v1.4.0

• Support space-time (‘matern_space_time’) and anisotropic ARD (‘matern_ard’, ‘gaussian_ard’) covariance functions
• support ‘negative_binomial’ likelihood
• support FITC aka modified predictive process approximation (‘fitc’) and full scale approximation with tapering (‘full_scale_tapering’) with ‘cholesky’ decomposition and ‘iterative’ methods
• add optimizer_cov option 'lbfgs', and make this the default for (generalized) linear effects models
• faster prediction for multiple grouped random effects and non-Gaussian likelihoods
• allow for duplicate locations / coordinates for Vecchia approximation for non-Gaussian likelihoods
• support vecchia approximation for space-time and ARD covariance functions with correlation-based neighbor selection
• support offset in GLMMs
• add safeguard against too large step sizes for linear regression coefficients
• change default initial values for (i) (marginal) variance and error variance to var(y)/2 for Gaussian likelihoods and (ii) range parameters such that the effective range is half the average distance
• add backtracking line search for mode finding in Laplace approximation
• add option ‘reuse_learning_rates_gp_model’ for GPBoost algorithm -> faster learning
• add option ‘line_search_step_length’ for GPBoost algorithm. This corresponds to the optimal choice of boosting learning rate as in e.g. Friedman (2001)
• support optimzer_coef = ‘wls’ when optimizer_cov = ‘lbfgs’ for Gaussian likelihood, make this the default