Nestimate

Unified network estimation, analysis, and validation for behavioral, psychological, and panel data.

Nestimate is a comprehensive R package for estimating, validating, and comparing networks from behavioral sequence data, psychological scales, and longitudinal panel data. A single entry point — build_network() — dispatches to 13 built-in estimators. Every network type shares the same validation pipeline: bootstrap confidence intervals, permutation testing, split-half reliability, and centrality stability. The entire package has only 4 hard imports (ggplot2, glasso, data.table, cluster).

Installation

# From CRAN
install.packages("Nestimate")

# Development version
devtools::install_github("mohsaqr/Nestimate")

What Nestimate Covers

Area	Key Functions
Dynamic / Transition Networks	`build_network()`, `wtna()`, `cooccurrence()`
Psychological Networks	`build_network(method = "glasso/pcor/cor/ising/mgm")`
Multilevel VAR	`build_mlvar()`
Idiographic Networks	`build_gimme()`
Cluster & Group Networks	`build_clusters()`, `build_mmm()`, `build_mcml()`
Higher-Order Networks	`build_hon()`, `build_honem()`, `build_hypa()`, `build_mogen()`
Topological Analysis	`build_simplicial()`, `persistent_homology()`, `q_analysis()`
Sequence Visualization	`sequence_plot()`, `distribution_plot()`
Sequence Pattern Comparison	`sequence_compare()`
Association Mining	`association_rules()`
Link Prediction	`predict_links()`, `evaluate_links()`
Markov Chain Analysis	`markov_stability()`, `passage_time()`
Statistical Validation	`bootstrap_network()`, `permutation()`, `nct()`, `network_reliability()`, `centrality_stability()`

Dynamic Networks

All dynamic network methods use build_network(). Pass an event log with action, actor, and time columns — no preprocessing needed.

Estimation Methods

Method	Aliases	Description
`"relative"`	`"tna"`, `"transition"`	Transition probabilities (directed)
`"frequency"`	`"ftna"`, `"counts"`	Raw transition counts (directed)
`"attention"`	`"atna"`	Decay-weighted transitions emphasising recent events (directed)
`"co_occurrence"`	`"cna"`	Co-occurrence from sequential data (undirected)

library(Nestimate)
data(human_long)

net <- build_network(human_long, method = "tna",
                     action = "action", actor = "session_id", time = "time")

# Per-group networks in one call
group_nets <- build_network(human_long, method = "tna",
                            action = "action", actor = "session_id",
                            time = "time", group = "phase")

Window-Based TNA

wtna() builds networks from binary (one-hot) data using temporal windows — directed transitions between windows, undirected co-occurrence within windows, or a mixed network combining both.

data(learning_activities)
net_wtna  <- wtna(learning_activities, actor = "student",
                  method = "transition", type = "relative")
net_mixed <- wtna(learning_activities, actor = "student",
                  method = "both", type = "relative")

Co-occurrence Networks

cooccurrence() builds undirected co-occurrence networks from 6 input formats (delimited fields, long/bipartite, binary matrix, wide sequence, lists) with 8 similarity methods (Jaccard, cosine, association strength, Dice, and more).

# From a long-format data frame
net_co <- cooccurrence(human_long, field = "action", by = "session_id",
                       similarity = "jaccard", threshold = 0.1)

Psychological Networks

Method	Description
`"cor"`	Pearson correlations
`"pcor"`	Partial correlations (precision matrix inversion)
`"glasso"`	EBICglasso — sparse regularised partial correlations
`"ising"`	L1-regularised logistic regression for binary items
`"mgm"`	Mixed Graphical Model — continuous + categorical variables together

All implemented from scratch with no dependency on igraph, qgraph, or bootnet.

data(srl_strategies)
net_gl  <- build_network(srl_strategies, method = "glasso")
net_mgm <- build_network(mixed_data, method = "mgm")   # scales + demographics

predictability(net_gl)   # R-squared per node from network structure

Multilevel VAR

build_mlvar() estimates three networks simultaneously from ESM/EMA diary data — the three pillars of mlVAR analysis in a single function call:

