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Introducing ankiR Stats: The Only Anki Addon with Time Series Analytics

I’m excited to announce the release of ankiR Stats, a new Anki addon that brings advanced statistical analysis to your flashcard reviews. If you’ve ever wondered about the patterns hidden in your study data, this addon is for you.

Why Another Stats Addon?

There are several statistics addons for Anki already – Review Heatmap, More Overview Stats, True Retention. They’re great for basic numbers. But none of them answer questions like:

Is my retention trending up or down over time?
What’s my weekly study pattern? Do I study more on weekends?
Which days were unusually productive (or lazy)?
How are my card intervals growing over months?

ankiR Stats answers all of these using the same statistical techniques data scientists use.

Features

📊 Time Series Charts

Track your retention, reviews, and intervals over time with a 4-week moving average to smooth out the noise:

🗓️ GitHub-style Heatmap

See your entire year of reviews at a glance:

🔬 Time Series Decomposition

This is the killer feature. The addon breaks down your daily reviews into three components:

Trend – Are you studying more or less over time?
Seasonal – Your weekly pattern (which days you study most)
Residual – Random variation that doesn’t fit the pattern

⚠️ Anomaly Detection

The addon automatically finds unusual study days using z-score analysis. Days where you studied way more (or less) than normal are flagged with their statistical significance.

No Dependencies

Unlike many addons that require you to install Python packages, ankiR Stats uses web-based charts (Chart.js). It works out of the box on Windows, Mac, and Linux.

Installation

Open Anki
Go to Tools → Add-ons → Get Add-ons
Enter code: 419954163
Restart Anki
Access via Tools → ankiR Stats

Based on ankiR

This addon is a Python port of key features from ankiR, an R package I developed for comprehensive Anki analytics. The R package has 91 functions including forecasting, autocorrelation analysis, and FSRS integration – if you want even deeper analysis, check it out.

Open Source

The addon is open source and available on GitHub. Issues and contributions welcome!

Links

Let me know what you think in the comments!