Package index • Nestimate

Network Estimation

Core functions for building networks from data

build_network(): 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(): Window-based Transition Network Analysis
cooccurrence(): Build a Co-occurrence Network
build_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(): Analytic certainty of network edges (Bayesian Dirichlet-Multinomial)
bayes_compare(): 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(): Build a Higher-Order Network (HON)
build_honem(): Build HONEM Embeddings for Higher-Order Networks
build_hypa(): Detect Path Anomalies via HYPA
build_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

Markov Analysis

Order, structure, entropy, and stability of Markov chains

chain_structure(): Qualitative structure of a discrete-time Markov chain
markov_order_test(): Test the Markov order of a sequential process
markov_stability() plot(<net_markov_stability>): Markov Stability Analysis
passage_time() summary(<net_mpt>) plot(<net_mpt>): Mean First Passage Times
path_dependence(): Per-Context Path Dependence at Order k
transition_entropy(): Transition Entropy of a Markov Chain

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(): Report Network Pruning Details

Bootstrap & Inference

Statistical inference for network estimation

bootstrap_network(): Bootstrap a Network Estimate
vertex_bootstrap(): Vertex Bootstrap for Network-Level Statistics
vertex_compare(): Compare Network-Level Statistics of Two Networks
boot_glasso(): Bootstrap for Regularized Partial Correlation Networks
permutation(): Permutation Test for Network Comparison
nct(): Network Comparison Test
compare_model(): Compare two networks descriptively
compare_model(<netobject_group>): Compare two networks within a netobject_group
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(): 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(): Centrality Stability Coefficient (CS-coefficient)
loading_stability(): Composite-Weight Stability Under Case Resampling

Clustering & Grouping

Cluster-based and multilevel network analysis

build_clusters(): Cluster Sequences by Dissimilarity
cluster_data(): Cluster sequence data (deprecated alias)
cluster_choice(): Cluster Choice – sweep k, dissimilarity and method
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(): Build MCML from Raw Transition Data
build_mcml_pc(): Multi-Cluster Multi-Level Aggregation for Psychometric Networks
build_mmm(): Fit a Mixed Markov Model
compare_mmm(): Compare MMM fits across different k

Simplicial Complex Analysis

Topological analysis of networks

build_simplicial(): Build a Simplicial Complex
persistent_homology(): Persistent Homology
bottleneck_distance(): Bottleneck Distance Between Persistence Diagrams
persistence_landscape(): Persistence Landscape
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(): 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(): Sequence Data Conversion Functions
state_frequencies(): Compute State Frequencies from Trajectory Data
net_aggregate_weights(): Aggregate Edge Weights
net_centrality(): Compute Centrality Measures for a Network
net_edge_betweenness(): Edge Betweenness Network
coefs(): Tidy coefficients from a fitted mlvar model
as_tna(): Convert cluster_summary to tna Objects
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(): Extract Initial Probabilities from Model
extract_transition_matrix(): Extract Transition Matrix from Model

Sequence Analysis

Sequence visualization, pattern comparison, and association mining

sequence_plot(): Sequence Plot (heatmap, index, or distribution)
distribution_plot(): State Distribution Plot Over Time
plot_state_frequencies(): 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(): Two-variable mosaic analysis (chi-square test + flat mosaic)
sequence_compare(): Compare Subsequence Patterns Between Groups
association_rules(): Discover Association Rules from Sequential or Transaction Data

Link Prediction

Predict and evaluate missing connections

predict_links(): Predict Missing or Future Links in a Network
evaluate_links(): Evaluate Link Predictions Against Known Edges

