dealing with big data require an alternative toolkit. This package not only identifies the mode or modes for various data types, it also provides a programmatic way of understanding the modality (i.e. unimodal, bimodal, etc.) of a dataset (whether it's big data or not). See <http://www.sdeevi.com/modes_package> for examples and discussion.
|Author||Sathish Deevi [aut, cre], 4D Strategies [aut,own]|
|Maintainer||Sathish Deevi <[email protected]>|
|License||CC BY-NC-SA 4.0|
|Package repository||View on CRAN|
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