Package index • Nestimate

Network Estimation

Core functions for building networks from data

build_network() print(<netobject>) print(<netobject_group>) print(<netobject_ml>) summary(<netobject>) summary(<netobject_group>) print(<summary.netobject>) print(<summary.netobject_group>): Build a Network
estimate_network(): Estimate a Network (Deprecated)
register_estimator(): Register a Network Estimator
get_estimator(): Retrieve a Registered Estimator
list_estimators(): List All Registered Estimators
remove_estimator(): Remove a Registered Estimator
build_tna(): Build a Transition Network (TNA)
build_atna(): Build an Attention-Weighted Transition Network (ATNA)
build_ftna(): Build a Frequency Transition Network (FTNA)
build_cna(): Build a Co-occurrence Network (CNA)
build_cor(): Build a Correlation Network
build_pcor(): Build a Partial Correlation Network
build_glasso(): Build a Graphical Lasso Network (EBICglasso)
build_ising(): Build an Ising Network
wtna() print(<wtna_mixed>): Window-based Transition Network Analysis
cooccurrence(): Build a Co-occurrence Network
build_mlvar() print(<net_mlvar>) summary(<net_mlvar>): Build a Multilevel Vector Autoregression (mlVAR) network
build_gimme(): GIMME: Group Iterative Multiple Model Estimation

Bayesian Inference

Dirichlet-Multinomial posterior inference for transition networks. certainty() is the closed-form counterpart of bootstrap_network(); bayes_compare() is the complement of permutation().

certainty() print(<net_certainty>): Analytic certainty of network edges (Bayesian Dirichlet-Multinomial)
bayes_compare() print(<net_bayes>) summary(<net_bayes>) plot(<net_bayes>) print(<net_bayes_group>) summary(<net_bayes_group>): Bayesian Dirichlet-Multinomial comparison of two transition networks
subtract_networks() print(<netdifference>): Subtract one network from another
as_netdifference(): Coerce an inferential comparison to a network difference

Higher-Order Networks

Methods for capturing higher-order dependencies

build_hon() print(<net_hon>) summary(<net_hon>): Build a Higher-Order Network (HON)
build_honem() print(<net_honem>) summary(<net_honem>) plot(<net_honem>): Build HONEM Embeddings for Higher-Order Networks
build_hypa() print(<net_hypa>) summary(<net_hypa>): Detect Path Anomalies via HYPA
build_mogen() print(<net_mogen>) summary(<net_mogen>) plot(<net_mogen>): Build Multi-Order Generative Model (MOGen)
pathways(): Extract Pathways from Higher-Order Network Objects
mogen_transitions(): Extract Transition Table from a MOGen Model
path_counts(): Count Path Frequencies in Trajectory Data
bipartite_groups(): Hypergraph from bipartite group / event data
clique_expansion(): Clique expansion of a hypergraph
hypergraph_centrality(): Hypergraph eigenvector centralities
build_hypergraph() print(<net_hypergraph>) summary(<net_hypergraph>): Higher-order hypergraph from a network's clique structure
hypergraph_measures() print(<hypergraph_measures>): Structural measures for a hypergraph
hypergraph_cluster() print(<net_hypergraph_cluster>) summary(<net_hypergraph_cluster>) as.data.frame(<net_hypergraph_cluster>) plot(<net_hypergraph_cluster>): Spectral clustering of hypergraph vertices
hypergraph_transduction() print(<net_hypergraph_transduction>) summary(<net_hypergraph_transduction>) as.data.frame(<net_hypergraph_transduction>) plot(<net_hypergraph_transduction>): Transductive label spreading on a hypergraph
hypergraph_laplacian(): Normalized hypergraph Laplacian

Markov Analysis

Order, structure, entropy, and stability of Markov chains

chain_structure() print(<chain_structure>) plot(<chain_structure>) summary(<chain_structure>) print(<chain_structure_group>) summary(<chain_structure_group>) print(<summary_chain_structure>): Qualitative structure of a discrete-time Markov chain
markov_order_test() print(<net_markov_order>) print(<net_markov_order_group>) summary(<net_markov_order>) plot(<net_markov_order>): Test the Markov order of a sequential process
markov_stability() print(<net_markov_stability>) print(<net_markov_stability_group>) summary(<net_markov_stability>) plot(<net_markov_stability>): Markov Stability Analysis
passage_time() print(<net_mpt>) print(<net_mpt_group>) summary(<net_mpt>) print(<summary.net_mpt>) plot(<net_mpt>): Mean First Passage Times
path_dependence() print(<net_path_dependence>) summary(<net_path_dependence>) print(<summary.net_path_dependence>) plot(<net_path_dependence>): Per-Context Path Dependence at Order k
transition_entropy() print(<net_transition_entropy>) print(<net_transition_entropy_group>) summary(<net_transition_entropy>) print(<summary.net_transition_entropy>) plot(<net_transition_entropy>): Transition Entropy of a Markov Chain
entropy_network(): Transition Entropy Network
entropy_trajectory() print(<net_entropy_trajectory>) summary(<net_entropy_trajectory>) plot(<net_entropy_trajectory>): Sliding-Window Transition Entropy Trajectory
entropy_bayes() print(<net_entropy_bayes>) print(<net_entropy_bayes_group>) summary(<net_entropy_bayes>) plot(<net_entropy_bayes>): Bayesian Transition Entropy

