Package: npsp 0.7-14
npsp: Nonparametric Spatial Statistics
Multidimensional nonparametric spatial (spatio-temporal) geostatistics. S3 classes and methods for multidimensional: linear binning, local polynomial kernel regression (spatial trend estimation), density and variogram estimation. Nonparametric methods for simultaneous inference on both spatial trend and variogram functions (for spatial processes). Nonparametric residual kriging (spatial prediction). For details on these methods see, for example, Fernandez-Casal and Francisco-Fernandez (2014) <doi:10.1007/s00477-013-0817-8> or Castillo-Paez et al. (2019) <doi:10.1016/j.csda.2019.01.017>.
Authors:
npsp_0.7-14.tar.gz
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npsp.pdf |npsp.html✨
npsp/json (API)
NEWS
| # Install 'npsp' in R: |
| install.packages('npsp', repos = c('https://rubenfcasal.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/rubenfcasal/npsp/issues
- aquifer - Wolfcamp aquifer data
- earthquakes - Earthquake data
- precipitation - Precipitation data
geostatisticsspatial-data-analysisstatistics
Last updated 13 days agofrom:5c15559dbf. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 09 2024 |
| R-4.5-win-x86_64 | OK | Nov 09 2024 |
| R-4.5-linux-x86_64 | OK | Nov 09 2024 |
| R-4.4-win-x86_64 | OK | Nov 09 2024 |
| R-4.4-mac-x86_64 | OK | Nov 09 2024 |
| R-4.4-mac-aarch64 | OK | Nov 09 2024 |
| R-4.3-win-x86_64 | OK | Nov 09 2024 |
| R-4.3-mac-x86_64 | OK | Nov 09 2024 |
| R-4.3-mac-aarch64 | OK | Nov 09 2024 |
Exports:.cpu.time.inias.bin.dataas.bin.denas.data.gridas.spas.variogramas.variomodelas.vgmbin.denbinningcoordscoordvaluescovarcpu.timedata.griddisc.sbfitsvar.sb.isogrid.parh.cvhcv.datahot.colorsinterpis.data.gridjet.colorskappasbkriging.simplelocpollocpolhcvmasknp.dennp.fitgeonp.geonp.krigingnp.svarnp.svarisonp.svariso.corrnp.svariso.hcvnpsp.tolerancerulerule.binningrule.svarscattersplotscolorsimagesperspsplotspointssvsvar.binsvar.gridsvarisosvarmodsvarmod.sb.isosvarmodelsvarcovvgm.tab.svarmod
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| npsp: Nonparametric spatial (geo)statistics | npsp-package npsp |
| Wolfcamp aquifer data | aquifer |
| data.grid-class methods | as.data.frame.data.grid as.data.grid as.data.grid.SpatialGridDataFrame |
| Convert npsp object to sp object | as.sp as.sp.data.grid as.sp.grid.par |
| Linear binning for density estimation | as.bin.den as.bin.den.bin.den as.bin.den.data.grid bin.den bin.den-class |
| Linear binning | as.bin.data as.bin.data.bin.data as.bin.data.data.grid as.bin.data.SpatialGridDataFrame bin.data bin.data-class binning |
| (spatial) coordinates | coords coords.data.grid coords.grid.par |
| Coordinate values | coordvalues coordvalues.data.grid coordvalues.grid.par |
| Covariance values | covar covar.np.svar covar.svarmod |
| Total and partial CPU time used | cpu.time |
| Gridded data (S3 class "data.grid") | data.grid data.grid-class |
| Discretization nodes of a Shapiro-Botha variogram model | disc.sb |
| Earthquake data | earthquakes |
| Fit an isotropic Shapiro-Botha variogram model | fitsvar fitsvar-class fitsvar.sb.iso |
| Grid parameters (S3 class "grid.par") | grid.par grid.par-class |
| Cross-validation methods for bandwidth selection | h.cv h.cv.bin.data h.cv.bin.den h.cv.svar.bin hcv.data |
| Fast linear interpolation of a regular grid | interp interp.data.grid interp.grid.par predict.locpol.bin predict.np.den |
| Coefficients of an extended Shapiro-Botha variogram model | kappasb |
| Local polynomial estimation | locpol locpol.bin locpol.bin-class locpol.bin.data locpol.bin.den locpol.default locpol.svar.bin locpolhcv |
| Mask methods | mask mask.bin.data mask.bin.den mask.data.grid mask.default mask.locpol.bin |
| Local polynomial density estimation | np.den np.den-class np.den.bin.data np.den.bin.den np.den.default np.den.svar.bin |
| Fit a nonparametric geostatistical model | np.fitgeo np.fitgeo.default np.fitgeo.fitgeo np.fitgeo.locpol.bin |
| Nonparametric geostatistical model (S3 class "np.geo") | fitgeo-class np.geo np.geo-class, |
| Nonparametric (residual) kriging | kriging kriging.simple np.kriging np.kriging.default np.kriging.np.geo |
| Local polynomial estimation of the semivariogram | iso.np.svar np.svar np.svar-class np.svar.default np.svar.svar.bin np.svariso np.svariso.corr np.svariso.hcv |
| Interface to package "geoR" | as.variogram as.variogram.np.svar as.variogram.svar.bin as.variomodel as.variomodel.svarmod npsp-geoR variogram variomodel |
| Interface to package "gstat" | as.vgm as.vgm.sb.iso as.vgm.svarmod as.vgm.variomodel npsp-gstat vgm.tab.svarmod |
| npsp Tolerances | npsp.tolerance |
| Plot a nonparametric geostatistical model | plot.fitgeo |
| Precipitation data | precipitation |
| R Graphics for gridded data | contour.data.grid image.data.grid persp.data.grid rgraphics |
| npsp Rules | rule rule.binning rule.binning.default rule.svar rule.svar.bin.den rule.svar.default |
| Exploratory scatter plots | scattersplot scattersplot.default scattersplot.SpatialPointsDataFrame |
| Image plot with a color scale | plot.np.den simage simage.data.grid simage.default |
| Perspective plot with a color scale | spersp spersp.data.grid spersp.default |
| Utilities for plotting with a color scale | hot.colors jet.colors scolor splot |
| Scatter plot with a color scale | spoints spoints.data.grid spoints.default spoints.SpatialPointsDataFrame |
| Evaluate a semivariogram model | sv sv.default sv.sb.iso sv.svar.grid sv.svarmod |
| Linear binning of semivariances | iso.svar svar.bin svar.bin-class svar.bin.default svariso |
| Discretize a (semi)variogram model | svar.grid svar.grid.svarmod |
| Plot a semivariogram object | plot.fitsvar plot.np.svar plot.svar.bin svar.plot |
| Define a (semi)variogram model | sb.iso-class svarmod svarmod.sb.iso svarmodels |
| Covariance matrix | varcov varcov.isotropic varcov.np.svar |
