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tna is an R package for the analysis of relational dynamics throughTransition Network Analysis (TNA). TNA provides tools for building TNAmodels, plotting transition networks, calculating centrality measures,and identifying dominant events and patterns. TNA statistical techniques(e.g., bootstrapping and permutation tests) ensure the reliability ofobserved insights and confirm that identified dynamics are meaningful.See(Saqr et al., 2025) formore details on TNA.

Check out our tutorials:

• Basics ofTNA
• Frequency-basedTNA
• Clustering

You can also try ourShinyapp.

You can install the most recent stable version oftna fromCRAN or the developmentversion fromGitHub by running one of thefollowing:

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)

Plot the transition network

# Default plotplot(tna_model)

# Optimized plotplot(tna_model,cut=0.2,minimum=0.05,edge.label.position=0.8,edge.label.cex=0.7)

cent<- centralities(tna_model)

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)