Network Pruning

Non-destructive edge pruning and restoration

net_prune(): Prune a Network's Edges
net_deprune(): Undo Network Pruning
net_reprune(): Re-apply Network Pruning
net_pruning_details() print(<net_pruning_details>): Report Network Pruning Details

Bootstrap & Inference

Statistical inference for network estimation

bootstrap_network() print(<net_bootstrap>) summary(<net_bootstrap>) print(<net_bootstrap_group>) summary(<net_bootstrap_group>) print(<wtna_boot_mixed>) summary(<wtna_boot_mixed>): Bootstrap a Network Estimate
vertex_bootstrap() print(<net_vertex_bootstrap>) summary(<net_vertex_bootstrap>) plot(<net_vertex_bootstrap>): Vertex Bootstrap for Network-Level Statistics
vertex_compare() print(<net_vertex_comparison>) summary(<net_vertex_comparison>) plot(<net_vertex_comparison>): Compare Network-Level Statistics of Two Networks
boot_glasso() print(<boot_glasso>) summary(<boot_glasso>) plot(<boot_glasso>): Bootstrap for Regularized Partial Correlation Networks
permutation() print(<net_permutation>) summary(<net_permutation>) print(<net_permutation_group>) summary(<net_permutation_group>) print(<wtna_perm_mixed>) summary(<wtna_perm_mixed>): Permutation Test for Network Comparison
permutation_diagnostics(): Does Nesting Bias a Permutation Test?
nct() print(<net_nct>) summary(<net_nct>): Network Comparison Test
compare_model() print(<net_comparison>) plot(<net_comparison>): Compare two networks descriptively
compare_networks() print(<net_network_comparison>) plot(<net_network_comparison>): Compare two or more networks
summary(<net_network_comparison>) edge_differences() node_differences() global_differences() network_metrics() print(<net_table>): Tables of a network comparison
rename_models(): Rename the models of a netobject_group
magnitude_difference() print(<magnitude_difference>) plot(<magnitude_difference>): Magnitude difference between the frequency and probability views

Reliability & Stability

Assess reliability and stability of network estimates

network_reliability() print(<net_reliability>) summary(<net_reliability>) plot(<net_reliability>): Split-Half Reliability for Network Estimates
casedrop_reliability() print(<net_casedrop_reliability>) summary(<net_casedrop_reliability>) print(<net_casedrop_reliability_group>) summary(<net_casedrop_reliability_group>) print(<summary.net_casedrop_reliability_group>) plot(<net_casedrop_reliability>) plot(<net_casedrop_reliability_group>): Edge-weight Case-dropping Stability
centrality_stability() print(<net_stability>) print(<net_stability_group>) summary(<net_stability_group>) summary(<net_stability>) plot(<net_stability>): Centrality Stability Coefficient (CS-coefficient)
loading_stability() print(<pc_loading_stability>) plot(<pc_loading_stability>): Composite-Weight Stability Under Case Resampling

Clustering & Grouping

Cluster-based and multilevel network analysis

build_clusters() print(<net_clustering>) summary(<net_clustering>) plot(<net_clustering>) print(<tidy_covariates>): Cluster Sequences by Dissimilarity
cluster_data(): Cluster sequence data (deprecated alias)
cluster_choice() print(<cluster_choice>) summary(<cluster_choice>) plot(<cluster_choice>): Cluster Choice – sweep k, dissimilarity and method
cluster_diagnostics() print(<net_cluster_diagnostics>) plot(<net_cluster_diagnostics>) as.data.frame(<net_cluster_diagnostics>): Cluster Diagnostics
cluster_summary(): Cluster Summary Statistics
cluster_mmm(): Cluster sequences using Mixed Markov Models
cluster_network(): Cluster data and build per-cluster networks in one step
build_mcml() print(<mcml_layer>) print(<mcml>) summary(<mcml>): Build MCML from Raw Transition Data
build_mcml_pc() print(<mcml_pc>) summary(<mcml_pc>) plot(<mcml_pc>): Multi-Cluster Multi-Level Aggregation for Psychometric Networks
composites(): Cluster Scores From a Psychometric MCML Fit
item_loadings(): Item Diagnostics From a Psychometric MCML Fit
macro_network(): Cluster-Level Network, With One Cluster Expanded
build_mmm() print(<net_mmm>) summary(<net_mmm>) plot(<net_mmm>) print(<net_mmm_clustering>) plot(<net_mmm_clustering>): Fit a Mixed Markov Model
compare_mmm() print(<mmm_compare>) summary(<mmm_compare>) plot(<mmm_compare>): Compare MMM fits across different k
session_ids(): The session behind each sequence