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

S3 Methods

Print, summary, and plot methods for package classes

build_hypergraph() print(<net_hypergraph>) summary(<net_hypergraph>): Higher-order hypergraph from a network's clique structure
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
hypergraph_measures() print(<hypergraph_measures>): Structural measures for a hypergraph
magnitude_difference() print(<magnitude_difference>) plot(<magnitude_difference>): Magnitude difference between the frequency and probability views
print(<boot_glasso>): Print Method for boot_glasso
print(<chain_structure>): Print method for chain_structure
print(<chain_structure_group>): Print method for chain_structure_group
print(<cluster_choice>): Print Method for cluster_choice
print(<mcml>): Print Method for mcml
print(<mcml_layer>): Print Method for an mcml Layer
print(<mcml_pc>): Print an MCML-PC Object
print(<mmm_compare>): Print Method for mmm_compare
print(<mosaic_analysis>): Print method for mosaic_analysis objects
print(<nestimate_data>): Print Method for nestimate_data
print(<net_association_rules>): Print Method for net_association_rules
print(<net_bayes>): Print method for net_bayes
print(<net_bayes_group>): Print method for net_bayes_group
print(<net_bootstrap>): Print Method for net_bootstrap
print(<net_bootstrap_group>): Print Method for net_bootstrap_group
print(<net_certainty>): Print Method for net_certainty
print(<net_cluster_diagnostics>): Print Method for net_cluster_diagnostics
print(<net_clustering>): Print Method for net_clustering
print(<net_gimme>): Print Method for net_gimme
print(<net_hon>): Print Method for net_hon
print(<net_honem>): Print Method for net_honem
print(<net_hypa>): Print Method for net_hypa
print(<net_link_prediction>): Print Method for net_link_prediction
print(<net_markov_order>): Print Method for net_markov_order
print(<net_markov_order_group>): Print method for net_markov_order_group
print(<net_markov_stability_group>): Print method for net_markov_stability_group
print(<net_mlvar>): Print method for net_mlvar
print(<net_mmm>): Print Method for net_mmm
print(<net_mmm_clustering>): Print Method for MMM Clustering Attribute
print(<net_mogen>): Print Method for net_mogen
print(<net_mpt_group>): Print method for net_mpt_group
print(<net_nct>): Print Method for net_nct
print(<net_path_dependence>): Print method for net_path_dependence
print(<net_permutation>): Print Method for net_permutation
print(<net_permutation_group>): Print Method for net_permutation_group
print(<net_pruning_details>): Print method for pruning details
print(<net_reliability>): Print Method for net_reliability
print(<net_sequence_comparison>): Print Method for net_sequence_comparison
print(<net_stability>): Print Method for net_stability
print(<net_stability_group>): Print Method for net_stability_group
print(<net_transition_entropy>): Print method for net_transition_entropy
print(<net_transition_entropy_group>): Print method for net_transition_entropy_group
print(<net_vertex_bootstrap>): Print a Vertex Bootstrap Result
print(<net_vertex_comparison>): Print a Two-Network Vertex-Bootstrap Comparison
print(<netobject>): Print Method for Network Object
print(<netobject_group>): Print Method for Group Network Object
print(<netobject_ml>): Print Method for Multilevel Network Object
print(<pc_loading_stability>): Print Composite-Weight Stability
print(<persistence_landscape>): Print Persistence Landscape
print(<persistent_homology>): Print persistent homology results
print(<q_analysis>): Print Q-analysis results
print(<simplicial_complex>): Print a simplicial complex
print(<summary.net_path_dependence>): Print method for summary.net_path_dependence
print(<summary.net_transition_entropy>): Print method for summary.net_transition_entropy
print(<summary_chain_structure>): Print method for summary.chain_structure
print(<tidy_covariates>): Print method for tidy covariate output
print(<wtna_boot_mixed>): Print Method for wtna_boot_mixed
print(<wtna_mixed>): Print Method for wtna_mixed
print(<wtna_perm_mixed>): Print Method for wtna_perm_mixed
print(<state_freq>) plot(<state_freq>) as.data.frame(<state_freq>): Print, Plot, and Convert a state_freq Object
subtract_networks() print(<netdifference>): Subtract one network from another
passage_time() summary(<net_mpt>) plot(<net_mpt>): Mean First Passage Times
summary(<boot_glasso>): Summary Method for boot_glasso
summary(<chain_structure>): Tidy per-state summary of a chain_structure
