Online Algorithms for Statistics, Models, and Big Data Viz
Online algorithms are well suited for streaming data or when data is too large to hold in memory. OnlineStats processes observations one by one and all algorithms use O(1) memory.
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import Pkg
Pkg.add("OnlineStats")
using OnlineStats
o = Series(Mean(), Variance(), P2Quantile(), Extrema())
fit!(o, randn(10^6))
When contributing to OnlineStats, trivial PRs like documentation typos are very welcome! For nontrivial changes, please first discuss the change you wish to make via issue/email/slack with @joshday.
- Primary Author: Josh Day (@joshday)
- Significant early contributions from Tom Breloff (@tbreloff)
See also the list of contributors to OnlineStats.
OnlineStats is licensed under the MIT License - see the LICENSE.md file for details.