Reference
Package: smile 1.1.0
smile: Spatial Misalignment: Interpolation, Linkage, and Estimation
Provides functions to estimate, predict and interpolate areal data. For estimation and prediction we assume areal data is an average of an underlying continuous spatial process as in Moraga et al. (2017) <doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020) <doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020) <doi:10.1093/biostatistics/kxy041>. The interpolation methodology is (mostly) based on Goodchild and Lam (1980, ISSN:01652273).
Authors:
smile_1.1.0.tar.gz
smile_1.1.0.zip(r-4.7-x86_64)smile_1.1.0.zip(r-4.7-arm64)smile_1.1.0.zip(r-4.6-x86_64)smile_1.1.0.zip(r-4.6-arm64)smile_1.1.0.zip(r-4.5-x86_64)
smile_1.1.0.tgz(r-4.6-x86_64)smile_1.1.0.tgz(r-4.6-arm64)smile_1.1.0.tgz(r-4.5-x86_64)smile_1.1.0.tgz(r-4.5-arm64)
smile_1.1.0.tar.gz(r-4.7-arm64)smile_1.1.0.tar.gz(r-4.7-x86_64)smile_1.1.0.tar.gz(r-4.6-arm64)smile_1.1.0.tar.gz(r-4.6-x86_64)
smile_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
smile/json (API)
| # Install 'smile' in R: |
| install.packages('smile', repos = c('https://lcgodoy.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/lcgodoy/smile/issues
Pkgdown/docs site:https://lcgodoy.me
Datasets:
Last updated from:35ad624902. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 136 | ||
| linux-devel-x86_64 | OK | 159 | ||
| source / vignettes | OK | 236 | ||
| linux-release-arm64 | OK | 134 | ||
| linux-release-x86_64 | OK | 157 | ||
| macos-release-arm64 | OK | 189 | ||
| macos-release-x86_64 | OK | 513 | ||
| macos-oldrel-arm64 | OK | 184 | ||
| macos-oldrel-x86_64 | OK | 444 | ||
| windows-devel-arm64 | OK | 447 | ||
| windows-devel-x86_64 | OK | 164 | ||
| windows-release-arm64 | OK | 527 | ||
| windows-release-x86_64 | OK | 118 | ||
| windows-oldrel-x86_64 | OK | 128 | ||
| wasm-release | OK | 133 |
Exports:aiai_varfind_phifit_spmfit_spm2predict_spmsev_pexpsf_to_spmsummary_spm_fit
Dependencies:classclassIntDBIe1071KernSmoothlatticeMatrixmvtnormnumDerivproxyRcppRcppEigens2sfunitswk
Fitting models and making predictions
Last update: 2024-11-15
Started: 2020-12-19
Method
General setup | Model-based approach | Areal Interpolation (AI) | Reference
Last update: 2024-11-15
Started: 2020-12-19
Spatial covariance functions
Reference
Last update: 2024-11-15
Started: 2020-12-18
Converting sf objects to spm
References
Last update: 2024-11-15
Started: 2020-12-19
Areal Interpolation
Reference
Last update: 2024-11-15
Started: 2021-12-15
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Areal Interpolation | AI ai ai_var |
| Find phi parameter for the Exponential spatial auto-correlation function | find_phi |
| Fitting an underlying continuous process to areal data | fit_spm fit_spm.spm fit_spm2 |
| Akaike's (and Bayesian) An Information Criterion for 'spm_fit' objects. | AIC.spm_fit BIC.spm_fit goodness_of_fit |
| Liverpool Lower Super Output Area. | liv_lsoa |
| Liverpool Middle Super Output Area. | liv_msoa |
| Nova Lima census tracts | nl_ct |
| Prediction over the same or a different set of regions (or points). | predict_spm predict_spm.sf predict_spm.spm_fit |
| Calculate Smallest Eigenvalue for Power Exponential Correlation Matrices | sev_pexp |
| single 'sf' to 'spm' | sf_to_spm single_sf_to_spm |
| smile: Spatial MIsaLignment Estimation | smile |
| Summarizing 'spm_fit' | summary_spm_fit |
| Voronoi Data Linkage | vdl |
| Voronoi Data Linkage - Single variable and variance | vdl_var |