
spBayes
 Referenced in 336 articles
[sw10160]
 template encompassing a wide variety of Gaussian spatial process models for univariate as well...

INLA
 Referenced in 37 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 logGaussian Cox processes...

laGP
 Referenced in 21 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...

convoSPAT
 Referenced in 5 articles
[sw15289]
 Fits convolutionbased nonstationary Gaussian process models to pointreferenced spatial data. The nonstationary covariance...

spTimer
 Referenced in 15 articles
[sw24237]
 spatially predicts and temporally forecasts large amounts of spacetime data using [1] Bayesian Gaussian ... Process (GP) Models, [2] Bayesian AutoRegressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes...

spTDyn
 Referenced in 3 articles
[sw32493]
 forecasts spacetime 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 spatiallyvarying 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...

varycoef
 Referenced in 1 article
[sw38767]
 package varycoef: Modeling Spatially Varying Coefficients. Implements a maximum likelihood estimation (MLE) method ... estimation and prediction of Gaussian processbased spatially varying coefficient (SVC) models (Dambon...

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...

GPfit
 Referenced in 19 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 multistart...

spBFA
 Referenced in 1 article
[sw31476]
 parametric prior, the probit stickbreaking process. Areal spatial data is modeled using a conditional ... pointreferenced spatial data is treated using a Gaussian process. The response variable...

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

HOSIM
 Referenced in 7 articles
[sw38402]
 simulate complex nonlinear and nonGaussian systems. HOSIM is an alternative to the current ... upon new highorder spatial connectivity measures, termed highorder spatial cumulants. The HOSIM algorithm ... implements a sequential simulation process, where local conditional distributions are generated using weighted orthonormal Legendre...

aws4SPM
 Referenced in 1 article
[sw04035]
 data in a preprocessing 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 leastsquares methods for processing timeseries 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 spatialtemporal 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 loglikelihood function requires ... surface temperature dataset. The performance evaluation involves spatial datasets with up to 250K observations...

TomoPhantom
 Referenced in 2 articles
[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 ... multithreaded implementation, volumetric phantoms of high spatial resolution can be obtained with computational efficiency...

ANSYS
 Referenced in 665 articles
[sw00044]
 ANSYS offers a comprehensive software suite that spans...