Package: spNNGP 1.0.1
Andrew Finley
spNNGP: Spatial Regression Models for Large Datasets using Nearest Neighbor Gaussian Processes
Fits univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Banerjee (2022) <doi:10.18637/jss.v103.i05>, Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) <doi:10.1080/10618600.2018.1537924>, and Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091>.
Authors:
spNNGP_1.0.1.tar.gz
spNNGP_1.0.1.tar.gz(r-4.5-noble)spNNGP_1.0.1.tar.gz(r-4.4-noble)
spNNGP_1.0.1.tgz(r-4.4-emscripten)spNNGP_1.0.1.tgz(r-4.3-emscripten)
spNNGP.pdf |spNNGP.html✨
spNNGP/json (API)
| # Install 'spNNGP' in R: |
| install.packages('spNNGP', repos = '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 6 months agofrom:97c5bc299b. Checks:2 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 21 2025 |
| R-4.5-linux-x86_64 | OK | Feb 21 2025 |
Exports:PGLogitspConjNNGPspDiagspNNGP
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Forest Canopy Height from NASA Goddard's LiDAR Hyperspectral and Thermal (G-LiHT) over Bonanza Creek Experimental Forest | BCEF |
| Occurrence of Tsuga canadensis (Eastern hemlock) in Michigan | MI_TSCA |
| Function for Fitting Logistic Models using Polya-Gamma Latent Variables | PGLogit |
| Function for prediction at new locations using 'NNGP' models. | predict.NNGP |
| Methods for spDiag Object | print.spDiag |
| Function for Fitting Univariate Bayesian Conjugate Spatial Regression Models | spConjNNGP |
| Model fit diagnostics | spDiag |
| Function for Fitting Univariate Bayesian Spatial Regression Models | spNNGP |
| Methods for NNGP and Derived Objects | fitted.NNGP print.NNGP print.predict.NNGP residuals.NNGP summary.NNGP |
| Methods for PGLogit Object | fitted.PGLogit print.PGLogit residuals.PGLogit summary.PGLogit |
