• spBayes

  • Referenced in 315 articles [sw10160]
  • template encompassing a wide variety of Gaussian spatial process models for univariate as well...
  • INLA

  • Referenced in 23 articles [sw07535]
  • toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA) This ... spatial point pattern data. We consider models that are based on log-Gaussian Cox processes...
  • laGP

  • Referenced in 20 articles [sw14043]
  • Approximate Gaussian Process Regression. Performs approximate GP regression for large computer experiments and spatial datasets...
  • spNNGP

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

  • Referenced in 15 articles [sw24237]
  • spatially predicts and temporally forecasts large amounts of space-time data using [1] Bayesian Gaussian ... Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes...
  • convoSPAT

  • Referenced in 4 articles [sw15289]
  • Fits convolution-based nonstationary Gaussian process models to point-referenced spatial data. The nonstationary covariance...
  • spTDyn

  • Referenced in 2 articles [sw32493]
  • forecasts space-time data using Gaussian Process (GP): (1) spatially varying coefficient process models...
  • BayesNSGP

  • Referenced in 2 articles [sw30769]
  • shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves ... based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically ... furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley ... package, and posterior prediction for the Gaussian process at unobserved locations is provided...
  • ramps

  • Referenced in 4 articles [sw24248]
  • with RAMPS. Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling ... samples. Package performance is tuned for large spatial datasets...
  • spBFA

  • Referenced in 1 article [sw31476]
  • parametric prior, the probit stick-breaking process. Areal spatial data is modeled using a conditional ... point-referenced spatial data is treated using a Gaussian process. The response variable...
  • GPfit

  • Referenced in 16 articles [sw14044]
  • model to a deterministic simulator. Gaussian process (GP) models are commonly used statistical metamodels ... package GPfit. A novel parameterization of the spatial correlation function and a new multi-start...
  • BRISC

  • Referenced in 1 article [sw31761]
  • Inference on Spatial Covariances (BRISC) for large datasets using Nearest Neighbor Gaussian Processes detailed...
  • aws4SPM

  • Referenced in 1 article [sw04035]
  • data in a pre-processing step by a Gaussian filter. However, this comes ... resolution, which is especially disturbing at high spatial resolutions. In a series of recent papers...
  • TIMESAT

  • Referenced in 4 articles [sw27691]
  • data. Three different least-squares methods for processing time-series of satellite sensor data ... basis of harmonic functions and asymmetric Gaussian functions, respectively. The methods incorporate qualitative information ... Difference Vegetation Index data over Africa, giving spatially coherent images of seasonal parameters such...
  • GpGp

  • Referenced in 1 article [sw31760]
  • fitting and doing predictions with Gaussian process models using Vecchia’s (1988) approximation. Package also ... operations, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains...
  • ExaGeoStatR

  • Referenced in 2 articles [sw30063]
  • Geostatistics in R. Parallel computing in Gaussian process calculation becomes a necessity for avoiding computational ... with Geostatistics applications. The evaluation of the Gaussian log-likelihood function requires ... surface temperature dataset. The performance evaluation involves spatial datasets with up to 250K observations...
  • TomoPhantom

  • Referenced in 1 article [sw32603]
  • additive combinations of geometrical objects, such as, Gaussians, parabolas, cones, ellipses, rectangles and volumetric extensions ... benchmarking and testing of different image processing techniques. Specifically, tomographic reconstruction algorithms which employ ... multi-threaded implementation, volumetric phantoms of high spatial resolution can be obtained with computational efficiency...
  • ANSYS

  • Referenced in 654 articles [sw00044]
  • ANSYS offers a comprehensive software suite that spans...
  • ATLAS

  • Referenced in 197 articles [sw00056]
  • This paper describes the Automatically Tuned Linear Algebra...
  • CGAL

  • Referenced in 350 articles [sw00118]
  • The goal of the CGAL Open Source Project...