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).

Version: 1.0.5
Depends: R (≥ 4.0)
Imports: numDeriv, Rcpp, sf, mvtnorm, stats, parallel, Matrix
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, ggplot2, graphics, spelling
Published: 2024-06-14
DOI: 10.32614/
Author: Lucas da Cunha Godoy ORCID iD [aut, cre]
Maintainer: Lucas da Cunha Godoy <lcgodoy at>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: GDAL (>= 2.0.1), GEOS (>= 3.4.0), PROJ (>= 4.8.0)
Language: en-US
Materials: README NEWS
CRAN checks: smile results


Reference manual: smile.pdf
Vignettes: 2. Fitting models and making predictions
3. Areal Interpolation
1. Converting sf objects to spm
5. Spatial covariance functions
4. Method


Package source: smile_1.0.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): smile_1.0.5.tgz, r-oldrel (arm64): smile_1.0.5.tgz, r-release (x86_64): smile_1.0.5.tgz, r-oldrel (x86_64): smile_1.0.5.tgz
Old sources: smile archive


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