CRAN release: 2023-09-03
- A bug from the underlying Python implementations is fixed when
name = matern2.5in
- Thanks to @yyimingucl, a bug from the underlying Python implementations for the MICE sequential design criterion
- An argument
resetis added to
design()to reset hyperparameters of a (D)GP emulator to their initial values (that were specified when the emulator is initialized) after the input and output of the emulator are updated and before the emulator is refitted. This argument can be useful for sequential designs in cases where the hyperparameters of a (D)GP emulator get caught in suboptimal estimates. In such circumstances, one can set
reset = TRUEto reinitialize the (D)GP emulator in some steps of the sequential designs as a strategy to escape the poor estimates.
- The refitting of an emulator in the final step of a sequential design is no longer forced in
- An argument
typeis added to
plot()to allow users to draw OOS validation plots with testing data shown as a line instead of individual points when the emulator’s input is one-dimensional and
style = 1.
- Thanks to @tjmckinley, an issue relating to
libstdc++.so.6on Linux machines when R is restarting after the installation of the package is fixed.
mice()can locate new design points for stochastic simulators with (D)GP or bundle emulators that can deal with stochastic outputs.
design()can be used to construct (D)GP or bundle emulators adaptively by utilizing multiple realizations from a stochastic simulator at the same design positions through the new argument
method = almor
method = mice.
- A new slot called
specsis added to the objects returned by
dgp()that contains the key information of the kernel functions used in the constructions of GP and DGP emulators.
- Due to a bug in the latest version of an underlying Python package, the emulators saved by
2.2.0may not work properly with
design()when they are loaded back by
read()in this version. This bug has been addressed in this version so emulators saved in this version would not have the compatibility issue in future version.
- A new sequential design criterion, called the Variance of Improvement for Global Fit (VIGF), is added to the package with the function
- The sampling from an existing candidate set
design()is changed from a random sampling to a conditioned Latin Hypercube sampling in
- The python environment is now automatically installed or invoked when a function from the package is executed. One does not need to run
init_py()to activate the required python environment but
init_py()is still useful to re-install and uninstall the underlying python environment. A
verbargument is added to
init_py()to switch on/off the trace information.
CRAN release: 2023-06-05
- The efficiency and speed of imputations involved in the training and predictions of DGP emulators are significantly improved (achieving roughly 3x faster training and imputations) by utilizing blocked Gibbs sampling that imputes latent variables layer-wise rather than node-wise. The blocked Gibbs sampling is now the default method for DGP emulator inference and can be changed back to the old node-wise approach by setting
blocked_gibbs = FALSEin
- One can now optimize GP components that are contained in the same layer of a DGP emulator in parallel during the DGP emulator training, using multiple cores by setting the new argument
dgp(). This option is useful and can accelerate the training speed when the input dimension is moderately large (in which case there is a large number of GP components to be optimized) and the optimization of GP components is computationally expensive, e.g., when
share = FALSEin which case input dimensions to individual GP components have different lengthscales.
- Thanks to @tjmckinley, a bug in
objectis an instance of the
dgpclass (that has been trimmed by
window()) is fixed.
- Thanks to @tjmckinley, some R memory issues due to the underlying Python implementations are rectified.
set_seed()function is added to ensure reproducible results from the package.
- A bug is fixed when candidate sets
y_candare provided to
- One can choose different color palettes using the new argument
style = 2.
set_linked_idx()allows constructions of different (D)GP emulators (in terms of different connections to the feeding layers) from a same (D)GP emulator.
CRAN release: 2023-02-08
- A bug is found in multi-core predictions in
objectis an instance of
xis a list. This bug has been fixed in this version.
- Thanks to @tjmckinley, an issue (
/usr/lib/x86_64-linux-gnu/libstdc++.so.6: version 'GLIBCXX_3.4.30' not found) encountered in Linux machines is fixed automatically during the execution of
dgp()allow users to specify the value of scale parameters and whether to estimate the parameters.
dgp()allow users to specify the bounds of lengthscales.
- The jointly robust prior (Gu, 2019) is implemented as the default inference approach for GP emulators in
- The default value of
gp()is changed from
0.1, and the default value for
gp()is changed from
nugget_est = FALSE.
- One can now specify the number of GP nodes in each layer (except for the final layer) of a DGP emulator via the
- Training data are now contained in the S3 classes
- The RMSEs (without the min-max normalization) of emulators are now contained in the S3 classes
lgpafter the execution of
window()function is added to trim the traces and obtain new point estimates of DGP model parameters for predictions.
- The min-max normalization can now be switched off in
plot()by setting the value of
- The default number of imputations
dgp()is changed from
30to better balance the uncertainty and the speed of DGP emulator predictions. A new function
set_imp()is made available to change the number of imputations of a trained DGP emulator so one can either achieve faster predictions by further reducing the number of imputations, or account for more imputation uncertainties by increasing the number of imputations, without re-training the emulator.
- The default number of imputations
continue()is set to
NULL, in which case the same number of imputations used in
objectwill be applied.
dgp()now specifies the nugget values for GP nodes in different layers rather than GP nodes in the final layer.
- The speed of predictions from DGP emulators with squared exponential kernels is significantly improved and is roughly 3x faster than the implementations in version
- The implementation of sequential designs (with two vignettes) of (D)GP emulators using different criterion is made available.
- Thanks to @tjmckinley, an internal reordering issue in
init_py()now allows users to reinstall and uninstall the underlying Python environment.
- A bug that occurs when a linked DGP emulator involves a DGP emulator with external inputs is fixed.
Intel SVMLwill now be installed with the Python environment automatically for Intel users for faster implementations.