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RobustCalibration (version 0.5.6)

Robust Calibration of Imperfect Mathematical Models

Description

Implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang, 2018, Journal of Uncertainty Quantification; Mengyang Gu, Fangzheng Xie and Long Wang, 2022, Journal of Uncertainty Quantification; Mengyang Gu, Kyle Anderson and Erika McPhillips, 2023, Technometrics.

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Version

Install

install.packages('RobustCalibration')

Monthly Downloads

162

Version

0.5.6

License

GPL (>= 2)

Maintainer

Mengyang Gu

Last Published

July 15th, 2025

Functions in RobustCalibration (0.5.6)

Sample_delta

Sample the model discrepancy.
Sample_sigma_2_theta_m

Sample the variance and mean parameters.
post_sample_MS

Posterior sampling.
post_sample_no_discrepancy

Posterior sampling for the model with no discrepancy function.
rcalibration_MS

Setting up the robust Calibration model for multiple sources data
predict_separable_2dim_MS

Fast prediction when the test points lie on a 2D lattice for multiple sources of observations.
rcalibration_MS-class

Robust Calibration for multiple sources class
rcalibration

Setting up the robust Calibration model
predict

Prediction for the robust calibration model
predictobj.rcalibration_MS-class

Predictive results for the Robust Calibration class
predict_separable_2dim

Fast prediction when the test points lie on a 2D lattice.
separable_kernel

Product correlation matrix with the product form
predict_MS

Prediction for the robust calibration model for multiple sources
show

Show an Robust Calibration object.
rcalibration-class

Robust Calibration class
predictobj.rcalibration-class

Predictive results for the Robust Calibration class
Get_inv_all

Produce the inversion of the covariances of the model discrepancy and the measurement bias.
Log_marginal_post_delta

Natural Logorithm of the posterior of the discrepancy in model calibration with multiple sources with measurement bias.
Log_marginal_post_no_discrepancy

Natural Logorithm of the posterior with no discrepancy function.
Log_marginal_post

Natural Logorithm of the posterior.
post_sample_with_discrepancy

Posterior sampling for the model with a discrepancy function
Update_R_inv_y

Update the inverse of covariance multiplied by the outputs in the S-GaSP model.
Accept_proposal

Determine whether we accept the proposed poserior sample at one step of MCMC.
Chol_Eigen

Cholesky decomposition of a symmetric matrix.
Mogihammer

A geophysical model for the ground deformation in Kilauea.
Sample_sigma_2_theta_m_no_discrepancy

Sample the variance and mean parameters with no discrepancy function.
post_sample

Posterior sampling.
mathematical_model_eval

Evaluation of the mathmatical model at given observed inputs and calibration parameters.
Get_R_z_new

Cholesky decomposition of the covariance matrix in S-GaSP.
Get_R_new

Cholesky decomposition of the correlation matrix in GaSP.
RobustCalibration-package

tools:::Rd_package_title("RobustCalibration")