The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. 'FRK' also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe 'FRK' in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie <doi:10.18637/jss.v108.i10> describe 'FRK' in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.
Version: | 2.3.1 |
Depends: | R (≥ 3.5.0) |
Imports: | digest, dplyr, fmesher, ggplot2, grDevices, Hmisc (≥ 4.1), Matrix, methods, plyr, Rcpp (≥ 0.12.12), sp, spacetime, sparseinv, statmod, stats, TMB, utils, ggpubr, reshape2, scales |
LinkingTo: | Rcpp, TMB, RcppEigen |
Suggests: | covr, dggrids, gstat, knitr, lme4, mapproj, parallel, sf, spdep, splancs, testthat, verification |
Published: | 2024-07-16 |
DOI: | 10.32614/CRAN.package.FRK |
Author: | Andrew Zammit-Mangion [aut, cre], Matthew Sainsbury-Dale [aut] |
Maintainer: | Andrew Zammit-Mangion <andrewzm at gmail.com> |
BugReports: | https://github.com/andrewzm/FRK/issues/ |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: | https://andrewzm.github.io/FRK/, https://github.com/andrewzm/FRK/ |
NeedsCompilation: | yes |
Additional_repositories: | https://andrewzm.github.io/dggrids-repo/ |
Citation: | FRK citation info |
In views: | Spatial |
CRAN checks: | FRK results |
Reference manual: | FRK.pdf |
Vignettes: |
Introduction to FRK Tutorial on modelling spatial and spatio-temporal non-Gaussian data with FRK |
Package source: | FRK_2.3.1.tar.gz |
Windows binaries: | r-devel: FRK_2.3.1.zip, r-release: FRK_2.3.1.zip, r-oldrel: FRK_2.3.1.zip |
macOS binaries: | r-release (arm64): FRK_2.3.1.tgz, r-oldrel (arm64): FRK_2.3.1.tgz, r-release (x86_64): FRK_2.3.1.tgz, r-oldrel (x86_64): FRK_2.3.1.tgz |
Old sources: | FRK archive |
Reverse imports: | IDE |
Please use the canonical form https://CRAN.R-project.org/package=FRK to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.