v1.5.4

• support Matern covariance functions with general shape parameters (‘cov_fct_shape’), not just 0.5, 1.5, 2.5
• reduce memory footprint of ‘vecchia’ approximation
• "make 'fitc' and 'full_scale_taperig' approximations numerically more stable
• [python-package] add function ‘tune_pars_TPE_algorithm_optuna’ for choosing tuning parameters with the Tree-structured Parzen estimator algorithm
• [R-package] add function ‘tune.pars.bayesian.optimization’ for choosing tuning parameters using Bayesian optimization and the ‘mlrMBO’ R package
• change default initial values for marginal variances when having multiple random effects
• [R-package] fix bug in ‘gpb.grid.search.tune.parameters’ for ‘metric’ parameter for metrics for which higher is better (auc, average_precision)
• other bug fixes

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

v1.2.5

• support iterative methods for Vecchia-Laplace approximation (non-Gaussian data and gp_approx=”vecchia”)
• faster model construction and prediction for compactly supported covariance functions
• add metric 'test_neg_log_likelihood'
• change handling of 'objective' parameter for GPBoost algorithm: only ‘likelihood’ in ‘GPModel()’ needs to be set
• change API for parameters ‘vecchia_pred_type’ and ‘num_neighbors_pred’

v1.0.1

• faster gradient calculation for

  1. Multiple / multilevel grouped random effects for non-Gaussian likelihoods
  2. GPs with Vecchia approximation for non-Gaussian likelihoods
  3. GPs with compactly supported covariance functions / tapering

• enable estimation of shape parameter in gamma likelihood

• predict_training_data_random_effects: enable for Vecchia approximation and enable calculation of variances

• change API for Vecchia approximation and tapering

• correction in nearest neighbor search for Vecchia approximation

• show GPModel parameters on original and not transformed scale when trace = true

• change initial intercept for bernoulli_probit, gamma, and poisson likelihood

• change default value for ‘delta_rel_conv’ to 1e-8 for nelder_mead

• avoid unrealistically large learning rates for gradient descent

v0.8.0

• cap too large gradient descent steps on log-scale for covariance parameters, GLMMs: reset small learning rates for covariance parameters and regression parameters if the other parameters change
• add gaussian_neg_log_likelihood as validation metric
• add function ‘get_nested_categories‘ for nested grouped random effects
• prediction: remove nugget variance from predictive (co)variances when predict_response = false for Gaussian likelihoods
• set default value for predict_response to true in prediction function of GPModel
• NA’s and Inf’s are not allowed in label
• correct prediction if Vecchia approximation for non-Gaussian likelihoods

v0.7.7

• Reduce memory usage for Vecchia approximation
• [R-package] add function for creating interaction partial dependence plots
• Add function ‘predict_training_data_random_effects’ for predicting (=‘estimating’) training data random effects
• [R-package][python-package] predict function: rename ‘raw_score’ argument to ‘pred_latent’ and unify handling of Gaussian and non-Gaussian data
• (G)LMMs: better initialization of intercept, change internal scaling of covariates, change default value of ‘lr_coef’ to 0.1
• Add ‘adam’ as optimizer option
• allow for grouped random coefficients without random intercept effects
• [R-package][python-package] nicer summary function

v0.7.1

• make predictions faster and more memory efficient when having multiple grouped random effects
• set “nelder_mead” as automatic fallback option if problems in optimization occur
• (generalized) linear mixed effects models: scale covariate data for linear predictor internally for optimization using gradient descent
• add “bfgs” as optimizer option

v0.6.7

• add Grabit model / Tobit objective function
• support calculation of approximate standard deviations of fixed effects coefficients in GLMMs
• [R package] added function for creating partial dependence plots (gpb.plot.partial.dependence)
• [R package] use R’s internal .Call function, correct function registration, use R’s internal error function, use R standard routines to access data in C++, move more finalizer logic into C++ side, fix PROTECT/UNPROTECT issues, limit exported symbols in DLL,
• [Python package] Fix bug in scikit-learn wrapper for classification
• change in initialization and checking of convergence criterion for mode finding algorithm for Laplace approximation for non Gaussian data

v0.6.0

• add support for Wendland covariance function and covariance tapering
• add Nelder-Mead as covariance parameter optimizer option
• change calculation of gradient for GPBoost algorithm and use permutations for Cholesky factors for non-Gaussian data
• use permutations for Cholesky factors for Gaussian data when having sparse matrices
• make “gradient_descent” the default optimizer option also for Gaussian data

v0.5.0

• add function in R and Python packages that allows for choosing tuning parameters using deterministic or random grid search
• faster training and prediction for grouped random effects models for non-Gaussian data when there is only one grouping variable
• faster training and prediction for Gaussian process models for non-Gaussian data when there are duplicate locations
• faster prediction for grouped random effects models for Gaussian data when there is only one grouping variable
• support pandas DataFrame and Series in Python package
• fix bug in initialization of score for the GPBoost algorithm for non-Gaussian data
• add lightweight option for saving booster models with gp_models by not saving the raw data (this is the new default)
• update eigen to newest version (commit b271110788827f77192d38acac536eb6fb617a0d)