Package index • tna

Basic functions

tna-package: The tna package.

build_model() tna() ftna() ctna(): Build a Transition Network Analysis Model

print(<tna>): Print a tna object

plot(<tna>): Plot a Transition Network Analysis Model

prepare_data(): Compute User Sessions from Event Data

print(<tna_data>): Print a TNA data object

summary(<tna>): Calculate Summary of Network Metrics for a Transition Network

hist(<tna>): Plot a Histogram of Edge Weights in the Network

print(<summary.tna>): Print a TNA Summary

plot_mosaic(): Create a Mosaic Plot of Transitions or Events

plot_mosaic(<tna_data>): Plot state frequencies as a mosaic between two groups

print(<group_tna>): Print a group_tna Object

plot(<group_tna>): Plot a grouped Transition Network Analysis Model

summary(<group_tna>): Calculate Summary of Network Metrics for a grouped Transition Network

hist(<group_tna>): Plot a Histogram of Edge Weights for a group_tna Object.

plot_mosaic(<group_tna>): Plot state frequencies as a mosaic between two groups

print(<summary.group_tna>): Print the summary of a grouped Transition Network Analysis Model

Centralities

centralities(): Calculate Centrality Measures for a Transition Matrix

betweenness_network(): Build and Visualize a Network with Edge Betweenness

print(<tna_centralities>): Print Centrality Measures

plot(<tna_centralities>): Plot Centrality Measures

print(<group_tna_centralities>): Print Centrality Measures

plot(<group_tna_centralities>): Plot Centrality Measures

Communities

communities(): Community Detection for Transition Networks

print(<tna_communities>): Print Detected Communities

plot(<tna_communities>): Plot Communities

print(<group_tna_communities>): Print Detected Communities

plot(<group_tna_communities>): Plot Found Communities

Cliques

cliques(): Identify Cliques in a Transition Network

print(<tna_cliques>): Print Found Cliques of a TNA Network

plot(<tna_cliques>): Plot Cliques of a TNA Network

print(<group_tna_cliques>): Print Found Cliques

plot(<group_tna_cliques>): Plot Found Cliques

Comparison

compare(): Compare Two Matrices or TNA Models with Comprehensive Metrics

compare(<group_tna>): Compare TNA Clusters with Comprehensive Metrics

print(<tna_comparison>): Print Comparison Results

plot_compare(): Plot the difference network between two models

plot_compare(<group_tna>): Plot the difference network between two clusters

plot(<tna_comparison>): Plot the results of comparing two tna models or matrices

Validation

permutation_test(): Compare Two Networks from Sequence Data Using Permutation Tests

print(<tna_permutation>): Print Permutation Test Results

plot(<tna_permutation>): Plot the Significant Differences from a Permutation Test

print(<tna_bootstrap>): Print Bootstrap Results

print(<summary.tna_bootstrap>): Print a Bootstrap Summary

print(<summary.group_tna_bootstrap>): Print Bootstrap Summary for a Grouped Transition Network

print(<tna_stability>): Print Centrality Stability Results

plot(<tna_stability>): Plot Centrality Stability Results

print(<group_tna_bootstrap>): Print group_tna Bootstrap Results

print(<group_tna_stability>): Print Centrality Stability Results

plot(<group_tna_stability>): Plot Centrality Stability Results

estimate_cs() estimate_centrality_stability(): Estimate Centrality Stability

prune(): Prune a tna network based on transition probabilities

deprune() reprune(<tna>): Restore a Pruned Transition Network Analysis Model

reprune(): Restore Previous Pruning of a Transition Network Analysis Model

pruning_details(): Print Detailed Information on the Pruning Results

bootstrap(): Bootstrap Transition Networks from Sequence Data

summary(<tna_bootstrap>): Summarize Bootstrap Results

summary(<group_tna_bootstrap>): Summarize Bootstrap Results for a Grouped Transition Network

Clusters

group_model() group_tna() group_ftna() group_ctna(): Build a grouped Transition Network Analysis Model

rename_groups(): Rename Clusters

mmm_stats(): Retrieve statistics from a mixture Markov model (MMM)

Helpers

as.igraph(<group_tna>): Coerce a specific group from a group_tna object into an igraph object.

as.igraph(<matrix>): Coerce a weight matrix into an igraph object.

as.igraph(<tna>): Coerce a tna object into an igraph object.

Datasets

engagement: Example data on student engagement

engagement_mmm: Example mixed Markov model fitted to the engagement data

group_regulation: Example data on group regulation