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)

v0.4.0

• update LightGBM part to version 3.1.1.99 (git commit 42d1633aebe124821cff42c728a42551db715168)
• add support for scikit-learn wrapper interface for GPBoost
• change initialization of score (=tree ensemble) for non-Gaussian data for GPBoost algorithm
• add support for saving and loading models from file in R and Python packages

v0.3.0

• Add support for non-Gaussian data (other loss functions than L2 loss). Currently supported: binary data, Poisson data, gamma distributed data
• Changed the default value for 'use_gp_model_for_validation' from False to True
• Covariance parameter estimation: add safeguard against too large steps also when using Nesterov acceleration
• Changed the default value for 'use_nesterov_acc' from False to True. This is only relevant for gradient descent based covariance parameter estimation. For Gaussian data (=everything the library could handle so far before version 0.3.0), Fisher scoring (aka natural gradient descent) is used by default and this is not relevant for Fisher scoring
• Change default values for gradient descent based covariance parameter estimation: 'lr_cov=0.1' (before 0.01), 'lr_coef=0.1' (before 0.01), 'acc_rate_coef =0.5' (before 0.1). This is only relevant for gradient descent based covariance parameter estimation. For Gaussian data (=everything the library could handle so far before version 0.3.0), Fisher scoring (aka natural gradient descent) is used by default and this is not relevant for Fisher scoring
• Change parameter 'std_dev' from being a single parameter in 'fit' of a GPModel function to being a part of the 'params' parameter of the 'fit' function
• Removed the boosting parameter 'has_gp_model' (not visible to most users)
• Removed storage of the optimizer paramters 'optimizer_cov' and 'init_cov_pars' from R/Python to C++ only (not visible to user)

v0.2.0

• GPModel : change default convergence criterion to relative change in negative log-likelihood for model fitting
• GPModel : add safeguard against too large steps (step halving) for gradient descent and Fisher scoring (without Nesterov acceleration) when doing model fitting
• Add support for R version 4.0
• GPModel: faster initialization of GPModel for grouped data with grouping data that is not ordered
• GPModel: faster model fitting for grouped data due to changes in the use of the Woodburry identity

v0.1.0

Major changes

• use Woodbury identity for grouped random effects models -> faster inference for grouped random effects / mixed effects models
• no profiling out of error variance for Fisher scoring -> faster learning of covariance paramters
• add functionality that negative log-likelihood can be evaluated