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)