• spBayes

  • Referenced in 254 articles [sw10160]
  • template encompassing a wide variety of Gaussian spatial process models for univariate as well...
  • SPOT

  • Referenced in 71 articles [sw06347]
  • such as CART and random forest; Gaussian process models (Kriging), and combinations of di erent...
  • Kernlab

  • Referenced in 59 articles [sw07926]
  • Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver...
  • ParEGO

  • Referenced in 43 articles [sw10968]
  • latin hypercube and updates a Gaussian processes surrogate model of the search landscape after every...
  • tgp

  • Referenced in 27 articles [sw07921]
  • package tgp: Bayesian treed Gaussian process models. Bayesian nonstationary, semiparametric nonlinear regression and design ... treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also...
  • GPML

  • Referenced in 25 articles [sw12890]
  • Gaussian processes for machine learning (GPML) toolbox. The GPML toolbox provides a wide range ... functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance ... ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well...
  • invGauss

  • Referenced in 86 articles [sw11207]
  • data. invGauss fits the (randomized drift) inverse Gaussian distribution to survival data. The model ... Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based ... Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution...
  • LS-SVMlab

  • Referenced in 23 articles [sw07367]
  • closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual...
  • mlegp

  • Referenced in 14 articles [sw08213]
  • package mlegp: Maximum Likelihood Estimates of Gaussian Processes. Maximum likelihood Gaussian process modeling for univariate...
  • GPy

  • Referenced in 14 articles [sw14302]
  • Gaussian Process (GP) framework written in python, from the Sheffield machine learning group. Gaussian processes...
  • GPfit

  • Referenced in 12 articles [sw14044]
  • package GPfit: Gaussian Processes Modeling. A computationally stable approach of fitting a Gaussian Process ... model to a deterministic simulator. Gaussian process (GP) models are commonly used statistical metamodels...
  • OP-ELM

  • Referenced in 18 articles [sw12171]
  • support vector machine (SVM), and Gaussian process (GP). As the experiments for both regression...
  • GPstuff

  • Referenced in 17 articles [sw12867]
  • toolbox is a versatile collection of Gaussian process models and computational tools required for inference...
  • spectralGP

  • Referenced in 11 articles [sw08081]
  • package spectralGP: Approximate Gaussian processes using the Fourier basis. Routines for creating, manipulating, and performing ... Bayesian inference about Gaussian processes in one and two dimensions using the Fourier basis approximation...
  • GPS-ABC

  • Referenced in 11 articles [sw16117]
  • Gaussian Process Surrogate Approximate Bayesian Computation. Scientists often express their understanding of the world through ... obtained from every simulation in a Gaussian process which acts as a surrogate function...
  • INLA

  • Referenced in 17 articles [sw07535]
  • toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA) This ... models that are based on log-Gaussian Cox processes and include local interaction in these...
  • lgcp

  • Referenced in 10 articles [sw22045]
  • Inference with Spatio-Temporal Log-Gaussian Cox Processes. This paper introduces an R package ... temporal prediction and forecasting for log-Gaussian Cox processes. The main computational tool for these...
  • spTimer

  • Referenced in 8 articles [sw24237]
  • space-time data using [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive ... Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal...
  • George

  • Referenced in 7 articles [sw29786]
  • fast and flexible Python library for Gaussian Process (GP) Regression. A full introduction ... theory of Gaussian Processes is beyond the scope of this documentation but the best resource...
  • MAGP

  • Referenced in 7 articles [sw14019]
  • MAGP (Maximum Analysis of Gaussian Processes). Numerical bounds for the distributions of the maxima ... some one- and two-parameter Gaussian processes. We consider the class of real-valued stochastic ... processes indexed on a compact subset of ℝ or ℝ 2 with almost surely absolutely ... very accurate, in the Gaussian case, for levels that are not large. We also present...