Transition Network Analysis (TNA)

Provides tools for performing Transition Network Analysis (TNA) to study relational dynamics, including functions for building and plotting TNA models, 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.

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:

Vignettes

Check out the tna R package vignettes:

Book chapters

Do not forget to check out our tutorials in the “Advanced learning analytics methods” book:

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

plot(tna_model) 

cent <- centralities(tna_model)

Plot the centrality measures

plot(cent, ncol = 3)

Estimate centrality stability

estimate_centrality_stability(tna_model)
#> Centrality Stability Coefficients
#> 
#>  InStrength OutStrength Betweenness 
#>         0.9         0.9         0.9

Identify and plot communities

coms <- communities(tna_model)
plot(coms)

Find and plot cliques

cqs <- cliques(tna_model, threshold = 0.12)
plot(cqs)

Compare high achievers (first 1000) with low achievers (last 1000)

tna_model_start_high <- tna(group_regulation[1:1000, ])
tna_model_start_low <- tna(group_regulation[1001:2000, ])
comparison <- permutation_test(
  tna_model_start_high, 
  tna_model_start_low,
  measures = c("InStrength")
)

Simple comparison vs. permutation test comparison

plot_compare(tna_model_start_high, tna_model_start_low)
plot(comparison)

Compare centralities

print(comparison$centralities$stats)

Papers using TNA

• Saqr, M., Lopez-Pernas, S., Tormanen, 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. Proceedings of the 15th International Learning Analytics and Knowledge Conference (LAK ’25), 351–361. ACM. https://doi.org/10.1145/3706468.3706513
• Tikka, S., Lopez-Pernas, S., & Saqr, M. (2025). tna: An R Package for Transition Network Analysis. Applied Psychological Measurement (online ahead of print). doi:10.1177/01466216251348840
• López-Pernas, S., Misiejuk, K., Kaliisa, R., & Saqr, M. (2025). Capturing the process of students’ AI interactions when creating and learning complex network structures. IEEE Transactions on Learning Technologies, 1–13. https://doi.org/10.1109/tlt.2025.3568599
• Törmänen, T., Saqr, M., López-Pernas, S., Mänty, K., Suoraniemi, J., Heikkala, N., & Järvenoja, H. (2025). Emotional dynamics and regulation in collaborative learning. Learning and Instruction, 100, 102188. https://doi.org/10.1016/j.learninstruc.2025.102188
• López-Pernas, S., Misiejuk, K., Oliveira, E., & Saqr, M. (2025). The dynamics of the self-regulation process in student-AI interactions: The case of problem-solving in programming education. Proceedings of the 25th Koli Calling International Conference on Computing Education Research, 1–12. https://doi.org/10.1145/3769994.3770043
• Misiejuk, K., Kaliisa, R., Lopez-Pernas, S., & Saqr, M. (2026). Expanding the Quantitative Ethnography Toolkit with Transition Network Analysis: Exploring Methodological Synergies and Boundaries. In Advances in Quantitative Ethnography. ICQE 2025, CCIS vol. 2677. Springer. https://doi.org/10.1007/978-3-032-12229-2_10
• Lopez-Pernas, S., Misiejuk, K., Tikka, S., & Saqr, M. (2026). Role Dynamics in Student-AI Collaboration: A Heterogeneous Transition Network Analysis Approach. SSRN preprint. https://doi.org/10.2139/ssrn.6082190