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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Quickstart
import Pkg
Pkg.add("OnlineStats")
using OnlineStats
o = Series(Mean(), Variance(), P2Quantile(), Extrema())
fit!(o, 1.0)
fit!(o, randn(10^6))Documentation
Contributing
- Trivial PRs such as fixing typos are very welcome!
- For nontrivial changes, you'll probably want to first discuss the changes via issue/email/slack with
@joshday.
Authors
- Primary Author: Josh Day (@joshday)
- Significant early contributions from Tom Breloff (@tbreloff)
- Many algorithms developed under mentorship of Hua Zhou (@Hua-Zhou)
See also the list of contributors to OnlineStats.
License
Packages Using OnlineStats/OnlineStatsBase
See JuliaHub:
