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. A useful reference for disaggregation modelling is Lucas et al. (2019) <doi:10.1101/548719>.

Version: 0.1.3
Imports: maptools, raster, foreach, sp, parallel, doParallel, rgeos, splancs, rgdal, Matrix, stats, TMB, dplyr, ggplot2, cowplot, sparseMVN, utils
LinkingTo: TMB, RcppEigen
Suggests: testthat, INLA, knitr, rmarkdown, SpatialEpi
Published: 2020-02-19
Author: Anita Nandi ORCID iD [aut, cre], Tim Lucas ORCID iD [aut], Rohan Arambepola [aut], Andre Python ORCID iD [aut]
Maintainer: Anita Nandi <anita.k.nandi at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: yes
SystemRequirements: GNU make
Citation: disaggregation citation info
CRAN checks: disaggregation results

Downloads:

Reference manual: disaggregation.pdf
Vignettes: A short introduction to the disaggregation package
Package source: disaggregation_0.1.3.tar.gz
Windows binaries: r-devel: disaggregation_0.1.3.zip, r-devel-gcc8: disaggregation_0.1.3.zip, r-release: disaggregation_0.1.3.zip, r-oldrel: disaggregation_0.1.3.zip
OS X binaries: r-release: disaggregation_0.1.3.tgz, r-oldrel: disaggregation_0.1.3.tgz
Old sources: disaggregation archive

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