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
This package has been downloaded 269 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 9 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 4,059 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 42 downloads.
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