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

Check out our tutorials:

• Basics of TNA
• Frequency-based TNA
• Clustering

You can also try our Shiny app.

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	13.406	2.333	18.546	17.000	1.000	5.586	0.337
cohesion	0.973	0.812	3.651	2.791	13.813	0.000	19.000	5.209	0.300
consensus	0.918	2.667	0.798	4.344	11.481	0.000	103.000	4.660	0.161
coregulate	0.977	0.567	4.547	2.309	5.974	5.000	27.000	5.148	0.306
discuss	0.805	1.188	1.954	2.681	7.308	0.000	53.000	4.628	0.240
emotion	0.923	0.894	1.568	3.133	14.539	0.000	36.000	5.070	0.290
monitor	0.982	0.346	6.243	2.210	7.757	3.000	11.000	5.157	0.289
plan	0.626	1.194	5.475	2.914	17.593	10.000	61.000	3.488	0.287
synthesis	1.000	0.192	12.271	2.184	15.901	14.000	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         0.7

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	-6.7273879	0.000999001
cohesion	InStrength	0.01634987	0.3341493	0.715284715
consensus	InStrength	0.53680793	7.1591129	0.000999001
coregulate	InStrength	-0.25275371	-7.5223301	0.000999001
discuss	InStrength	-0.09009651	-1.9049329	0.051948052
emotion	InStrength	0.19288376	4.0605067	0.000999001
monitor	InStrength	-0.09192991	-3.3634217	0.001998002
plan	InStrength	0.12225988	2.7325892	0.006993007
synthesis	InStrength	-0.04909607	-3.1813886	0.000999001