NEWS.md

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Nestimate 0.4.3

CRAN resubmission (addresses incoming-check NOTEs on 0.4.2)

• Tarball now ships build/vignette.rds (the vignette index). Previous 0.4.2 build used R CMD build --no-build-vignettes, which preserved pre-built inst/doc/*.html but stripped the index — CRAN flagged "VignetteBuilder field but no prebuilt vignette index."
• test-gimme.R now skip_on_cran(). GIMME tests fit a lavaan SEM per subject and took ~50s locally (2-3× on Windows), pushing total check time to 11 min on win-devel. Full test suite still runs in CI and local dev.

Nestimate 0.4.2

CRAN resubmission

• Full --as-cran --run-donttest audit pass.
• Purged stale .Rcheck/ and Meta/ build artifacts from working tree; added explicit ^Nestimate\.Rcheck$ and ^\.\.Rcheck$ entries to .Rbuildignore as belt-and-suspenders against repeat-submission contamination.

Nestimate 0.4.1

CRAN resubmission

• Pre-built vignettes included in inst/doc/ as required by CRAN.
• Fixed 301-redirect URLs in README.
• Added skip_on_cran() to slow test block to keep check time under 10 minutes.

Nestimate 0.4.0

New functions

• build_mlvar() — multilevel VAR networks from ESM/EMA panel data. Estimates temporal (directed), contemporaneous (undirected), and between-subjects (undirected) networks matching mlVAR::mlVAR() at machine precision.
• build_mmm() / compare_mmm() — mixture of Markov models via EM, with BIC/AIC/ICL model selection and optional covariate regression.
• cooccurrence() — standalone co-occurrence network builder supporting 6 input formats and 8 similarity methods.
• sequence_compare() — k-gram pattern comparison across groups with optional permutation testing.
• sequence_plot() / distribution_plot() — base-R sequence index and state distribution plots with clustering integration.
• build_simplicial(), persistent_homology(), q_analysis() — topological analysis of networks via simplicial complexes.
• nct() — Network Comparison Test matching NetworkComparisonTest::NCT() at machine precision.
• build_gimme() — group iterative mean estimation for idiographic networks via lavaan.
• passage_time(), markov_stability() — Markov chain passage times and stability analysis.
• predict_links() / evaluate_links() — link prediction with 6 structural similarity methods.
• association_rules() — Apriori association rule mining from sequences or binary matrices.
• predictability() — node predictability for glasso/pcor/cor networks.
• build_hon(), build_honem(), build_hypa(), build_mogen() — higher-order network methods (HON, HONEM, HYPA, MOGen) now cograph_network-compatible.

New datasets

• human_long, ai_long — canonical long-format human–AI pair programming interaction sequences (10,796 turns, 429 sessions).
• chatgpt_srl — ChatGPT-generated SRL scale scores for psychological network analysis.
• trajectories — 138-student engagement trajectory matrix (15 timepoints, 3 states).

API

• build_clusters(), network_reliability(), permutation(), and prepare() replace earlier internal names for consistency with the build_* naming convention.
• mgm estimator added (method = "mgm") for mixed continuous + categorical data via nodewise lasso, matching mgm::mgm() at machine precision.

Bug fixes

• build_mmm() no longer crashes on platforms where parallel::detectCores() returns NA (macOS ARM64 CRAN check failure).
• gimme convergence filter now correctly handles all typed NA variants (NA_character_, NA_real_, etc.).
• NaN values in numeric metadata aggregation (all-NA sessions) normalized to NA_real_.
• HYPA p-values corrected; hypa_score column renamed to p_value.

CRAN compliance

• .data pronoun added to globalVariables().
• base::.rowSums() / base::.colSums() replaced with rowSums() / colSums().
• dev.new() guarded by interactive() — no side effects under knitr or CI.
• Equivalence test files excluded from the built tarball.

Performance

• do.call(rbind, ...) replaced with data.table::rbindlist() in mcml.R and sequence_compare.R.

Nestimate 0.3.4

• HYPA: Renamed hypa_score column to p_value for clarity. Added $over, $under, $n_over, $n_under fields to net_hypa objects. Scores are now pre-sorted with anomalous paths first.
• HYPA: summary.net_hypa() now shows over/under-represented paths separately with a configurable n parameter.
• pathways.netobject(): New S3 method to extract higher-order pathways directly from a netobject (builds HON or HYPA internally).
• path_counts(): Now handles NAs in trajectories by stripping them before k-gram counting.

Nestimate 0.2.15

• Preparing for publication

Nestimate 0.2.0

• Reduced hard dependencies from 6 to 4 Imports (ggplot2, glasso, data.table, cluster).
• Removed igraph from Imports — centrality_stability() and boot_glasso() now accept a centrality_fn parameter for external centrality computation.
• Removed tna from Imports — moved to Suggests (only used for input class detection).
• Implemented graphical_var() from scratch using coordinate descent lasso + graphical lasso with EBIC model selection, eliminating the graphicalVAR dependency.
• Dropped ml_graphical_var() — users should use mlvar() for multilevel VAR.
• Removed cograph plot wrappers — plot.netobject(), plot.net_bootstrap(), plot.net_permutation(), plot.net_hon(), plot.net_hypa() and as_cograph() removed. Users call cograph plotting functions directly on netobjects.
• Added attention estimator for decay-weighted transition networks.
• Increased test coverage from 84.5% to 96.1% (2780 tests).
• Passes R CMD check with 0 errors, 0 warnings, 0 notes.

Nestimate 0.1.0

• Initial release. Split from Saqrlab v0.3.0.
• Core estimation via build_network() with 8 built-in estimators.
• Bootstrap inference (bootstrap_network()), permutation testing (permutation()), EBICglasso bootstrap (boot_glasso()).
• Higher-order networks: HON, HONEM, HYPA, MOGen.
• GIMME, MCML, multilevel VAR, graphical VAR.
• Temporal network analysis and velocity TNA.
• Dual-class c("netobject", "cograph_network") output for cograph compatibility.

Nestimate documentation built on April 20, 2026, 5:06 p.m.