# Sequenzo

> Sequenzo is a Python package for social sequence analysis. It analyzes categorical trajectories such as careers, family formation, and country development paths, with tools for distances, clustering, indicators, visualization, hidden Markov models, event mining, and group comparison. This file lists the main documentation pages for language models and AI tools.

## Getting started

- [Quickstart](https://sequenzo.yuqi-liang.tech/en/basics/quickstart): a complete first analysis with a bundled dataset
- [Installing](https://sequenzo.yuqi-liang.tech/en/basics/installing): pip installation and supported Python versions
- [Typical Workflow](https://sequenzo.yuqi-liang.tech/en/basics/typical-workflow): the analysis map, from data to method families
- [About Sequenzo](https://sequenzo.yuqi-liang.tech/en/basics/about-sequenzo): scope, performance, and relation to R packages

## Typical Workflow Functions

- [Function Reference Map](https://sequenzo.yuqi-liang.tech/en/function-library/introduction): task-based index of public functions
- [SequenceData](https://sequenzo.yuqi-liang.tech/en/function-library/sequence-data): define the sequence data object
- [get_distance_matrix()](https://sequenzo.yuqi-liang.tech/en/function-library/get-distance-matrix): pairwise sequence dissimilarities
- [Cluster()](https://sequenzo.yuqi-liang.tech/en/function-library/hierarchical-clustering): hierarchical clustering
- [ClusterQuality()](https://sequenzo.yuqi-liang.tech/en/function-library/cluster-quality): choose the number of clusters
- [KMedoids()](https://sequenzo.yuqi-liang.tech/en/function-library/KMedoids): PAM and k-medoids clustering

## Tutorials

- [Tutorials Overview](https://sequenzo.yuqi-liang.tech/en/tutorials/introduction): suggested reading order by research goal
- [Basic Concepts](https://sequenzo.yuqi-liang.tech/en/tutorials/basic-concepts): states, spells, subsequences
- [Dissimilarity Measures](https://sequenzo.yuqi-liang.tech/en/tutorials/dissimilarity-measures): choosing a distance measure
- [Cluster Quality Indicators](https://sequenzo.yuqi-liang.tech/en/tutorials/cluster-quality-indicators): reading CQI tables
- [How to Read Sequence Plots](https://sequenzo.yuqi-liang.tech/en/tutorials/reading-sequence-plots): interpreting sequence visualizations
- [Reporting Results of the Typical Workflow](https://sequenzo.yuqi-liang.tech/en/tutorials/reporting-sequence-analysis): writing up methods for a paper

## Method families

- [Visualization Tools](https://sequenzo.yuqi-liang.tech/en/visualization/introduction): plot parameters and gallery
- [Sequence Characteristics Indicators](https://sequenzo.yuqi-liang.tech/en/sequence-characteristics-indicators/introduction): per-sequence indicators
- [Markov Chain Models](https://sequenzo.yuqi-liang.tech/en/markov-chain-models/introduction): HMM, MHMM, NHMM, MNHMM workflows
- [Event Sequences](https://sequenzo.yuqi-liang.tech/en/event-sequences/introduction): frequent event subsequences
- [Discrepancy Analysis](https://sequenzo.yuqi-liang.tech/en/discrepancy-analysis/introduction): distance-based group association
- [Multidomain Analysis](https://sequenzo.yuqi-liang.tech/en/multidomain/introduction): CAT, DAT, IDCD, polyadic workflows
- [From Sequences to Variables](https://sequenzo.yuqi-liang.tech/en/beyond-basic-clustering/from-sequences-to-variables/introduction): regression-ready variables
- [CLARA for Large Data](https://sequenzo.yuqi-liang.tech/en/big-data/clara): clustering large datasets

## Datasets and R users

- [Datasets Overview](https://sequenzo.yuqi-liang.tech/en/datasets/introduction): bundled example datasets
- [Sequenzo and R](https://sequenzo.yuqi-liang.tech/en/traminer-and-sequenzo/introduction): entry point for TraMineR, WeightedCluster, TraMineRextras, and seqHMM users
- [Functions Comparison](https://sequenzo.yuqi-liang.tech/en/traminer-and-sequenzo/functions-comparison): R-to-Python function map
- [Citation Guide](https://sequenzo.yuqi-liang.tech/en/basics/how-to-cite): how to cite the package and documentation
- [View Our Tutorials Online](https://sequenzo.yuqi-liang.tech/en/basics/view-tutorials-online): Google Colab tutorials and source notebooks
- [Join Our Community](https://sequenzo.yuqi-liang.tech/en/basics/join-our-community): community and contribution entry points
- [Bug Reports and Feature Requests](https://sequenzo.yuqi-liang.tech/en/faq/bug_reports_and_feature_requests): where to report issues
- [GitHub Repository](https://github.com/Liang-Team/Sequenzo): source code for the Python package
- [PyPI Package](https://pypi.org/project/sequenzo/): released package builds