NEWS

SPmodels 0.1.1

Allow the nugget variance to be fixed independently while other covariance parameters are estimated. nugget_variance = 0 now fits the no-nugget boundary model, and estimate_nugget = FALSE fixes any supplied non-negative nugget.
Propagate fixed-nugget parameter maps through dense Gaussian, Laplace, Monte Carlo maximum-likelihood, nearest-predecessor, prediction-uncertainty, simulation-recovery, likelihood and reporting workflows.
Report fixed covariance parameters without invented standard errors or likelihood degrees of freedom.
Correct the transformed-covariance ordering used by predictive parameter draws when grouped random slopes and temporal scale are both estimated.
Add native and WebAssembly regression coverage for no-nugget models.