Provides tools for performing transition network analysis (TNA),
including functions for building TNA models, plotting transition networks,
and calculating centrality measures. The package relies on the qgraph
and igraph for network plotting and centrality measure calculations.
References
Saqr M., López-Pernas S., Törmänen T., Kaliisa R., Misiejuk K., Tikka S. (2025). Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning Processes. In Proceedings of the 15th International Learning Analytics and Knowledge Conference (LAK '25), 351-361.
Banerjee A., Chandrasekhar A., Duflo E., Jackson M. (2014). Gossip: Identifying Central Individuals in a Social Network. Working Paper.
Kivimaki, I., Lebichot, B., Saramaki, J., Saerens, M. (2016). Two betweenness centrality measures based on Randomized Shortest Paths. Scientific Reports, 6, 19668.
Serrano, M. A., Boguna, M., Vespignani, A. (2009). Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences, 106, 6483-6488.
Zhang, B., Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1).
See also
Useful links:
Report bugs at https://github.com/sonsoleslp/tna/issues/
Basic functions
build_model(),
hist.group_tna(),
hist.tna(),
plot.group_tna(),
plot.tna(),
plot_mosaic(),
plot_mosaic.group_tna(),
plot_mosaic.tna_data(),
prepare_data(),
print.group_tna(),
print.summary.group_tna(),
print.summary.tna(),
print.tna(),
print.tna_data(),
summary.group_tna(),
summary.tna()