tna: An R package for Transition Network Analysis

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 library

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	1.0000000	0.3445778	13.406025	2.333065	18.546309	17	1	5.586292	0.3369839
cohesion	0.9728614	0.8116478	3.651321	2.791075	13.812739	0	19	5.208633	0.2996487
consensus	0.9179965	2.6672185	0.797502	4.344062	11.481098	0	103	4.659728	0.1607773
coregulate	0.9766497	0.5665811	4.546876	2.308867	5.973798	5	27	5.147938	0.3057842
discuss	0.8051126	1.1882315	1.954285	2.680996	7.308377	0	53	4.627577	0.2397108
emotion	0.9231583	0.8941312	1.568289	3.132785	14.538635	0	36	5.069888	0.2904793
monitor	0.9818562	0.3457150	6.243045	2.210039	7.757314	3	11	5.156837	0.2888819
plan	0.6257918	1.1937843	5.474533	2.913798	17.593057	10	61	3.487529	0.2874904
synthesis	1.0000000	0.1915394	12.270650	2.183927	15.900927	14	3	5.582502	0.3586136

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	-6.8131390	0.000
cohesion	InStrength	0.01634987	0.3218349	0.751
consensus	InStrength	0.53680793	7.5039077	0.000
coregulate	InStrength	-0.25275371	-7.4748752	0.000
discuss	InStrength	-0.09009651	-1.8726678	0.051
emotion	InStrength	0.19288376	4.0752947	0.000
monitor	InStrength	-0.09192991	-3.3529720	0.001
plan	InStrength	0.12225988	2.8214020	0.003
synthesis	InStrength	-0.04909607	-3.2129876	0.001