.GlobalEnv in prior calibration
helpers.var1() — a Vector Autoregressive order-1 latent
field for bivariate time series modelling.RCallback in src/latents/rcallback.cpp).ngme2 noise distributions (NIG, GAL,
normal) as a single shared innovation noise.print() method displays the recovered \(A\) matrix, its spectral radius, and the
raw \((p_1, p_2, p_3, p_4)\)
values.vignettes/var1-model.Rmd) covering: model specification,
Cayley reparameterization, simulation study with parameter recovery,
convergence trace plots, and NIG vs Gaussian model comparison.posterior_plot().plot() support for ngme_sgld_ci
objects, reusing stored SGLD samples to visualize marginal posterior
distributions.~ 0 + ...): skip
fixed-effect centering when no intercept is present.fe() centering with structural zeros: grouped
fe() columns are centered using in-group rows only, so
out-of-group structural zeros remain zero.data_idx) instead of always taking the first
n rows.start = previous_fit)
across standardization settings by remapping fixed effects through the
current model parameterization."(Intercept)"*) now default to prior_none(),
while non-intercept columns keep the default N(0,10)
prior.standardize_fixed = TRUE, add prior compatibility
handling: isotropic normal priors on standardized columns are
transformed to the SVD basis; incompatible custom
prior_beta specifications now automatically disable
fixed-effect standardization with a warning.prior_inv_exponential(lambda, lower) for
nu, implementing kappa = 1 / nu ~ Exp(lambda)
as a first-class prior option.prior_inv_exp(...) for the same
prior.calibrate_inv_exp_lambda_driven_nig() and
calibrate_inv_exp_lambda() for choosing lambda
from a driven-noise tail-inflation target.R_c(nu)
curves: the helper now scans for crossings and reports observed
R_c range when the requested target is unattainable.nu prior in f() for
NIG-driven noise: when nu prior is not explicitly set and
nu is stationary, use
prior_inv_exp(lambda = log(2)/median(h), lower = nu_lower_bound).
For non-stationary nu, keep the legacy N(0,10)
default prior.ngme() estimation/sampling
path: C++ exceptions are now propagated as R errors (including OpenMP
parallel regions) instead of potentially terminating the R session.nu initialization in noise helper constructors to
respect nu_lower_bound, using
theta_nu = log(nu - nu_lower_bound) and validating
nu > nu_lower_bound.normal_nig conversion, printing, and plotting
with effective parameterization
nu = nu_lower_bound + exp(theta_nu).prior_normal(), prior_pc_sd(),
prior_half_cauchy(), prior_none(), and
priors(...).f() and ngme_noise() to accept
unified prior = ... inputs (remove
prior_theta_K and
prior_mu/prior_sigma/prior_nu arguments).ngme_prior() interface and its
documentation entry.coef/field) for
noise parameter priors and per-parameter operator prior
compilation.ngme(..., prior_beta = ...), using the same
prior_*()/priors(...) API.Prior Templates for Stationary and Non-Stationary Models.control_opt(stepsize_decay = "grad_norm_plateau")
(epoch-level, synchronized across chains)stepsize_decay() helper for configuring decay
optionscross_validation(data = ...) model rebuild for
refit-on-new-data workflows: it now resolves external formula symbols
(for example mesh, B, n_basis)
from the fitted object when needed, and falls back to
rebuild-without-start plus hyperparameter transplant if
start state dimensions differ.chain_combine = "predictive_average" in
predict() and cross_validation(), which
averages predictions across optimization chains instead of averaging
parameters first.control_opt to
specify the solver
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