
tgp
 Referenced in 35 articles
[sw07921]
 Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps...

laGP
 Referenced in 20 articles
[sw14043]
 laGP: Local Approximate Gaussian Process Regression. Performs approximate GP regression for large computer experiments...

GPML
 Referenced in 37 articles
[sw12890]
 wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified ... functions are supported including Gaussian and heavytailed for regression as well as others suitable...

Kernlab
 Referenced in 88 articles
[sw07926]
 classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods kernlab includes ... Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver...

George
 Referenced in 16 articles
[sw29786]
 flexible Python library for Gaussian Process (GP) Regression. A full introduction to the theory...

invGauss
 Referenced in 114 articles
[sw11207]
 invGauss: Threshold regression that fits the (randomized drift) inverse Gaussian distribution to survival data. invGauss ... fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described ... Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based...

acebayes
 Referenced in 11 articles
[sw20243]
 optimisation steps. At each step, a Gaussian process regression model is used to approximate...

SPOT
 Referenced in 76 articles
[sw06347]
 includes methods for tuning based on classical regression and analysis of variance techniques; treebased ... such as CART and random forest; Gaussian process models (Kriging), and combinations of di erent...

spTimer
 Referenced in 15 articles
[sw24237]
 using [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian AutoRegressive (AR) Models...

gprege
 Referenced in 6 articles
[sw31093]
 expressed gene expression timecourses through Gaussian process regression”. The software fits two GPs with...

OPELM
 Referenced in 22 articles
[sw12171]
 detail and then applied to several regression and classification problems. Results for both computational time ... machine (SVM), and Gaussian process (GP). As the experiments for both regression and classification illustrate...

gptk
 Referenced in 5 articles
[sw24250]
 implements a generalpurpose toolkit for Gaussian process regression with a variety of covariance functions...

lineqGPR
 Referenced in 2 articles
[sw31421]
 package lineqGPR: Gaussian Process Regression Models with Linear Inequality Constraints. Gaussian processes regression models with...

GPareto
 Referenced in 3 articles
[sw28711]
 Pareto Front Estimation and Optimization. Gaussian process regression models, a.k.a. Kriging models, are applied...

GPFDA
 Referenced in 6 articles
[sw14770]
 analysis. Use functional regression as the mean structure and Gaussian Process as the covariance structure...

muq
 Referenced in 2 articles
[sw33064]
 internally and through NLOPT); Regression (including Gaussian process regression...

GPLP
 Referenced in 1 article
[sw08483]
 local and parallel computation toolbox for Gaussian process regression. This paper presents the gettingstarted ... local and parallel computation toolbox for Gaussian process regression (GPLP), an open source software package...

spNNGP
 Referenced in 3 articles
[sw31449]
 Datasets using Nearest Neighbor Gaussian Processes. Fits univariate Bayesian spatial regression models for large datasets...

GoGP
 Referenced in 1 article
[sw23912]
 GoGP: Fast Online Regression with Gaussian Processes ... most current challenging problems in Gaussian process regression (GPR) is to handle largescale datasets ... paper, we introduce a novel online Gaussian process model that could scale with massive datasets ... based on alternative representation of the Gaussian process under geometric and optimization views, hence termed...

krisp
 Referenced in 1 article
[sw14717]
 kriging based regression (also known as Gaussian process regression) and optimization of deterministic simulators...