README.md
In tna: Transition Network Analysis (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.

Check out our tutorials:

You can also try our Shiny app.

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")

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