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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.
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.
Health stats visible at Monitor.