Temporal (directed) — lagged fixed effects: what predicts what across time
Contemporaneous (undirected) — partial correlations of within-person residuals: what moves together right now
Between-subjects (undirected) — partial correlations of person means: who differs from whom

Machine-precision equivalence to mlVAR::mlVAR() validated across 25 real ESM datasets, runs 1.45× faster.

data(chatgpt_srl)
fit <- build_mlvar(chatgpt_srl, vars = c("planning", "monitoring", "evaluation"),
                   id = "id", day = "day", beep = "beep")

fit$temporal          # directed network of lagged effects
fit$contemporaneous   # undirected within-person partial correlations
fit$between           # undirected between-persons partial correlations
coefs(fit)            # tidy data.frame: beta, SE, t, p, CI for every edge

Idiographic Networks

build_gimme() estimates a separate network for each person using the Group Iterative Mean Estimation (GIMME) algorithm, then aggregates to a group-level picture. Use this when between-person heterogeneity matters and a single group network would average over meaningfully different individuals.

fit_g <- build_gimme(panel_data, vars = c("x1", "x2", "x3"), id = "id")
fit_g$group_network       # aggregated group-level paths
fit_g$individual_networks # one network per person

Cluster & Group Networks

Sequence Clustering

build_clusters() partitions sequences into k groups using pairwise distance matrices. Supports 9 distance metrics and 8 clustering algorithms. Both build_clusters() and build_mmm() results pass directly to build_network().

clust <- build_clusters(net, k = 3, dissimilarity = "hamming", method = "ward.D2")
plot(clust, type = "silhouette")
cluster_nets <- build_network(clust, method = "tna")

Mixed Markov Models

build_mmm() discovers latent subgroups of sequences that share similar transition dynamics via EM — without pre-labelling groups. BIC/AIC/ICL model selection via compare_mmm().

mmm   <- build_mmm(net, k = 3)
compare_mmm(net, k = 2:6)   # model selection plot + table
mmm_nets <- build_network(mmm, method = "tna")

MCML

build_mcml() decomposes a network into macro (between-cluster) and micro (within-cluster) layers when nodes belong to known groups.

clusters <- list(Metacognitive = c("Planning", "Monitoring"),
                 Cognitive     = c("Elaboration", "Organisation"))
mcml <- cluster_summary(net, clusters)
mcml$macro$weights

Higher-Order Networks

Capture dependencies beyond first-order transitions:

Function	What it finds
`build_hon()`	Variable-length memory paths
`build_honem()`	Higher-order network embedding
`build_hypa()`	Statistically anomalous paths (over/under-represented)
`build_mogen()`	Optimal Markov order per node

hon  <- build_hon(net, max_order = 2)
pathways(hon)                      # arrow-notation path strings
hypa <- build_hypa(net)
hypa$over                          # over-represented paths with p-values

Topological Analysis

Go beyond edges — find cliques, holes, and high-order connectivity using tools from algebraic topology.

sc <- build_simplicial(net, method = "clique")
betti_numbers(sc)          # connected components, cycles, voids
euler_characteristic(sc)
ph <- persistent_homology(net)  # track topology across thresholds
plot(ph)
qa <- q_analysis(sc)       # Atkin's Q-connectivity structure vectors

Sequence Visualization

Visualize raw sequence data as index plots or state distribution charts — before or after clustering.

# Sequence index plot: one row per person, coloured by state
sequence_plot(net)

# After clustering: faceted by cluster
clust <- build_clusters(net, k = 3)
sequence_plot(clust, type = "index")

# State distribution over time
distribution_plot(net, type = "area")
distribution_plot(clust, type = "bar")

Sequence Pattern Comparison

sequence_compare() extracts all k-gram patterns from grouped sequences, counts per-group frequencies, and tests statistical differences via permutation — answering the question “do these groups actually behave differently, and where?”

