Package: CensSpatial 3.6

CensSpatial: Censored Spatial Models

It fits linear regression models for censored spatial data. It provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. It also offers graphical tools for assessing the fitted model. More details can be found in Ordonez et al. (2018) <doi:10.1016/j.spasta.2017.12.001>.

Authors:Alejandro Ordonez, Christian E. Galarza, Victor H. Lachos

CensSpatial_3.6.tar.gz
CensSpatial_3.6.zip(r-4.5)CensSpatial_3.6.zip(r-4.4)CensSpatial_3.6.zip(r-4.3)
CensSpatial_3.6.tgz(r-4.4-any)CensSpatial_3.6.tgz(r-4.3-any)
CensSpatial_3.6.tar.gz(r-4.5-noble)CensSpatial_3.6.tar.gz(r-4.4-noble)
CensSpatial_3.6.tgz(r-4.4-emscripten)CensSpatial_3.6.tgz(r-4.3-emscripten)
CensSpatial.pdf |CensSpatial.html
CensSpatial/json (API)

# Install 'CensSpatial' in R:
install.packages('CensSpatial', repos = c('https://joalor93.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/joalor93/censspatial/issues

Datasets:
  • Missouri - TCDD concentrations in Missouri (1971).
  • depth - Depths of a geological horizon.

On CRAN:

2.93 score 17 scripts 322 downloads 10 exports 41 dependencies

Last updated 2 years agofrom:eda79c90e9. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-winNOTENov 10 2024
R-4.5-linuxNOTENov 10 2024
R-4.4-winNOTENov 10 2024
R-4.4-macNOTENov 10 2024
R-4.3-winNOTENov 10 2024
R-4.3-macNOTENov 10 2024

Exports:algnaive12derivcormatrixderivQfundistmatrixlocalinfmeaspredgraphicspredSCLrspacensSAEMSCLSeminaive

Dependencies:BHcliexpmfansigenericsgeoRgluegmmGPArotationlatticelifecyclemagrittrMASSMatrixmnormtmomentsmsmmvtnormnlmenloptrnumDerivoptimxpillarpkgconfigpracmapsychrasterRcppRcppEigenrlangsandwichspsplancssurvivalterratibbletlrmvnmvttmvtnormutf8vctrszoo