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