data(human_long)
net <- build_network(human_long, method = "tna",
                     action = "action", actor = "session_id",
                     time = "time", group = "phase")

res <- sequence_compare(net, sub = 2:4, test = "chisq", adjust = "fdr")
res$patterns                        # per-pattern frequencies + p-values
plot(res)                           # back-to-back pyramid chart
plot(res, style = "heatmap")        # heatmap for many patterns

Association Rule Mining

association_rules() mines “if A then B” patterns from sequences or binary matrices using the Apriori algorithm. Returns support, confidence, lift, and conviction for every rule above a threshold.

rules <- association_rules(net, min_support = 0.05, min_confidence = 0.6)
rules$rules                       # tidy data.frame, sorted by lift
pathways(rules)                   # rules as arrow-notation strings

# From a raw binary matrix
rules2 <- association_rules(binary_mat, min_support = 0.1)

Link Prediction

predict_links() scores all unobserved node pairs using structural similarity, identifying which missing connections are most likely to exist. evaluate_links() computes AUC, precision, and recall against held-out edges.

preds <- predict_links(net)       # common neighbours, Adamic-Adar, Katz, ...
head(preds$scores)                # sorted by predicted score

# Evaluate against known missing edges
eval  <- evaluate_links(net, held_out = test_edges)
eval$auc

Markov Chain Analysis

markov_stability() measures how stable a network partition is under random-walk dynamics at different time scales — a resolution-free way to find communities. passage_time() computes expected first-passage and return times between states.

stab <- markov_stability(net, times = seq(0.1, 10, 0.1))
plot(stab)                # stability vs time-scale curve

pt <- passage_time(net)
pt$first_passage          # expected steps to reach state j from state i
pt$return_time            # expected steps to return to the same state

Statistical Validation

Every network type shares the same validation pipeline.

# Bootstrap confidence intervals
boot <- bootstrap_network(net, iter = 1000)
summary(boot)

# Permutation test: are two networks different?
perm <- permutation(group_nets$`Cluster 1`, group_nets$`Cluster 2`)

# Network Comparison Test (NCT): formal test of structure + global strength
nct_res <- nct(data1, data2, iter = 500)
print(nct_res)            # M-statistic, S-statistic, per-edge p-values

# Split-half reliability
network_reliability(net)

# Centrality stability (CS-coefficient)
centrality_stability(net)

# Glasso-specific bootstrap (edge inclusion + centrality CIs)
boot_gl <- boot_glasso(net_pna, iter = 1000)

Function	Purpose
`bootstrap_network()`	Bootstrap CIs and p-values for each edge
`permutation()`	Edge-level comparison between two networks
`nct()`	Formal Network Comparison Test (global strength + structure)
`network_reliability()`	Split-half reliability of edge weights
`centrality_stability()`	CS-coefficient via case-dropping
`boot_glasso()`	Edge inclusion, centrality CIs, difference tests for glasso networks

Bundled Datasets

Dataset	Description
`human_long`	10,796 human actions across 429 human-AI coding sessions
`ai_long`	Matched AI actions from the same 429 sessions
`human_cat`	Same sessions coded at category level (9 types)
`human_detailed`	Same sessions at fine-grained code level
`srl_strategies`	SRL strategy frequencies — 250 students, 9 strategies
`chatgpt_srl`	ChatGPT-generated SRL scale scores for psychological networks
`learning_activities`	Binary learning activity indicators — 200 students × 30 timepoints
`group_regulation_long`	Group regulation sequences with covariates
`trajectories`	138-student engagement trajectory matrix

Documentation

Citation

If you use Nestimate in your research, please cite:

Saqr, M., Lopez-Pernas, S., Tormanen, T., Kaliisa, R., Misiejuk, K., & Tikka, S. (2025). Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning. Proceedings of the 15th Learning Analytics and Knowledge Conference. doi: 10.1145/3706468.3706513

Saqr, M., Beck, E., & Lopez-Pernas, S. (2024). Psychological Networks. In M. Saqr & S. Lopez-Pernas (Eds.), Learning Analytics Methods and Tutorials (pp. 513–546). Springer. doi: 10.1007/978-3-031-54464-4_19

License

MIT