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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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