Package: SpTe2M 1.0.3
Kai Yang
SpTe2M: Nonparametric Modeling and Monitoring of Spatio-Temporal Data
Spatio-temporal data have become increasingly popular in many research fields. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) <doi:10.1002/sim.7622>, the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) <doi:10.1002/sim.8315>, the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) <doi:10.1007/s10463-021-00787-2>, the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) <doi:10.1002/sim.9150> that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2023) <doi:10.1080/00224065.2022.2081104>, and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) <doi:10.1080/24725854.2019.1696496>.
Authors:
SpTe2M_1.0.3.tar.gz
SpTe2M_1.0.3.tar.gz(r-4.5-noble)SpTe2M_1.0.3.tar.gz(r-4.4-noble)
SpTe2M_1.0.3.tgz(r-4.4-emscripten)SpTe2M_1.0.3.tgz(r-4.3-emscripten)
SpTe2M.pdf |SpTe2M.html✨
SpTe2M/json (API)
# Install 'SpTe2M' in R: |
install.packages('SpTe2M', 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 1 years agofrom:aa2c6666d8. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 24 2024 |
R-4.5-linux-x86_64 | OK | Oct 24 2024 |
Exports:cv_mspemod_cvspte_covestspte_decorspte_meanestspte_semiparmregsptemnt_cusumsptemnt_ewmacsptemnt_ewsl
Dependencies:base64encbslibcachemclicodetoolscolorspacedigestevaluatefansifarverfastmapfontawesomeforeachfsggplot2glmnetgluegtablehighrhtmltoolsisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrmapprojmapsMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownsassscalesshapesurvivaltibbletinytexutf8vctrsviridisLitewithrxfunyaml
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Nonparametric Modeling and Monitoring of Spatio-Temporal Data | SpTe2M-package SpTe2M |
Cross-validation mean squared prediction error | cv_mspe |
Florida influenza-like illness data | ili_dat |
Modifed cross-validation for bandwidth selection | mod_cv |
PM2.5 concentration data | pm25_dat |
A simulated spatio-temporal dataset | sim_dat |
Estimate the spatio-temporal covariance function | spte_covest |
Decorrelate the spatio-temporal data | spte_decor |
Estimate the spatio-temporal mean function | spte_meanest |
Fit the semiparametric spatio-temporal model | spte_semiparmreg |
Online spatio-temporal process monitoring by a CUSUM chart | sptemnt_cusum |
Spatio-temporal process monitoring using covariate information | sptemnt_ewmac |
Spatio-temporal process monitoring using exponentially weighted spatial LASSO | sptemnt_ewsl |