summary(<chain_structure_group>): Cross-group comparison of chain_structure_group
summary(<cluster_choice>): Summary Method for cluster_choice
summary(<mcml>): Summary Method for mcml
summary(<mcml_pc>): Summarize an MCML-PC Object
summary(<mmm_compare>): Summary Method for mmm_compare
summary(<mosaic_analysis>): Summary method for mosaic_analysis objects
summary(<nest_initial_probs>): Summary Method for Initial Probability Vectors
summary(<nest_transition_counts>): Summary Method for Transition Count Matrices
summary(<nest_transition_matrix>): Summary Method for Transition Matrices
summary(<net_association_rules>): Summary Method for net_association_rules
summary(<net_bayes>): Summary method for net_bayes
summary(<net_bayes_group>): Summary method for net_bayes_group
summary(<net_bootstrap>): Summary Method for net_bootstrap
summary(<net_bootstrap_group>): Summary Method for net_bootstrap_group
summary(<net_clustering>): Summary Method for net_clustering
summary(<net_gimme>): Summary Method for net_gimme
summary(<net_hon>): Summary Method for net_hon
summary(<net_honem>): Summary Method for net_honem
summary(<net_hypa>): Summary Method for net_hypa
summary(<net_link_prediction>): Summary Method for net_link_prediction
summary(<net_markov_order>): Summary Method for net_markov_order
summary(<net_mlvar>): Summary method for net_mlvar
summary(<net_mmm>): Summary Method for net_mmm
summary(<net_mogen>): Summary Method for net_mogen
summary(<net_nct>): Summary Method for net_nct
summary(<net_path_dependence>): Summary method for net_path_dependence
summary(<net_permutation>): Summary Method for net_permutation
summary(<net_permutation_group>): Summary Method for net_permutation_group
summary(<net_reliability>): Summary Method for net_reliability
summary(<net_sequence_comparison>): Summary Method for net_sequence_comparison
summary(<net_stability>): Summary Method for net_stability
summary(<net_stability_group>): Summary Method for net_stability_group
summary(<net_transition_entropy>): Summary method for net_transition_entropy
summary(<net_vertex_bootstrap>): Summarize a Vertex Bootstrap Result
summary(<net_vertex_comparison>): Summarize a Two-Network Vertex-Bootstrap Comparison
summary(<netobject>): Network metrics for a netobject
summary(<netobject_group>): Network metrics for a netobject_group
summary(<wtna_boot_mixed>): Summary Method for wtna_boot_mixed
summary(<wtna_perm_mixed>): Summary Method for wtna_perm_mixed
markov_stability() plot(<net_markov_stability>): Markov Stability Analysis
plot(<boot_glasso>): Plot Method for boot_glasso
plot(<chain_structure>): Plot method for chain_structure
plot(<cluster_choice>): Plot Method for cluster_choice
plot(<mcml_pc>): Plot an MCML-PC Object
plot(<mmm_compare>): Plot Method for mmm_compare
plot(<mosaic_analysis>): Plot method for mosaic_analysis objects
plot(<net_association_rules>): Plot Method for net_association_rules
plot(<net_bayes>): Plot method for net_bayes
plot(<net_centrality>): Plot centrality measures
plot(<net_centrality_group>): Plot grouped centrality measures
plot(<net_cluster_diagnostics>): Plot Method for net_cluster_diagnostics
plot(<net_clustering>): Plot Sequence Clustering Results
plot(<net_comparison>): Plot a network comparison
plot(<net_edge_betweenness>): Plot edge-betweenness scores
plot(<net_gimme>): Plot Method for net_gimme
plot(<net_honem>): Plot Method for net_honem
plot(<net_markov_order>): Plot Method for net_markov_order
plot(<net_mmm>): Plot Method for net_mmm
plot(<net_mmm_clustering>): Plot Method for MMM Clustering Attribute
plot(<net_mogen>): Plot Method for net_mogen
plot(<net_path_dependence>): Plot method for net_path_dependence
plot(<net_reliability>): Plot Method for net_reliability
plot(<net_sequence_comparison>): Plot Method for net_sequence_comparison
plot(<net_stability>): Plot Method for net_stability
plot(<net_transition_entropy>): Plot method for net_transition_entropy
plot(<net_vertex_bootstrap>): Plot Vertex Bootstrap Distributions
plot(<net_vertex_comparison>): Plot a Two-Network Vertex-Bootstrap Comparison
plot(<pc_loading_stability>): Plot Composite-Weight Stability
plot(<persistence_landscape>): Plot Persistence Landscape
plot(<persistent_homology>): Plot Persistent Homology
plot(<q_analysis>): Plot Q-Analysis
plot(<simplicial_complex>): Plot a Simplicial Complex
plot_mosaic(): Draw a Marimekko / Mosaic Plot from a Tidy Data Frame
plot_state_frequencies(): Plot State Frequency Distributions