Simplicial Complex Analysis

Topological analysis of networks

build_simplicial() print(<simplicial_complex>) plot(<simplicial_complex>): Build a Simplicial Complex
simplicial_features(): Tidy Topological Features for One or Many Networks
persistent_homology() print(<persistent_homology>) plot(<persistent_homology>): Persistent Homology
bottleneck_distance(): Bottleneck Distance Between Persistence Diagrams
persistence_landscape() print(<persistence_landscape>) plot(<persistence_landscape>): Persistence Landscape
q_analysis() print(<q_analysis>) plot(<q_analysis>): Q-Analysis
betti_numbers(): Betti Numbers
euler_characteristic(): Euler Characteristic
simplicial_degree(): Simplicial Degree
verify_simplicial(): Verify Simplicial Complex Against igraph

Data Preparation

Convert and prepare data for network estimation

prepare() print(<nestimate_data>): Prepare Event Log Data for Network Estimation
prepare_for_tna(): Prepare Data for TNA Analysis
action_to_onehot(): Convert Action Column to One-Hot Encoding
prepare_onehot(): Import One-Hot Encoded Data into Sequence Format
wide_to_long(): Convert Wide Sequences to Long Format
long_to_wide(): Convert Long Format to Wide Sequences
convert_sequence_format(): Convert Sequence Data to Different Formats
actor_endpoints(): Tidy per-actor endpoint summary of a wide-format sequence dataset
mark_first_state(): Mark leading-NA cells with an explicit state label
mark_terminal_state(): Mark terminal-NA cells with an explicit state label

Utilities

Helper functions and extractors

predictability(): Compute Node Predictability
frequencies() summary(<nest_transition_counts>): Build a Transition Frequency Matrix
state_frequencies(): Compute State Frequencies from Trajectory Data
net_aggregate_weights(): Aggregate Edge Weights
net_centrality() plot(<net_centrality>) plot(<net_centrality_group>): Compute Centrality Measures for a Network
net_edge_betweenness() plot(<net_edge_betweenness>): Edge Betweenness Network
coefs(): Tidy coefficients from a fitted mlvar model
as_tna(): Promote the Layers of an mcml to Networks
as_htna(): Build a grouped node-level network (htna) from data and a clustering
as_networks(): Promote a psychometric MCML result to a network group
as_netobject(): Coerce a network object to a Nestimate netobject
validate_netobject(): Validate a netobject / cograph_network against the shared schema
extract_edges(): Extract Edge List with Weights
extract_initial_probs() summary(<nest_initial_probs>): Extract Initial Probabilities from Model
extract_transition_matrix() summary(<nest_transition_matrix>): Extract Transition Matrix from Model
state_colors(): The state colours an object will draw with
set_state_colors() `state_colors<-`(): Set the state colours carried by a network object

Sequence Analysis

Sequence visualization, pattern comparison, and association mining

sequence_plot() print(<mcml_sequence_plot>): Sequence Plot (heatmap, index, or distribution)
distribution_plot(): State Distribution Plot Over Time
plot_state_frequencies() print(<nestimate_facet_plot>) print(<nestimate_facet_list>) print(<state_freq>) plot(<state_freq>) as.data.frame(<state_freq>): Plot State Frequency Distributions
state_distribution(): Per-Class State Distribution as a Tidy Data Frame
plot_mosaic(): Draw a Marimekko / Mosaic Plot from a Tidy Data Frame
mosaic_plot(): Mosaic Plot of a Network's Transition or Co-occurrence Counts
mosaic_analysis() plot(<mosaic_analysis>) print(<mosaic_analysis>) summary(<mosaic_analysis>): Two-variable mosaic analysis (chi-square test + flat mosaic)
sequence_compare() print(<net_sequence_comparison>) summary(<net_sequence_comparison>) plot(<net_sequence_comparison>): Compare Subsequence Patterns Between Groups
extract_pathways(): Cut an Event Log into Pathways
association_rules() print(<net_association_rules>) summary(<net_association_rules>) plot(<net_association_rules>): Discover Association Rules from Sequential or Transaction Data

Link Prediction

Predict and evaluate missing connections

predict_links() print(<net_link_prediction>) summary(<net_link_prediction>): Predict Missing or Future Links in a Network
evaluate_links(): Evaluate Link Predictions Against Known Edges

Outcome Modelling

Relate network and sequence features to an outcome

outcome_model() print(<net_outcome_model>) summary(<net_outcome_model>) plot(<net_outcome_model>): Model Unit-Level Outcomes from Sequence or Network Predictors
effects_table(): Effect Table of a Fitted Outcome Model

Data

Example datasets

human_long ai_long: Human-AI Vibe Coding Interaction Data (Long Format)
srl_strategies: Self-Regulated Learning Strategy Frequencies
learning_activities: Online Learning Activity Indicators
group_regulation_long: Group Regulation in Collaborative Learning (Long Format)
chatgpt_srl: ChatGPT Self-Regulated Learning Scale Scores
trajectories: Student Engagement Trajectories