This article introduces the TraMineR toolbox for analyzing and visualizing categorical sequence data, focusing on state sequences. State sequences are sequences of discrete or categorical data where each position in the sequence carries meaningful information, such as age, date, or elapsed time. The core of the package is the state sequence object, which stores the set of sequences along with attributes like the alphabet, state labels, and color palette. The article covers various functionalities, including: 1. **Description of Sets of Sequences**: Techniques for describing sets of sequences, such as transversal aggregated views and longitudinal characteristics. 2. **Visualizing Sequences**: Multiple methods for visualizing sequences, including sequence index plots, frequency plots, and modal state sequences. 3. **Computing Dissimilarities**: Measures of pairwise dissimilarities between sequences, such as optimal matching and other metrics. 4. **Advanced Analyses**: Advanced analyses like clustering and statistical modeling of sequence data. The article also provides a detailed guide on how to use the TraMineR package, including creating state sequence objects, handling missing values, and computing various statistical measures. It demonstrates these methods using the `mvad` dataset, which contains data on school-to-work transitions in Northern Ireland. The article concludes with a discussion of the limitations and future directions of the package.This article introduces the TraMineR toolbox for analyzing and visualizing categorical sequence data, focusing on state sequences. State sequences are sequences of discrete or categorical data where each position in the sequence carries meaningful information, such as age, date, or elapsed time. The core of the package is the state sequence object, which stores the set of sequences along with attributes like the alphabet, state labels, and color palette. The article covers various functionalities, including: 1. **Description of Sets of Sequences**: Techniques for describing sets of sequences, such as transversal aggregated views and longitudinal characteristics. 2. **Visualizing Sequences**: Multiple methods for visualizing sequences, including sequence index plots, frequency plots, and modal state sequences. 3. **Computing Dissimilarities**: Measures of pairwise dissimilarities between sequences, such as optimal matching and other metrics. 4. **Advanced Analyses**: Advanced analyses like clustering and statistical modeling of sequence data. The article also provides a detailed guide on how to use the TraMineR package, including creating state sequence objects, handling missing values, and computing various statistical measures. It demonstrates these methods using the `mvad` dataset, which contains data on school-to-work transitions in Northern Ireland. The article concludes with a discussion of the limitations and future directions of the package.