This package is considered a duplicate. The official version of this package is found at:https://ggeniaux.r-universe.dev/mgwrsar

Package: mgwrsar 1.3.2

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_1.3.2.tar.gz
mgwrsar_1.3.2.tar.gz(r-4.7-arm64)mgwrsar_1.3.2.tar.gz(r-4.6-arm64)mgwrsar_1.3.2.tar.gz(r-4.7-x86_64)mgwrsar_1.3.2.tar.gz(r-4.6-x86_64)
mgwrsar_1.3.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
mgwrsar/json (API)
NEWS

# 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
openmp– GCC OpenMP (GOMP) support library

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:

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

openblas cpp openmp

4.60 score 9 stars 44 scripts 357 downloads 30 exports 154 dependencies

Last updated from:451be9bb9f. Checks:4 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	OK	431
source / vignettes	OK	407
linux-release-x86_64	OK	418
wasm-release	OK	220

Exports:coef find_TP golden_search_2d_bandwidth golden_search_bandwidth gwr_beta_pivotal_qrp_cpp gwr_beta_univar_cpp INST_C int_prems kernel_matW make_unique_by_structure mgwr_beta_pivotal_qrp_mixed_cpp MGWRSAR mgwrsar_bootstrap_test mgwrsar_bootstrap_test_all multiscale_gwr normW PhWY_C plot_effect predict Proj_C QRcpp2_C reord_D reord_M_R residuals search_bandwidths simu_multiscale Sl_C summary summary_Matrix TDS_MGWR

Dependencies:askpass base64enc BH brew bslib cachem caret class classInt cli clock codetools cpp11 crosstalk curl data.table DBI diagram digest doParallel dplyr e1071 evaluate farver fastmap fontawesome foreach Formula fs future future.apply generics geojsonsf geometries ggplot2 globals glue gower gridExtra gtable hardhat highr htmltools htmlwidgets httpuv httr inum ipred isoband iterators jquerylib jsonify jsonlite KernSmooth knitr labeling later lattice lava lazyeval leafem leaflet leaflet.providers leafpop libcoin lifecycle listenv lubridate magrittr mapview MASS Matrix mboost memoise mgcv mime ModelMetrics mvtnorm nabor nlme nnet nnls numDeriv openssl otel parallelly partykit pillar pkgconfig plotly plyr png pROC prodlim progressr promises proxy purrr quadprog R6 rapidjsonr rappdirs raster RColorBrewer Rcpp RcppArmadillo RcppEigen recipes reshape2 RhpcBLASctl rlang rmarkdown rpart RSpectra s2 S7 sass satellite scales servr sf sfheaders shape SKAT SMUT sp sparsevctrs SPAtest SQUAREM stabs stringi stringr survival svglite sys systemfonts terra textshaping tibble tidyr tidyselect timechange timeDate tinytex tzdb units utf8 uuid vctrs viridisLite withr wk xfun yaml

Estimating GWR and Mixed GWR Models with mgwrsar package: An Introduction with House Price Data

Rendered fromIntro_french_data.Rmdusingknitr::rmarkdownon Jun 01 2026.

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

GWR and MGWR with Space-Time Kernels

Rendered fromGWR-with-Space-Time-Kernels.Rmdusingknitr::rmarkdownon Jun 01 2026.

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

GWR and Mixed GWR with spatial autocorrelation

Ghislain Geniaux

Rendered fromGWR-and-Mixed-GWR-with-spatial-autocorrelation.Rmdusingknitr::rmarkdownon Jun 01 2026.

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

Multiscale GWR using top down scale approaches

Rendered fromMultiscale-GWR-using-top-down-scale-approach.Rmdusingknitr::rmarkdownon Jun 01 2026.

Last update: 2026-03-03
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::rmarkdownon Jun 01 2026.

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

Citation

Readme and manuals

Help Manual

Help page	Topics
atds_gwr Top-Down Scaling approach of GWR	atds_gwr
coef for mgwrsar model	coef,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 model	fitted,mgwrsar-method
Optimization of 2D Bandwidths (Spatial and Temporal) using Golden Section Search	golden_search_2d_bandwidth
Golden search bandwidth (deprecated)	golden_search_bandwidth
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 Structures	make_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 model	mydata
mydataf is a Simple Feature object with real estate data in south of France.	mydatasf
Row Normalization of Sparse Matrix	normW
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 model	plot,mgwrsar,missing-method plot.mgwrsar
predict method for mgwrsar model	predict,mgwrsar-method
reord_D	reord_D
reord_M	reord_M_R
residuals for mgwrsar model	residuals,mgwrsar-method
Bandwidth Selection via Multi-round Grid Search based on AICc	search_bandwidths
Simulate Data Generating Processes (DGP) for Multiscale GWR	simu_multiscale
Summary of a Sparse Matrix	summary_Matrix
summary for mgwrsar model	summary,mgwrsar-method
Top-Down Scale (TDS) and Adaptive Top-Down Scale (ATDS) Estimation for MGWR	TDS_MGWR

Package: mgwrsar 1.3.2

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

Estimating GWR and Mixed GWR Models with mgwrsar package: An Introduction with House Price Data

GWR and MGWR with Space-Time Kernels

GWR and Mixed GWR with spatial autocorrelation

Multiscale GWR using top down scale approaches

Speeding up GWR like models with mgwrsar package using Target Points, rough gaussian kernel and parallelisation

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)