Package: varycoef 0.3.4
Jakob A. Dambon
varycoef: Modeling Spatially Varying Coefficients
Implements a maximum likelihood estimation (MLE) method for estimation and prediction of Gaussian process-based spatially varying coefficient (SVC) models (Dambon et al. (2021a) <doi:10.1016/j.spasta.2020.100470>). Covariance tapering (Furrer et al. (2006) <doi:10.1198/106186006X132178>) can be applied such that the method scales to large data. Further, it implements a joint variable selection of the fixed and random effects (Dambon et al. (2021b) <doi:10.1080/13658816.2022.2097684>). The package and its capabilities are described in (Dambon et al. (2021c) <arxiv:2106.02364>).
Authors:
varycoef_0.3.4.tar.gz
varycoef_0.3.4.tar.gz(r-4.5-noble)varycoef_0.3.4.tar.gz(r-4.4-noble)
varycoef_0.3.4.tgz(r-4.4-emscripten)varycoef_0.3.4.tgz(r-4.3-emscripten)
varycoef.pdf |varycoef.html✨
varycoef/json (API)
| # Install 'varycoef' in R: |
| install.packages('varycoef', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jakobdambon/varycoef/issues
Last updated 2 years agofrom:5583d103ef. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 10 2024 |
| R-4.5-linux | OK | Dec 10 2024 |
Exports:check_cov_lowercov_parGLS_cholinit_bounds_optimnlocssample_SVCdataSVC_mleSVC_mle_controlSVC_selectionSVC_selection_control
Dependencies:backportsBBmisccheckmateclicodetoolscolorspacedata.tabledotCall64fansifarverfastmatchforeachggplot2glmnetgluegtableisobanditeratorslabelinglatticelhslifecyclemagrittrMASSMatrixmgcvmlrmlrMBOmunsellnlmeoptimParallelparallelMapParamHelperspbapplypillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppEigenrlangscalesshapesmoofspamstringisurvivaltibbleutf8vctrsviridisLitewithrXML
