Language: en-GB), with dialect fixes across the
documentation prose and a new inst/WORDLIST so the package
spell check runs clean.gimme, graphicalVAR, glasso,
corpcor, data.table, qgraph,
rio, and jsonlite, none of which the shipped
package uses.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.