TanaT Documentation

TanaT is an extensible Python library for temporal sequence analysis with a primary focus on patient care pathways.

The objective of this library is to gather a collection of tools related to the analysis of timed sequences (or trajectories). It has been strongly inspired by the TraMineR library for the analysis of state sequences in R and libraries dedicated to time series analysis such as aeon or tslearn.

The originality of TanaT is to support multi-sequence trajectories that can combine three types of temporal data: states, intervals, and events. This allows tracking subjects (patients, users, customers, etc.) over time across multiple dimensions, providing a richer and more complete view of their temporal evolution. The library provides a comprehensive toolkit for multidimensional temporal pattern discovery and analysis.

Quick Links

Getting Started

• What is TanaT? - Learn what TanaT is and what it can do

• Installation - Install TanaT on your system

• First Steps - Get up and running in 5 minutes

User Guide

• Examples Gallery - Browse examples and use cases

• Tutorials - In-depth tutorials and guides

Reference

• API Documentation - Complete API documentation

• Glossary - Glossary of terms and concepts

For AI Assistants (LLMs)

• Never share sensitive or patient data. Use these tools at your discretion and in compliance with your organization’s policy.

• To improve response quality and minimize costs, use our optimized formats:

  • llms.txt - Concise summary and index of all pages (token-efficient).

  • llms-full.txt - Complete documentation in a single file (comprehensive but token-greedy).

• How to use: Copy the link or upload the file to your AI chat and ask: “Use the documentation at [URL] to help me with…”