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tna: An R package for Transition Network Analysis

Project Status: Active – The project has reached a stable, usable state and is being actively developed. R-CMD-check Codecov test coverage tna CRAN badge License: MIT

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:

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 -6.6808853 0.000999001
cohesion InStrength 0.01634987 0.3255056 0.749250749
consensus InStrength 0.53680793 7.4741341 0.000999001
coregulate InStrength -0.25275371 -7.4623409 0.000999001
discuss InStrength -0.09009651 -1.8001285 0.076923077
emotion InStrength 0.19288376 4.0066931 0.000999001
monitor InStrength -0.09192991 -3.3380625 0.001998002
plan InStrength 0.12225988 2.6970534 0.009990010
synthesis InStrength -0.04909607 -3.2554851 0.002997003

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.