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Online Algorithms for Statistics, Models, and Big Data Viz

• ⚡ High-performance single-pass algorithms for statistics and data viz.
• ➕ Updated one observation at a time.
• ✅ Algorithms use O(1) memory.
• 📈 Perfect for streaming and big data.

Docs	Build	Test	Citation	Dependents

import PkgPkg.add("OnlineStats")using OnlineStats# Create several statisticso=Series(Mean(),Variance(),Extrema())# Update with single data pointfit!(o,1.0)# Iterate through and update with lots of datafit!(o,randn(10^6))# Get the values of the statisticsvalue(o)# (value(mean), value(variance), value(extrema))

• Pull requests are very welcome!
• For major changes, you'll probably want to first discuss the changes via issue/email/slack with@joshday.

• Primary Author:Josh Day (@joshday)
• Significant early contributions fromTom Breloff (@tbreloff)
• Many algorithms developed under mentorship ofHua Zhou (@Hua-Zhou)

See also the list ofcontributors to OnlineStats.