tna: An R package for Transition Network Analysis
tna is an R package for the analysis of relational dynamics through
Transition Network Analysis (TNA). TNA provides tools for building TNA
models, plotting transition networks, calculating centrality measures,
and identifying dominant events and patterns. TNA statistical techniques
(e.g., bootstrapping and permutation tests) ensure the reliability of
observed insights and confirm that identified dynamics are meaningful.
See (Saqr et al., 2025) for
more details on TNA.
Resources
Companion tutorials
We have also released comprehensive new tutorials for the main TNA features:
| Tutorial | Link |
|---|---|
| An Updated Comprehensive Tutorial on Transition Network Analysis (TNA) | https://sonsoles.me/posts/tna-tutorial/ |
| TNA Group Analysis: Analysis and Comparison of Groups | https://sonsoles.me/posts/tna-group/ |
| TNA Clustering: Discovering and Analysis of Clusters | https://sonsoles.me/posts/tna-clustering/ |
| TNA Model Comparison:TNA Model Comparison: A Comprehensive Guide to Network Comparison | https://sonsoles.me/posts/tna-compare/ |
Full reference guide on tna functions | https://sonsoles.me/tna/tna.html |
Vignettes
Check out the tna R package vignettes:
| Vignette | Link |
|---|---|
| Getting started with tna | https://sonsoles.me/tna/articles/tna.html |
| A showcase of the main tna functions | https://sonsoles.me/tna/articles/complete_tutorial.html |
| How to prepare data for tna | https://sonsoles.me/tna/articles/prepare_data.html |
| Frequency-based TNA | https://sonsoles.me/tna/articles/ftna.html |
| Attention TNA | https://sonsoles.me/tna/articles/atna.html |
| Finding cliques and communities | https://sonsoles.me/tna/articles/communities_and_cliques.html |
| Using grouped sequence data | https://sonsoles.me/tna/articles/grouped_sequences.html |
Book chapters
Do not forget to check out our tutorials in the “Advanced learning analytics methods” book:
| Title | Pages | Tutorial |
|---|---|---|
| Saqr, M., Lopez-Pernas, S., & Tikka, S. Mapping Relational Dynamics with Transition Network Analysis: A Primer and Tutorial | https://doi.org/10.1007/978-3-031-95365-1_15 | Online tutorial |
| Saqr, M., Lopez-Pernas, S., & Tikka, S. Capturing the Breadth and Dynamics of the Temporal Processes with Frequency Transition Network Analysis: A Primer and Tutorial | https://doi.org/10.1007/978-3-031-95365-1_16 | Online tutorial |
| Lopez-Pernas, S., Tikka, S., & Saqr, M. Mining Patterns and Clusters with Transition Network Analysis: A Heterogeneity Approach | https://doi.org/10.1007/978-3-031-95365-1_17 | Online tutorial |
Other tools
In addition to the tna R package, you can also try our Shiny
app and Jamovi
plugin.
Installation
You can install the most recent stable version of tna from
CRAN or the development
version from GitHub by running one of the
following:
install.packages("tna")
# install.packages("devtools")
# devtools::install_github("sonsoleslp/tna")Example
Load the package
library("tna")Example data
data("group_regulation", package = "tna")Build a Markov model
tna_model <- tna(group_regulation)summary(tna_model)Plot the transition network
# Default plot
plot(tna_model) # Optimized plot
plot(
tna_model, cut = 0.2, minimum = 0.05,
edge.label.position = 0.8, edge.label.cex = 0.7
)cent <- centralities(tna_model)