Description
Train and Apply a Gaussian Stochastic Process Model.
Description
Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or maximum a posteriori (MAP) estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, <doi:10.1214/ss/1177012413>. Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", <doi:10.1007/0-387-28014-6_14>.
README.md
GaSP: Train and Apply a Gaussian Stochastic Process Model
GaSP R package, created by William J. Welch and Yilin Yang.
See the documentation for the basic outline and some simple examples of GaSP functions, and see the vignette for a more detailed description of GaSP as well as some noteworthy implementation choices made by the authors.
Changelogs:
Version 1.0.1:
- Added a vignette for GaSP.
- Fixed memory leak in C functions.
- Minor bug fixes for error matrix console output and Fit C initialization when 'random_error = TRUE'.
Version 1.0.2:
- PROTECT bugs fixed
- Compilation warnings about function prototypes, declarations, arguments fixed
Version 1.0.3:
- More C compilation warnings fixed
- R class() comparison with string fixed
Version 1.0.4
- sprintf and vsprintf replaced by snprintf and vsnprintf, respectively
Version 1.0.5
- C format specifiers and type casts fixed for output messages
Version 1.0.6
- C types and type casts fixed.