convoSPAT: Convolution-Based Nonstationary Spatial Modeling

Fits convolution-based nonstationary Gaussian process models to point-referenced spatial data. The nonstationary covariance function allows the user to specify the underlying correlation structure and which spatial dependence parameters should be allowed to vary over space: the anisotropy, nugget variance, and process variance. The parameters are estimated via maximum likelihood, using a local likelihood approach. Also provided are functions to fit stationary spatial models for comparison, calculate the Kriging predictor and standard errors, and create various plots to visualize nonstationarity.

AuthorMark D. Risser [aut, cre]
Date of publication2016-10-21 10:32:36
MaintainerMark D. Risser <markdrisser@gmail.com>
LicenseMIT + file LICENSE
Version1.1.1
http://github.com/markdrisser/convoSPAT

View on CRAN

Man pages

Aniso_fit: Fit the stationary spatial model

evaluate_CV: Evaluation criteria

f_mc_kernels: Calculate mixture component kernel matrices.

kernel_cov: Calculate a kernel covariance matrix.

make_aniso_loglik: Constructor functions for local parameter estimation.

make_aniso_loglik_kappa: Constructor functions for local parameter estimation.

make_global_loglik1: Constructor functions for global parameter estimation.

make_global_loglik1_kappa: Constructor functions for global parameter estimation.

make_global_loglik2: Constructor functions for global parameter estimation.

make_global_loglik2_kappa: Constructor functions for global parameter estimation.

make_global_loglik3: Constructor functions for global parameter estimation.

make_global_loglik3_kappa: Constructor functions for global parameter estimation.

make_global_loglik4_kappa: Constructor functions for global parameter estimation.

mc_N: Calculate local sample sizes.

NSconvo_fit: Fit the nonstationary spatial model

NSconvo_sim: Simulate data from the nonstationary model.

plot.Aniso: Plot of the estimated correlations from the stationary model.

plot.NSconvo: Plot from the nonstationary model.

predict.Aniso: Obtain predictions at unobserved locations for the stationary...

predict.NSconvo: Obtain predictions at unobserved locations for the...

simdata: Simulated nonstationary dataset

summary.Aniso: Summarize the stationary model fit.

summary.NSconvo: Summarize the nonstationary model fit.

US.mc.grids: Mixture component grids for the western United States

USprecip97: Annual precipitation measurements from the western United...

US.prediction.locs: Prediction locations for the western United States

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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