Package: meshed 0.2.3
Michele Peruzzi
meshed: Bayesian Regression with Meshed Gaussian Processes
Fits Bayesian regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889>, Peruzzi, Banerjee, Dunson, and Finley (2021) <arxiv:2101.03579>, Peruzzi and Dunson (2022) <arxiv:2201.10080>. Funded by ERC grant 856506 and NIH grant R01ES028804.
Authors:
meshed_0.2.3.tar.gz
meshed_0.2.3.tar.gz(r-4.5-noble)meshed_0.2.3.tar.gz(r-4.4-noble)
meshed_0.2.3.tgz(r-4.4-emscripten)meshed_0.2.3.tgz(r-4.3-emscripten)
meshed.pdf |meshed.html✨
meshed/json (API)
NEWS
| # Install 'meshed' in R: |
| install.packages('meshed', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:ccd7e6a43c. Checks:OK: 1 NOTE: 1. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 17 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 17 2024 |
Exports:rmeshedgpspmeshedsummary_list_meansummary_list_q
Dependencies:clidplyrfansiFNNgenericsgluelifecyclemagrittrpillarpkgconfigR6RcppRcppArmadillorlangtibbletidyselectutf8vctrswithr
MGPs for multivariate data at irregularly spaced locations
Rendered frommultivariate_irregular.Rmdusingknitr::rmarkdownon Oct 17 2024.Last update: 2022-09-19
Started: 2021-06-11
MGPs for univariate data at irregularly spaced locations
Rendered fromunivariate_irregular.Rmdusingknitr::rmarkdownon Oct 17 2024.Last update: 2022-09-19
Started: 2021-06-11
MGPs for univariate spatial gridded data
Rendered fromunivariate_gridded.Rmdusingknitr::rmarkdownon Oct 17 2024.Last update: 2022-09-19
Started: 2021-06-11
MGPs for univariate non-Gaussian data at irregularly spaced locations
Rendered fromunivariate_irregular_poisson.Rmdusingknitr::rmarkdownon Oct 17 2024.Last update: 2022-09-19
Started: 2021-06-11
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Methods for fitting models based on Meshed Gaussian Processes (MGPs) | meshed-package meshed |
| Posterior predictive sampling for models based on MGPs | predict.spmeshed |
| Prior sampling from a Meshed Gaussian Process | rmeshedgp |
| Posterior sampling for models based on MGPs | spmeshed |
| Arithmetic mean of matrices in a list | summary_list_mean |
| Quantiles of elements of matrices in a list | summary_list_q |