Analyzing and Visualizing State Sequences in R with TraMineR

By Alexis Gabadinho, Gilbert Ritschard, Nicolas S. Müller, Matthias Studer. 2011

1,327 citations recorded by the source corpus.

This article describes the many capabilities offered by the TraMineR toolbox for categorical sequence data. It focuses more specifically on the analysis and rendering of state sequences. Addressed features include the description of sets of sequences by means of transversal aggregated views, the computation of longitudinal characteristics of individual sequences and the measure of pairwise dissimilarities. Special emphasis is put on the multiple ways of visualizing sequences. The core element of the package is the state se- quence object in which we store the set of sequences together with attributes such as the alphabet, state labels and the color palette. The functions can then easily retrieve this information to ensure presentation homogeneity across all printed and graphical displays. The article also demonstrates how TraMineR’s outcomes give access to advanced analyses such as clustering and statistical modeling of sequence data.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c50d1ed3f988745f7b969cfb42d38be1a6580f76

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Citations in the corpus

Gaze Prediction in Dynamic 360° Immersive Videos (2018, background): “Most of the models are based on the bottom-up [10] [2] [14] [23], top-down [25] [13] [8] [22] [29], or hybrid approaches to detect salient regions on images.”
Learning analytics to unveil learning strategies in a flipped classroom (2017, neutral): “The computation of the distance (similarity) between learning sequences, required for the clustering algorithm, was based on the optimal matching distance metric (Gabadinho et al., 2011), which is a variant of the Levenshtein’s edit distance (Levenshtein, 1966).”
Saliency in VR: How Do People Explore Virtual Environments? (2016, background): “Additional analysis on the influence of the viewport, including also a state sequence analysis [14, 49], can be found in the supplement.”
Transition to Adulthood (2015, background): “The high number of distinct sequences present in descriptive analysis was then condensed to three groups based on clustering the sequences from the pairwise optimal matching distances (Gabadinho et al., 2011; Gabadinho & Ritschard, 2013).”
WeightedCluster Library Manual A practical guide to creating typologies of trajectories in the social sciences with R (2013, methodology): “In this manual, we illustrate the methods presented with the aid of data from the study by McVicar and Anyadike-Danes (2002), available in TraMineR (Gabadinho et al. 2011), which will enable the reader to reproduce the set of analyses presented.”