Package: ramps 0.6.18

ramps: Bayesian Geostatistical Modeling with RAMPS

Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples. Package performance is tuned for large spatial datasets.

Authors:Brian J Smith [aut, cre], Jun Yan [aut], Mary Kathryn Cowles [aut]

ramps_0.6.18.tar.gz
ramps_0.6.18.zip(r-4.7)ramps_0.6.18.zip(r-4.6)ramps_0.6.18.zip(r-4.5)
ramps_0.6.18.tgz(r-4.6-any)ramps_0.6.18.tgz(r-4.5-any)
ramps_0.6.18.tar.gz(r-4.7-any)ramps_0.6.18.tar.gz(r-4.6-any)
ramps_0.6.18.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
ramps/json (API)

# Install 'ramps' in R:
install.packages('ramps', repos = c('https://brian-j-smith.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • NURE - Dataset of USGS NURE Uranium Measurements
  • NURE.grid - Dataset of USGS NURE Uranium Measurements
  • simGrid - Dataset of Simulated Measurements from JSS Publication
  • simIowa - Dataset of Simulated Measurements from JSS Publication

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.41 score 26 scripts 348 downloads 19 exports 11 dependencies

Last updated from:af67bcc81f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK125
source / vignettesOK165
linux-release-x86_64OK113
macos-release-arm64OK116
macos-oldrel-arm64OK113
windows-develOK93
windows-releaseOK110
windows-oldrelOK90
wasm-releaseOK102

Exports:corRCauchycorRExpcorRExp2corRExpwrcorRExpwr2corRExpwr2DtcorRGauscorRGneitcorRLincorRMaterncorRSphercorRWaveDICexpand.chaingenUSStateGridgenUSStateSitesgeorampsparamramps.control

Dependencies:codadotCall64fieldslatticemapsMatrixnlmeRColorBrewerRcppspamviridisLite

Readme and manuals

Help Manual

Help pageTopics
Cauchy Spatial Correlation StructurecorRCauchy
Spatial Correlation Structure ClassescorRClasses
Exponential Spatial Correlation StructurecorRExp
Non-Separable Exponential Spatio-Temporal Correlation StructurecorRExp2
Powered Exponential Spatial Correlation StructurecorRExpwr
Non-Separable Powered Exponential Spatio-Temporal Correlation StructurecorRExpwr2
Non-Separable Temporally Integrated Powered Exponential Spatial Correlation StructurecorRExpwr2Dt
Gaussian Spatial Correlation StructurecorRGaus
Gneiting Spatial Correlation StructurecorRGneit
Linear Spatial Correlation StructurecorRLin
Matern Spatial Correlation StructurecorRMatern
Spherical Spatial Correlation StructurecorRSpher
Sine Wave Spatial Correlation StructurecorRWave
Deviance Information CriterionDIC DIC.ramps
Expand MCMC Samples for georamps Model Fitsexpand.chain
Generating a Grid over a US StategenUSStateGrid
Generating Random Sites in a US StategenUSStateSites
Bayesian Geostatistical Model Fitting with RAMPSgeoramps print.ramps
Dataset of USGS NURE Uranium MeasurementsNURE NURE.grid
Initialization of georamps Model Parametersparam
Posterior Spatial Distribution Plotsplot.predict.ramps plot.ramps
Prediction Method for georamps Model Fitspredict.ramps
Auxiliary for Controlling georamps Model Fittingramps.control
Dataset of Simulated Measurements from JSS PublicationsimGrid simIowa simJSS
Posterior Summaries of georamps Model Fitssummary.ramps
Subsetting of MCMC Sampler Resultswindow.predict.ramps window.ramps