Package: bigDM 0.5.5

Aritz Adin

bigDM: Scalable Bayesian Disease Mapping Models for High-Dimensional Data

Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 <doi:10.1016/j.spasta.2021.100496>; Orozco-Acosta et al., 2023 <doi:10.1016/j.cmpb.2023.107403> and Vicente et al., 2023 <doi:10.1007/s11222-023-10263-x>). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001).

Authors:Aritz Adin [aut, cre], Erick Orozco-Acosta [aut], Maria Dolores Ugarte [aut]

bigDM_0.5.5.tar.gz
bigDM_0.5.5.zip(r-4.5)bigDM_0.5.5.zip(r-4.4)bigDM_0.5.5.zip(r-4.3)
bigDM_0.5.5.tgz(r-4.4-any)bigDM_0.5.5.tgz(r-4.3-any)
bigDM_0.5.5.tar.gz(r-4.5-noble)bigDM_0.5.5.tar.gz(r-4.4-noble)
bigDM_0.5.5.tgz(r-4.4-emscripten)bigDM_0.5.5.tgz(r-4.3-emscripten)
bigDM.pdf |bigDM.html
bigDM/json (API)
NEWS

# Install 'bigDM' in R:
install.packages('bigDM', repos = c('https://spatialstatisticsupna.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/spatialstatisticsupna/bigdm/issues

Datasets:

On CRAN:

4.92 score 14 stars 10 scripts 352 downloads 14 exports 64 dependencies

Last updated 3 months agofrom:e42193e7a3. Checks:OK: 2 NOTE: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 18 2024
R-4.5-winOKOct 18 2024
R-4.5-linuxNOTEOct 18 2024
R-4.4-winNOTEOct 18 2024
R-4.4-macNOTEOct 18 2024
R-4.3-winNOTEOct 18 2024
R-4.3-macNOTEOct 18 2024

Exports:add_neighbourCAR_INLAclustering_partitionconnect_subgraphsdivide_cartoMCAR_INLAmergeINLAMmodel_compute_corMmodel_icarMmodel_iidMmodel_lcarMmodel_pcarrandom_partitionSTCAR_INLA

Dependencies:bootclassclassIntclicodacodetoolscrayondata.tableDBIdeldirdigestdoParallele1071fansifastDummiesforeachfuturefuture.applygeosglobalsglueiteratorsjsonliteKernSmoothlatticeLearnBayeslibgeoslifecyclelistenvmagrittrMASSMatrixmultcompmvtnormnlmeparallellypillarpkgconfigproxyrbibutilsRColorBrewerRcppRdpackrlangrlists2sandwichsfspspatialregspDataspdepstringistringrsurvivalTH.datatibbleunitsutf8vctrswkXMLyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Scalable Bayesian Disease Mapping Models for High-Dimensional DatabigDM-package bigDM
Add isolated areas (polygons) to its nearest neighbouradd_neighbour
Fit a (scalable) spatial Poisson mixed model to areal count data, where several CAR prior distributions can be specified for the spatial random effect.CAR_INLA
Spanish colorectal cancer mortality dataCarto_SpainMUN
Obtain a partition of the spatial domain using the density-based spatial clustering (DBSC) algorithm described in Santafé et al. (2021)clustering_partition
Merge disjoint connected subgraphsconnect_subgraphs
Spanish lung cancer mortality dataData_LungCancer
Spanish cancer mortality data for the joint analysis of multiple diseasesData_MultiCancer
Divide the spatial domain into subregionsdivide_carto
Fit a (scalable) spatial multivariate Poisson mixed model to areal count data where dependence between spatial patterns of the diseases is addressed through the use of M-models (Botella-Rocamora et al. 2015).MCAR_INLA
Merge 'inla' objects for partition modelsmergeINLA
Compute correlation coefficients between diseasesMmodel_compute_cor
Intrinsic multivariate CAR latent effectMmodel_icar
Spatially non-structured multivariate latent effectMmodel_iid
Leroux et al. (1999) multivariate CAR latent effectMmodel_lcar
Proper multivariate CAR latent effectMmodel_pcar
Define a random partition of the spatial domain based on a regular gridrandom_partition
Fit a (scalable) spatio-temporal Poisson mixed model to areal count data.STCAR_INLA