GPvecchia
Scalable Gaussian-Process Approximations
Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) doi:10.48550/arXiv.1708.06302. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) doi:10.48550/arXiv.1706.02205 and MaxMin ordering proposed in Guinness (2018) doi:10.48550/arXiv.1609.05372.
- Version0.1.7
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- Last release03/12/2024
Documentation
Team
Marcin Jurek
Matthias Katzfuss
Show author detailsRolesAuthorDaniel Zilber
Show author detailsRolesAuthorWenlong Gong
Show author detailsRolesAuthorJoe Guinness
Show author detailsRolesContributorJingjie Zhang
Show author detailsRolesContributorFlorian Schaefer
Show author detailsRolesContributor
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports6 packages
- Suggests4 packages
- Linking To3 packages