dgpsi v2.6.0

• Prediction speed with predict() enhanced for small testing data sets by reducing overhead caused by the multi-threading implementation.
• The Python environment now installs packages exclusively from conda-forge whenever possible. Packages from other channels will only be used if they are unavailable on conda-forge.
• A bug in vigf(), affecting a bundle of emulators that includes GP emulators, has now been fixed.
• The column names from the training input and output provided to gp() and dgp() are retained in the relevant slots of the returned objects, as well as in any updated objects produced by the downstream functions that operate on them.
• The column names from the testing input and output supplied to validate() and design() are retained in the relevant slots of the returned objects.
• Improved numerical stability and achieved ~30x faster speed for DGP emulators using heteroskedastic likelihoods with replicates, with or without the Vecchia approximation.
• Enhanced initialization of DGP emulators with heteroskedastic and categorical likelihoods for improved performance.
• Removed the mode argument from predict() for DGP emulators with categorical likelihoods. Predictions of class probabilities can now be obtained using either the "mean_var" or "sampling" method.
• Set the default method for predict(), validate(), and plot() to "mean_var".
• Redesigned the output of validate() for dgp objects with likelihood = "Categorical". See validate() documentation for details.
• Added support for the nugget_est argument in dgp() to control whether nuggets of GP nodes feeding into the likelihood node are estimated when likelihood is not NULL.
• Updated initial nugget values when nugget_est = TRUE in dgp(). If likelihood = NULL, all initial GP nuggets default to 1e-6; otherwise, GP nodes feeding into the likelihood node default to 1e-4 and all others to 1e-6.
• Added the accuracy metric to the figures produced by plot() for DGP emulators with categorical likelihoods.
• Fixed the confusion matrix visualization (style = 2 in plot()) so that the diagonal is drawn from top-left to bottom-right.
• Updated init_py() to handle errors related to TOS acceptance when installing Miniconda, and to automate TOS acceptance for required channels.
• Enabled use of the newaccelerate BLAS library on Apple Silicon machines running macOS >= 13.3.
• Added the decouple argument to dgp() to allow likelihood parameters to be modeled using separate deep Gaussian process hierarchies when depth > 2.
• Added the link argument to dgp() to support binary classification using either logit or probit link function when likelihood = "Categorical".
• Inference for (D)GPs with homogeneous noise and replicates in the training data has been significantly enhanced, achieving over 10× speed-up.