disaggregation: Disaggregation Modelling

Fits disaggregation regression models using 'TMB' ('Template Model Builder'). When the response data are aggregated to polygon level but the predictor variables are at a higher resolution, these models can be useful. Regression models with spatial random fields. The package is described in detail in Nandi et al. (2023) <doi:10.18637/jss.v106.i11>.

Version: 0.3.0
Imports: splancs, Matrix, stats, TMB, dplyr, ggplot2, cowplot, sparseMVN, fmesher, tidyterra, terra, sf, utils
LinkingTo: TMB, RcppEigen
Suggests: testthat, INLA, knitr, rmarkdown, SpatialEpi
Published: 2023-11-08
Author: Anita Nandi ORCID iD [aut], Tim Lucas ORCID iD [aut, cre], Rohan Arambepola [aut], Andre Python ORCID iD [aut], Simon Smart [ctb]
Maintainer: Tim Lucas <timcdlucas at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: yes
SystemRequirements: GNU make
Additional_repositories: https://inla.r-inla-download.org/R/stable
Citation: disaggregation citation info
CRAN checks: disaggregation results

Documentation:

Reference manual: disaggregation.pdf
Vignettes: A short introduction to the disaggregation package

Downloads:

Package source: disaggregation_0.3.0.tar.gz
Windows binaries: r-devel: disaggregation_0.3.0.zip, r-release: disaggregation_0.3.0.zip, r-oldrel: disaggregation_0.3.0.zip
macOS binaries: r-release (arm64): disaggregation_0.3.0.tgz, r-oldrel (arm64): disaggregation_0.3.0.tgz, r-release (x86_64): disaggregation_0.3.0.tgz
Old sources: disaggregation archive

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