Train and Apply a Gaussian Stochastic Process Model

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, . 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", .

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