Package: liGP
Title: Locally Induced Gaussian Process Regression
Version: 1.0.0
Date: 2021-06-29
Authors@R: c(person(c("D.", "Austin"), "Cole", role = c("aut", "cre"),
                     email = "austin.cole8@vt.edu"),
              person(c("Ryan", "B"), "Christianson", role = "cph"),
              person(c("Robert", "B."), "Gramacy", role = "cph",
                     email = "rbg@vt.edu"))
Maintainer: D. Austin Cole <austin.cole8@vt.edu>
Description: Performs locally induced approximate GP regression for large computer experiments and spatial datasets following Cole D.A., Christianson, R., Gramacy, R.B. (2021) Statistics and Computing, 31(3), 1-21, <arXiv:2008.12857>. The approximation is based on small local designs combined with a set of inducing points (latent design points) for predictions at particular inputs. Parallelization is supported for generating predictions over an immense out-of-sample testing set. Local optimization of the inducing points design is provided based on variance-based criteria. Inducing point template schemes, including scaling of space-filling designs, are also provided.
Depends: R (>= 3.4)
Imports: hetGP, laGP, doParallel, foreach
Suggests: lhs
License: LGPL
Encoding: UTF-8
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2021-06-30 17:43:09 UTC; austin
Author: D. Austin Cole [aut, cre],
  Ryan B Christianson [cph],
  Robert B. Gramacy [cph]
Repository: CRAN
Date/Publication: 2021-07-01 07:50:09 UTC
