Package: mgwrsar 1.4.1

Ghislain Geniaux

mgwrsar: GWR, Mixed GWR with Spatial Autocorrelation and Multiscale GWR/GTWR (Top-Down Scale Approaches)

Provides methods for Geographically Weighted Regression with spatial autocorrelation (Geniaux and Martinetti 2017) <doi:10.1016/j.regsciurbeco.2017.04.001>. Implements Multiscale Geographically Weighted Regression with Top-Down Scale approaches (Geniaux 2026) <doi:10.1007/s10109-025-00481-4>.

Authors:Ghislain Geniaux [aut, cre], Davide Martinetti [aut], César Martinez [aut]

mgwrsar_1.4.1.tar.gz
mgwrsar_1.4.1.zip(r-4.7-x86_64)mgwrsar_1.4.1.zip(r-4.7-arm64)mgwrsar_1.4.1.zip(r-4.6-x86_64)mgwrsar_1.4.1.zip(r-4.6-arm64)
mgwrsar_1.4.1.tar.gz(r-4.7-arm64)mgwrsar_1.4.1.tar.gz(r-4.7-x86_64)mgwrsar_1.4.1.tar.gz(r-4.6-arm64)mgwrsar_1.4.1.tar.gz(r-4.6-x86_64)
mgwrsar_1.3.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mgwrsar/json (API)

# Install 'mgwrsar' in R:
install.packages('mgwrsar', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • mydata - Mydata is a simulated data set of a mgwrsar model
  • mydatasf - Mydataf is a Simple Feature object with real estate data in south of France.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

5.33 score 9 stars 1 packages 53 scripts 784 downloads 35 exports 153 dependencies

Last updated from:4ca8f794f4. Checks:9 OK, 1 FAIL. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK313
linux-devel-x86_64OK362
source / vignettesOK4035
linux-release-arm64OK367
linux-release-x86_64OK424
windows-devel-arm64OK463
windows-devel-x86_64OK469
windows-release-arm64OK458
windows-release-x86_64OK291
wasm-releaseFAIL195

Exports:as_gtwrbw_gdt2hwbbw_hwb2gdtcoeffind_TPfittedgolden_search_2d_bandwidthgolden_search_bandwidthgtwr_HWB2010gwr_beta_pivotal_qrp_cppgwr_beta_univar_cppINST_Cint_premskernel_matWmake_unique_by_structuremgwr_beta_pivotal_qrp_mixed_cppMGWRSARmgwrsar_bootstrap_testmgwrsar_bootstrap_test_allmultiscale_gwrnormWPhWY_Cplot_effectpredictProj_CQRcpp2_Creord_Dreord_M_Rresidualssearch_bandwidthssimu_multiscaleSl_Csummarysummary_MatrixTDS_MGWR

Dependencies:askpassbase64encBHbrewbslibcachemcaretclassclassIntcliclockcodetoolscpp11crosstalkcurldata.tableDBIdiagramdigestdoParalleldplyre1071evaluatefarverfastmapfontawesomeforeachFormulafsfuturefuture.applygenericsgeojsonsfgeometriesggplot2globalsgluegowergridExtragtablehardhathighrhtmltoolshtmlwidgetshttpuvhttrinumipredisobanditeratorsjquerylibjsonifyjsonliteKernSmoothknitrlabelinglaterlatticelavalazyevalleafemleafletleaflet.providersleafpoplibcoinlifecyclelistenvlubridatemagrittrmapviewMASSMatrixmboostmemoisemgcvmimeModelMetricsmvtnormnabornlmennetnnlsnumDerivopensslotelparallellypartykitpillarpkgconfigplotlyplyrpngpROCprodlimprogressrpromisesproxypurrrquadprogR6rapidjsonrrappdirsrasterRColorBrewerRcppRcppArmadilloRcppEigenrecipesreshape2RhpcBLASctlrlangrmarkdownrparts2S7sandwichsasssatellitescalesservrsfsfheadersshapespsparsevctrsSQUAREMstabsstringistringrstrucchangesurvivalsvglitesyssystemfontsterratextshapingtibbletidyrtidyselecttimechangetimeDatetinytextzdbunitsutf8uuidvctrsviridisLitewithrwkxfunyamlzoo

