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FastGP  

Efficiently Using Gaussian Processes with Rcpp and RcppEigen
View on CRAN: Click here


Download and install FastGP package within the R console
Install from CRAN:
install.packages("FastGP")

Install from Github:
library("remotes")
install_github("cran/FastGP")

Install by package version:
library("remotes")
install_version("FastGP", "1.2")



Attach the package and use:
library("FastGP")
Maintained by
Giri Gopalan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-07-23
Latest Update: 2016-02-02
Description:
Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).
How to cite:
Giri Gopalan (2015). FastGP: Efficiently Using Gaussian Processes with Rcpp and RcppEigen. R package version 1.2, https://cran.r-project.org/web/packages/FastGP. Accessed 22 Sep. 2026.
Previous versions and publish date:
1.0 (2015-07-23 06:58), 1.1.2 (2015-10-25 08:55), 1.1 (2015-07-27 23:16), 1.2 (2016-02-02 12:27), 1.3 (2026-04-29 08:40), (2026-09-20 05:10)
Other packages that cited FastGP R package
View FastGP citation profile
Other R packages that FastGP depends, imports, suggests or enhances
Complete documentation for FastGP
Functions, R codes and Examples using the FastGP R package
Some associated functions: ess . rcpp_matrix_ops . rcpp_rmvnorm . 
Some associated R codes: RcppExports.R . gpfuncs.R . rcpp_mvn.R .  Full FastGP package functions and examples
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