NEWS

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ngme2 0.9.8

• Add pm25_quarterly_2022, a processed example dataset of quarterly PM2.5 monitor averages from EPA AQS/AirData for the contiguous United States in 2022. The dataset includes monitor location, quarter, quarterly average PM2.5, contributing day counts, and a replicate index for quarterly models.

ngme2 0.9.7 (2026-04-28)

• Add noise_moments() for stationary ngme_noise objects, with direct helpers noise_nig_moments() and noise_gal_moments() returning variance, standard deviation, skewness, kurtosis, excess kurtosis, and central moments.
• Improve prediction for composite latent noises (normal_nig, normal_gal, and nig_gal) by expanding prediction projection matrices when required and using the output-sample prediction path for non-mean estimators.
• Make inverse-exponential prior calibration less intrusive to the global RNG state by using local seeding for generated Gaussian samples.
• Update pkgdown configuration for the current reference site.

ngme2 0.9.6

• Improve estimation and sampling console output: ngme() now respects control_opt(verbose = ...) more consistently and uses messages instead of unconditional cat() output.
• Improve C++ estimation robustness for serial/OpenMP builds, including single-chain variance handling and guarded OpenMP-only calls.
• Make print and plotting helpers more S3-friendly: print methods now return their input invisibly, and traceplot() no longer prints last estimates as a side effect.
• Make simulate.ngme() compatible with the S3 simulate() interface by accepting posterior and m_noise through ....
• Export get_data_from_formula() and improve diagnostics that depend on captured model/design output.
• Refresh package metadata, examples, and roxygen documentation, including SuiteSparse attribution and a fuller package description.

ngme2 0.9.5

New feature: VAR(1) bivariate latent model

• Add var1() — a Vector Autoregressive order-1 latent field for bivariate time series modelling.
• Stationarity is guaranteed by construction via the Cayley reparameterization: four unconstrained parameters $(p_1, p_2, p_3, p_4)$ are mapped to a $2\times2$ VAR coefficient matrix $A$ with spectral radius $\rho(A) < 1$ at every iteration.
• The C++ backend implements the Cayley transform natively (class RCallback in src/latents/rcallback.cpp).
• Supports all 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.
• Add vignette "VAR(1) Bivariate Model in ngme2" (vignettes/var1-model.Rmd) covering: model specification, Cayley reparameterization, simulation study with parameter recovery, convergence trace plots, and NIG vs Gaussian model comparison.

ngme2 0.9.4

• Add posterior distribution plotting for SGLD samples via posterior_plot().
• Add plot() support for ngme_sgld_ci objects, reusing stored SGLD samples to visualize marginal posterior distributions.

ngme2 0.9.3

• Refine fixed-effect standardization: SVD now applies only to non-intercept columns; intercept columns remain on their original parameterization.
• Preserve no-intercept model semantics (~ 0 + ...): skip fixed-effect centering when no intercept is present.
• Improve fe() centering with structural zeros: grouped fe() columns are centered using in-group rows only, so out-of-group structural zeros remain zero.
• Fix fixed-effect scale restoration for multi-replicate models: when needed, reconstruction now uses replicate row indices (data_idx) instead of always taking the first n rows.
• Improve warm-start robustness (start = previous_fit) across standardization settings by remapping fixed effects through the current model parameterization.
• Update default fixed-effect priors: intercept-like columns ("(Intercept)"*) now default to prior_none(), while non-intercept columns keep the default N(0,10) prior.
• For 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.

ngme2 0.9.2

• Add prior_inv_exponential(lambda, lower) for nu, implementing kappa = 1 / nu ~ Exp(lambda) as a first-class prior option.
• Add shorthand alias prior_inv_exp(...) for the same prior.
• Add calibration helpers calibrate_inv_exp_lambda_driven_nig() and calibrate_inv_exp_lambda() for choosing lambda from a driven-noise tail-inflation target.
• Improve calibration robustness for non-monotone R_c(nu) curves: the helper now scans for crossings and reports observed R_c range when the requested target is unattainable.
• Update default 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.

ngme2 0.9.1

• Harden error handling in ngme() estimation/sampling path: C++ exceptions are now propagated as R errors (including OpenMP parallel regions) instead of potentially terminating the R session.
• Fix 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.
• Align normal_nig conversion, printing, and plotting with effective parameterization nu = nu_lower_bound + exp(theta_nu).

ngme2 0.9.0

• Refactor prior API (breaking change): use prior_normal(), prior_pc_sd(), prior_half_cauchy(), prior_none(), and priors(...).
• Update f() and ngme_noise() to accept unified prior = ... inputs (remove prior_theta_K and prior_mu/prior_sigma/prior_nu arguments).
• Remove legacy ngme_prior() interface and its documentation entry.
• Add prior target support (coef/field) for noise parameter priors and per-parameter operator prior compilation.
• Add fixed-effect prior support via ngme(..., prior_beta = ...), using the same prior_*()/priors(...) API.
• Add user-facing vignette: Prior Templates for Stationary and Non-Stationary Models.

ngme2 0.8.5

• Add grad.norm plateau-based step decay via control_opt(stepsize_decay = "grad_norm_plateau") (epoch-level, synchronized across chains)
• Add stepsize_decay() helper for configuring decay options
• Update verbose output to include stepsize decay scale and effective stepsize
• Improve cross_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.
• Add chain-aware prediction/CV aggregation via chain_combine = "predictive_average" in predict() and cross_validation(), which averages predictions across optimization chains instead of averaging parameters first.

ngme2 0.8.4

• Fix iid model using argument mesh instead of map

ngme2 0.8.3

• Update nu parameterization to be relative to nu_lower_bound (nu = nu_lower_bound + exp(B_nu * theta_nu))
• Allow NULL specification for noise parameter in non-stationary case
• Update fixed effects initialization
• Update start=fit logic

ngme2 0.8.2

• Add support for different type of ldlt solvers
• Use solver_type and solver_backend in control_opt to specify the solver
• Give print when hit nu lower bound

ngme2 0.8.1

• Add ldlt solver (solver_type = "ldlt")
• Minor fix on matern model on handling integer alpha case

ngme2 0.8.0

• Simplify solver structure
• Update to use model() instead of string for model definition
• Big update on the R interface, use model() instead of string for model definition
• Refactor on the codebase
• Add more vignettes

ngme2 0.7.1

• Documentation updates
• Add print log likelihood for Gaussian models
• Improvements to prediction with condition on specific data points
• Enhanced preconditioner with Gibbs samples
• Updates to AR1 vignette
• Various optimization workflow improvements

ngme2 0.6.0

• First version of the package

ngme2 0.3.0

• Add replicate feature
• Add OU process
• Add tensor-product model