Estimating GWR and Mixed GWR Models with mgwrsar package: An Introduction with House Price Data
Rendered fromIntro_french_data.Rmdusingknitr::rmarkdown

Last update: 2026-10-01
Started: 2026-01-21

GWR and MGWR with Space-Time Kernels
Rendered fromGWR-with-Space-Time-Kernels.Rmdusingknitr::rmarkdown

Last update: 2026-10-01
Started: 2026-01-21

Multiscale GWR using top down scale approaches
Rendered fromMultiscale-GWR-using-top-down-scale-approach.Rmdusingknitr::rmarkdown

Last update: 2026-10-01
Started: 2026-01-21

Speeding up GWR like models with mgwrsar package using Target Points, rough gaussian kernel and parallelisation
Rendered fromSpeeding_up_GWR_like_models.Rmdusingknitr::rmarkdown

Last update: 2026-10-01
Started: 2026-01-21

GWR and Mixed GWR with spatial autocorrelation
Rendered fromGWR-and-Mixed-GWR-with-spatial-autocorrelation.Rmdusingknitr::rmarkdown

Last update: 2026-01-21
Started: 2026-01-21

Readme and manuals

Help Manual

Help pageTopics
Coerce a calibrated Type='GDT' model into Huang's parameterisationas_gtwr
atds_gwr Top-Down Scaling approach of GWRatds_gwr
Convert an mgwrsar (h_S, h_T) pair back into Huang's (h_ST, tau)bw_gdt2hwb
Convert Huang's (h_ST, tau) into the mgwrsar (h_S, h_T) pairbw_hwb2gdt
coef for mgwrsar modelcoef,mgwrsar-method
Search of a suitable set of target points. find_TP is a wrapper function that identifies a set of target points based on spatial smoothed OLS residuals.find_TP
fitted for mgwrsar modelfitted,mgwrsar-method
Optimization of 2D Bandwidths (Spatial and Temporal) using Golden Section Searchgolden_search_2d_bandwidth
Golden search bandwidth (deprecated)golden_search_bandwidth
Geographically and Temporally Weighted Regression (Huang, Wu and Barry 2010)gtwr_HWB2010
Class "gtwr"gtwr-class
kernel_matW A function that returns a sparse weight matrix based computed with a specified kernel (gauss,bisq,tcub,epane,rectangle,triangle) considering coordinates provides in S and a given bandwidth. If NN<nrow(S) only NN firts neighbours are considered. If Type!='GD' then S should have additional columns and several kernels and bandwidths should be be specified by the user.kernel_matW
Ensure Uniqueness of Coordinates or Values in 1D, 2D, or 3D Structuresmake_unique_by_structure
Estimation of linear and local linear model with spatial autocorrelation model (mgwrsar).MGWRSAR
A bootstrap test for Betas for mgwrsar class model.mgwrsar_bootstrap_test
A bootstrap test for testing nullity of all Betas for mgwrsar class model,mgwrsar_bootstrap_test_all
Class of mgwrsar Model.mgwrsar-class
Multiscale Geographically Weighted Regression (MGWR)multiscale_gwr
mydata is a simulated data set of a mgwrsar modelmydata
mydataf is a Simple Feature object with real estate data in south of France.mydatasf
Row Normalization of Sparse MatrixnormW
plot_effect plot_effect is a function that plots the effect of a variable X_k with spatially varying coefficient, i.e X_k * Beta_k(u_i,v_i) for comparing the magnitude of effects of between variables.plot_effect
Plot method for mgwrsar modelplot,mgwrsar,missing-method plot.mgwrsar
predict method for mgwrsar modelpredict,mgwrsar-method
reord_Dreord_D
reord_Mreord_M_R
residuals for mgwrsar modelresiduals,mgwrsar-method
Bandwidth Selection via Multi-round Grid Search based on AICcsearch_bandwidths
Simulate Data Generating Processes (DGP) for Multiscale GWRsimu_multiscale
Summary of a Sparse Matrixsummary_Matrix
summary for gtwr modelsummary,gtwr-method
summary for mgwrsar modelsummary,mgwrsar-method
Top-Down Scale (TDS) and Adaptive Top-Down Scale (ATDS) Estimation for MGWRTDS_MGWR