# idiographic 0.3.2

* New package Title — "Idiographic Person-Specific and Heterogeneous Complex
  Networks" — and a rewritten Description with method references
  <doi:10.1007/978-3-031-95365-1_20> and <doi:10.1080/00273171.2018.1454823>.
* Added Sonsoles López-Pernas as package author.
* Documentation language standardized to British English (`Language: en-GB`),
  with dialect fixes across the documentation prose and a new `inst/WORDLIST`
  so the package spell check runs clean.
* Slimmer CRAN footprint: all competitor-equivalence tests now live only in
  the repository's opt-in validation lane and are excluded from the CRAN
  tarball. Suggests trimmed from 16 to 8 packages — removed `gimme`,
  `graphicalVAR`, `glasso`, `corpcor`, `data.table`, `qgraph`, `rio`, and
  `jsonlite`, none of which the shipped package uses.

# idiographic 0.2.0

* Made the CRAN package offline-first: the only mandatory imports are standard
  R packages, while `lme4`, `lavaan`, plotting, and external backends are
  optional. Competitor-oracle tests and the real-panel corpus now run in a
  separate opt-in `validation/` lane and are excluded from the CRAN tarball.
* Added a registry-backed `fit_idiographic()` front door, estimator discovery,
  method-specific `equivalence()` declarations, package-wide
  `equivalence_table()` and argument-by-argument `argument_coverage()` ledgers,
  and common tidy accessors. All 17 registered methods and 315 current public
  formals now have an executable evidence classification; new unassessed
  arguments fail the closure test.
* Expanded direct-oracle testing across graphicalVAR option combinations,
  mlVAR multi-lag/preprocessing/unique-model configurations, and bivariate plus
  three-variable GIMME standard, hybrid, and VAR searches. GIMME evidence now
  also covers fit statistics, uneven panels, exogenous-variable dimensions,
  and interacting correction/standardization controls. Tightened public argument
  validation so engine-specific controls cannot be silently ignored.
* Closed the remaining executable evidence cells: all 12 supported lag-1 lmer
  mlVAR structure combinations, per-subject/missing-data graphicalVAR fits,
  GIMME 10.0 correction/stopping/standardization/cutoff/forced-path controls,
  standardized ML/MLR uSEM fits, Mplus wrapper forwarding/conversion, Bayesian
  burn-in/thinning, positive random-residual recovery, parallel mlVAR, and
  base-R linear/logistic idiographic-ML engine equality.
* Migrated the 20-panel real ESM mlVAR validation corpus from the
  Dynalytics/psychaj work into the CRAN-excluded `validation/` lane, with
  self-contained raw inputs, mlVAR 0.7.3 frozen
  oracles, provenance hashes, and explicit regression coverage for missing IDs,
  irregular occasion gaps, and degenerate between-person networks. Duplicate
  observation keys now fail clearly instead of producing row-order-dependent
  preprocessing.

* **Uniform `fit_*` naming for all estimators (breaking).** Every model-fitting
  verb now uses a single `fit_` prefix: `fit_var()`,
  `fit_graphical_var()`, `fit_mlvar()`, `fit_rolling_var()`, and so on for all
  estimators. Short model nicknames passed to `compare_idiographic()`,
  `estimate_stability()`, and `validate_forecast()` (for example, `"var"` and
  `"graphical_var"`) are unchanged.
* New native Bayesian estimators that statistically reproduce Mplus DSEM output
  without requiring Mplus:
  * `fit_mlvar_bayes()` — two-level Bayesian VAR(1) with latent mean centring.
    `temporal = "fixed"` is statistically validated against frozen Mplus DSEM
    fixed-temporal + random-intercept fixtures;
    `temporal = "random"` fits the full DSEM with person-specific temporal
    matrices and a random-effect covariance (reports random-slope SDs).
  * `fit_var_bayes()` — single-level Bayesian VAR(1), the unregularized
    Bayesian analogue of `fit_graphical_var()`.
* Pure-R conjugate Gibbs sampler (hand-rolled inverse-Wishart draws; no new
  dependencies). Posterior median / SD / 95% CI / one-tailed p, three networks
  (temporal, contemporaneous, between), and a Gelman-Rubin PSR diagnostic.
* Validated to statistical (Monte-Carlo-error) equivalence against real Mplus 9
  output with frozen ground-truth fixtures and parity tests.
* Added `fit_ml()` for idiographic supervised machine-learning: ordered
  within-person train/test splits, person-specific models, pooled baselines on
  the same held-out rows, regression/classification metrics, row-level
  predictions, and coefficient extraction via `coefs()`. `model` names the
  statistical/ML model (for example, `"ridge"`), while `estimator`
  names the implementation/backend (default `"native"`). No new dependencies:
  native models include mean/majority baselines, OLS/logistic, ridge, lasso,
  elastic net, PCR, LDA, Gaussian naive Bayes, kNN, and one-split trees.
  `fit_idiographic_ml()` and `fit_individualized_ml()` remain aliases.

# idiographic 0.1.0

* Initial CRAN submission.
* Provides idiographic network estimators for intensive longitudinal data,
  including ordinary VAR, graphical VAR, mlVAR, uSEM, and GIMME-style models.
* Includes preprocessing audits, rolling-window estimation, forecast validation,
  edge stability diagnostics, model comparison, tidy accessors, and cograph
  plotting support.
