##change / RobustGaSP 0.6.6 * fix the problem that enables fixing range parameter and nugget for isotropic input space for ppgasp * fix the predictive variance for ppgasp with the zero mean scenario * fix the issues in some Rd files ##change / RobustGaSP 0.6.5 * fix the problem relevant to the "if" argument * fix the problem in C++ code relevant to using & in Eigen objects for removing the warning from the Linux Debian system ##change / RobustGaSP 0.6.4 *allow user to predict a subset of the locations in ppgasp for prediction ##change / RobustGaSP 0.6.3 *fix the issue related to STRICT_R_HEADERS in Rcpp.h *fix the issue related to interval_data=T and interval_data=F in predict() ##change / RobustGaSP 0.6.2 *fix the data length problem in the example of predict.ppgasp.Rd *remove printing too much from examples *fix a reference problem in higdon.1.data.Rd ##change / RobustGaSP 0.6.1 *update the description of the kernel in the likelihood functions *correct the typo of estimating sigma_2_0_hat for method mle when mean is zero *change the setting of the lower_bound=F in rgasp() and ppgasp(); now we will not search lower bound for inverse range parameters if lower_bound=F. The first initial values for optimization is changed ##change / RobustGaSP 0.6.0 *Implement the periodic folding technique of exponential and Gaussian kernel for periodic inputs *Implement the profile likelihood estimator and marginal likelihood estimator *Implement the isotropic kernel with Euclidean distance *Fix the issue when the input is not a matrix *Fix the issue to check the output is not all the same for particular location in PP GaSP *Allow lower95 and upper95 predictive interval for both data and mean *Allow sample the predictive process without noise *Implement 'Neld-Mead' and 'Brent' methods for optimization *Include user-specified R0 and r0 when estimating the parameters ##change / RobustGaSP 0.5.7 *Implement the Parallel partial Gaussian stochastic process through the ppgasp() function. Implements the ppgasp-class, predppgasp-class ppgasp.predict(), show.ppgasp(). *Include the data set humanity_model.rda. The document of the ppgasp() function uses it as an example. *Implement the separable_multi_kernel() function to allow users to specify a different kernel function for each dimension of the input *For the rgasp() and ppgasp() functions, now users can specify the number of initial values of the kernel parameters to be optimized and specify the initial values to be optimizers through the arguments “num_initial_values” and “initial_values”, respectively. * Imports the R package nloptr as the main optimizer in rgasp() and ppgasp() depend on the lbfgs() function in nloptr. ## change / RobustGaSP 0.5.6 *Implement two functions called simulate() and plot() to replace Sample() and Plot() functions, respectively. *Reimplemented the demo-functions, including higdon.1.data(), limetal.2.data(), dettepepel.3.data(), borehole() and friedman.5.data() *The license is modified to GPL-2 | GPL-3. *change the slight typo of the reference prior when the emulator has zero mean *Add two references. The main paper is recently published in Annals of Statistics and another paper is on arXiv. ## RobustGaSP 0.5.5 * Update the maximum num of evaluation in the optimizer by nloptr::lbfgs(). The maximum num of evaluation should increase with the increase of the dimension of the inputs * Implement leave_one_out_rgasp() function for model checking * Allow user to choose whether we use an S3 or S4 method for prediction ## RobustGaSP 0.5.4 * Add the choice of the zero mean GaSP * Change the setting of nloptr to be nl.info=F. Delete sink() * Correct printing of the noise ## change / RobustGaSP 0.5.2 * Bugs corrected in rgasp() about the sink() function when the optimization step fails * Typo corrected in printing out the nugget value after the optimization is done * Allow the argument design in rgasp() to be initialized as a sequence (previously it has to be the matrix type) * Change of the maintainer’s Email address ## change / RobustGaSP 0.5 * Implement a function ‘findInertInputs’ to identify inert inputs * Implement a function ‘Sample’ to sample from the predictive distribution * Modify the printing information of the function ‘rgasp’ by removing the automatic printing message from the function of ‘nloptr::lbfgs’ and adding other printing information * Change the parameter ‘beta’ to ‘beta_hat’ in rgasp class * Fix the bug in the function ‘predict’ of the value ‘sd’