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 Pkg

Pkg.add("OnlineStats")

using OnlineStats

# Create several statistics
o = Series(Mean(), Variance(), Extrema())

# Update with single data point
fit!(o, 1.0)

# Iterate through and update with lots of data
fit!(o, randn(10^6))

# Get the values of the statistics
value(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 from Tom Breloff (@tbreloff)
• Many algorithms developed under mentorship of Hua Zhou (@Hua-Zhou)

See also the list of contributors to OnlineStats.