Package: cocons 0.1.4

Federico Blasi

cocons: Covariate-Based Covariance Functions for Nonstationary Spatial Modeling

Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics. An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.

Authors:Federico Blasi [aut, cre], Reinhard Furrer [ctb]

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cocons.pdf |cocons.html
cocons/json (API)
NEWS

# Install 'cocons' in R:
install.packages('cocons', repos = c('https://blasif.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/blasif/cocons/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • holes - Holes Data Set
  • holes_bm - Holes with trend + multiple realizations Data Set
  • stripes - Stripes Data Set

On CRAN:

covariance-matrixcppestimationgaussian-processeslarge-datasetnonstationarityoptimizationprediction

5.23 score 2 stars 1 scripts 259 downloads 37 exports 13 dependencies

Last updated 5 days agofrom:8d48bb32f8. Checks:OK: 1 WARNING: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-win-x86_64WARNINGNov 18 2024
R-4.5-linux-x86_64WARNINGNov 18 2024
R-4.4-win-x86_64WARNINGNov 18 2024
R-4.4-mac-x86_64WARNINGNov 18 2024
R-4.4-mac-aarch64WARNINGNov 18 2024
R-4.3-win-x86_64WARNINGNov 18 2024
R-4.3-mac-x86_64WARNINGNov 18 2024
R-4.3-mac-aarch64WARNINGNov 18 2024

Exports:cocococoOptimcocoPredictcocoSimcov_rnscov_rns_classiccov_rns_predcov_rns_tapercov_rns_taper_predgetAICgetBICgetBoundariesgetBoundariesV2getBoundariesV3getCIsgetCovMatrixgetCRPSgetDensityFromDeltagetDesignMatrixgetEstimsgetHessiangetLoglikgetLogScoregetModelListsgetModHessGetNeg2loglikelihoodGetNeg2loglikelihoodProfileGetNeg2loglikelihoodREMLGetNeg2loglikelihoodTaperGetNeg2loglikelihoodTaperProfilegetScalegetSpatEffectsgetSpatMeanis.formulaplotplotOptimInfosummary

Dependencies:BHdotCall64evaluatefieldshighrknitrmapsoptimParallelRcppspamviridisLitexfunyaml

cocons-vignette

Rendered fromcocons.Rnwusingutils::Sweaveon Nov 18 2024.

Last update: 2024-07-22
Started: 2024-07-22

Readme and manuals

Help Manual

Help pageTopics
Covariate-based Covariance Functions for Nonstationary Gaussian Processescocons-package cocons
Creates a coco S4 objectcoco
An S4 class to store informationcoco-class
Optimizer for coco objectscocoOptim
Prediction for coco objectscocoPredict
Marginal and conditional simulation of nonstationary Gaussian processescocoSim
Dense covariance function (difference parameterization)cov_rns
Dense covariance function (classic parameterization)cov_rns_classic
Dense covariance functioncov_rns_pred
Sparse covariance functioncov_rns_taper
Sparse covariance functioncov_rns_taper_pred
Retrieve AICgetAIC
Retrieve BICgetBIC
Simple build of boundariesgetBoundaries
Simple build of boundaries (v2)getBoundariesV2
Simple build of boundaries (v3)getBoundariesV3
Compute approximate confidence intervals for a coco objectgetCIs
Covariance matrix for "coco" classgetCovMatrix
Based on a set of predictions computes the Continuous Ranked Probability ScoregetCRPS
Based on a specific taper scale (delta), retrieves the density of the covariance matrix.getDensityFromDelta
Create an efficient design matrix based on a list of aspect modelsgetDesignMatrix
Retrieve estimates from a fitted coco objectgetEstims
getHessiangetHessian
Retrieve the loglikelihood valuegetLoglik
Based on a set of predictions computes the Log-ScoregetLogScore
Builds the necessary input for building covariance matricesgetModelLists
Retrieves the modified inverse of the hessiangetModHess
GetNeg2loglikelihoodGetNeg2loglikelihood
GetNeg2loglikelihoodProfileGetNeg2loglikelihoodProfile
GetNeg2loglikelihoodREMLGetNeg2loglikelihoodREML
GetNeg2loglikelihoodTaperGetNeg2loglikelihoodTaper
GetNeg2loglikelihoodTaperProfileGetNeg2loglikelihoodTaperProfile
Fast and simple standardization for the design matrix.getScale
Evaluates the spatially-varying functions from a coco object at locsgetSpatEffects
Computes the spatial mean of a (fitted) coco objectgetSpatMean
Holes Data Setholes
Holes with trend + multiple realizations Data Setholes_bm
check whether an R object is a formulais.formula
Plot Method for coco objectsplot,coco,missing-method plot,coco-method
Plot log info detailedplotOptimInfo
Show Method for Coco Classshow show,coco-method
Stripes Data Setstripes
Summary Method for Coco Classsummary summary,coco-method