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)

metric	value
Node Count	9.00
Edge Count	78.00
Network Density	1.00
Mean Distance	0.05
Mean Out-Strength	1.00
SD Out-Strength	0.81
Mean In-Strength	1.00
SD In-Strength	0.00
Mean Out-Degree	8.67
SD Out-Degree	0.71
Centralization (Out-Degree)	0.02
Centralization (In-Degree)	0.02
Reciprocity	0.99

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
)

Calculate the centrality measures

cent <- centralities(tna_model)

state	OutStrength	InStrength	ClosenessIn	ClosenessOut	Closeness	Betweenness	BetweennessRSP	Diffusion	Clustering
adapt	1.000	0.345	0.008	0.015	0.025	1.000	1.000	5.586	0.337
cohesion	0.973	0.812	0.014	0.012	0.027	0.000	19.000	5.209	0.300
consensus	0.918	2.667	0.035	0.013	0.038	30.000	103.000	4.660	0.161
coregulate	0.977	0.567	0.016	0.015	0.021	0.000	27.000	5.148	0.306
discuss	0.805	1.188	0.020	0.013	0.027	16.000	53.000	4.628	0.240
emotion	0.923	0.894	0.014	0.012	0.023	5.000	36.000	5.070	0.290
monitor	0.982	0.346	0.008	0.014	0.019	0.000	11.000	5.157	0.289
plan	0.626	1.194	0.027	0.012	0.027	9.000	61.000	3.488	0.287
synthesis	1.000	0.192	0.010	0.016	0.024	7.000	3.000	5.583	0.359

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)

state	centrality	diff_true	effect_size	p_value
adapt	InStrength	-0.23693341	-6.746110	0.000999001
cohesion	InStrength	0.01634987	0.345312	0.720279720
consensus	InStrength	0.53680793	7.777826	0.000999001
coregulate	InStrength	-0.25275371	-7.385802	0.000999001
discuss	InStrength	-0.09009651	-1.930958	0.046953047
emotion	InStrength	0.19288376	4.215793	0.000999001
monitor	InStrength	-0.09192991	-3.454281	0.000999001
plan	InStrength	0.12225988	2.745588	0.007992008
synthesis	InStrength	-0.04909607	-3.220131	0.002997003

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