{ "_id": "6a1ee0dab401979e7341089c", "Package": "GWRLASSO", "Type": "Package", "Title": "A Hybrid Model for Spatial Prediction Through Local Regression", "Version": "0.1.0", "Authors@R": "c(person(\"Nobin Chandra\",\"Paul\", role=c(\"aut\",\"cre\",\"cph\"), email=\"nobin.paul@icar.gov.in\"),person(\"Anil\",\"Rai\",role=\"aut\"),person(\"Ankur\",\"Biswas\",role=\"aut\"),person(\"Tauqueer\",\"Ahmad\",role=\"aut\"),person(\"Bhaskar B.\",\" Gaikwad\",role=\"aut\"),person(\"Dhananjay D.\",\"Nangare\",role=\"aut\"),person(\"K. Sammi\",\"Reddy\",role=\"aut\"))", "Description": "It implements a hybrid spatial model for improved spatial\nprediction by combining the variable selection capability of\nLASSO (Least Absolute Shrinkage and Selection Operator) with\nthe Geographically Weighted Regression (GWR) model that\ncaptures the spatially varying relationship efficiently. For\nmethod details see, Wheeler, D.C.(2009)..\nThe developed hybrid model efficiently selects the relevant\nvariables by using LASSO as the first step; these selected\nvariables are then incorporated into the GWR framework,\nallowing the estimation of spatially varying regression\ncoefficients at unknown locations and finally predicting the\nvalues of the response variable at unknown test locations while\ntaking into account the spatial heterogeneity of the data.\nIntegrating the LASSO and GWR models enhances prediction\naccuracy by considering spatial heterogeneity and capturing the\nlocal relationships between the predictors and the response\nvariable. The developed hybrid spatial model can be useful for\nspatial modeling, especially in scenarios involving complex\nspatial patterns and large datasets with multiple predictor\nvariables.", "License": "GPL (>= 2.0)", "Encoding": "UTF-8", "RoxygenNote": "7.2.3", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-20 07:04:15 UTC", "User": "root" }, "Author": "Nobin Chandra Paul [aut, cre, cph], Anil Rai [aut], Ankur\nBiswas [aut], Tauqueer Ahmad [aut], Bhaskar B. Gaikwad [aut],\nDhananjay D. Nangare [aut], K. Sammi Reddy [aut]", "Maintainer": "Nobin Chandra Paul ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2023-08-28 12:47:05 UTC", "RemoteUrl": "https://github.com/cran/GWRLASSO", "RemoteRef": "HEAD", "RemoteSha": "98b31a6ecc7f54c6abdf89a8b2d8010751769d9b", "MD5sum": "f0a02b658363bfe3def86a2434aa5694", "_user": "cran", "_type": "src", "_file": "GWRLASSO_0.1.0.tar.gz", "_fileid": "5f35d20481d54096ae70c6b674cb20733eb54bf6b8695f4cdaf2a07232b6c8e0", "_filesize": 97145, "_sha256": "5f35d20481d54096ae70c6b674cb20733eb54bf6b8695f4cdaf2a07232b6c8e0", "_created": "2026-05-20T07:04:15.000Z", "_published": "2026-06-02T13:55:38.625Z", "_distro": "noble", "_jobs": [ { "job": 79087472461, "time": 161, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7103402452" }, { "job": 79087472278, "time": 146, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7103401625" }, { "job": 79087470912, "time": 188, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7103356733" }, { "job": 79087471027, "time": 104, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7359729915" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/26146842459", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/GWRLASSO", "_commit": { "id": "98b31a6ecc7f54c6abdf89a8b2d8010751769d9b", "author": "Nobin Chandra Paul ", "committer": "cran-robot ", "message": "version 0.1.0\n", "time": 1693226825 }, "_maintainer": { "name": "Nobin Chandra Paul", "email": "nobin.paul@icar.gov.in" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 2.10", "role": "Depends" }, { "package": "stats", "role": "Imports" }, { "package": "qpdf", "role": "Imports" }, { "package": "numbers", "role": "Imports" }, { "package": "glmnet", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "testthat", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [], "_tags": [], "_stars": 0, "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 187, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/GWRLASSO" }, "_searchresults": 3, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/GWRLASSO.html", "manual.pdf" ], "_realowner": "cran", "_cranurl": false, "_releases": [ { "version": "0.1.0", "date": "2023-08-28" } ], "_exports": [ "GWRLASSO_exponential", "GWRLASSO_gaussian" ], "_help": [ { "page": "GWRLASSO_exponential", "title": "GWRLASSO: a hybrid model that uses the LASSO model for important variable selection and GWR model with exponential kernel for prediction at an unknown location based on the selected variables.", "topics": [ "GWRLASSO_exponential" ] }, { "page": "GWRLASSO_gaussian", "title": "GWRLASSO: a hybrid model that uses the LASSO model for important variable selection and GWR model with gaussian kernel for prediction at an unknown location based on the selected variables.", "topics": [ "GWRLASSO_gaussian" ] } ], "_rundeps": [ "askpass", "codetools", "curl", "foreach", "glmnet", "iterators", "lattice", "Matrix", "numbers", "qpdf", "Rcpp", "RcppEigen", "shape", "survival", "sys" ], "_vignettes": [ { "source": "GWRLASSO.Rmd", "filename": "GWRLASSO.html", "title": "GWRLASSO:A Hybrid Model for Spatial Prediction Through Local Regression", "engine": "knitr::rmarkdown", "headings": [ "Introduction" ], "created": "2023-08-28 12:47:05", "modified": "2023-08-28 12:47:05", "commits": 1 } ], "_score": 2, "_indexed": true, "_nocasepkg": "gwrlasso", "_universes": [ "cran" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.1.0", "date": "2026-05-20T07:06:41.000Z", "distro": "noble", "commit": "98b31a6ecc7f54c6abdf89a8b2d8010751769d9b", "fileid": "b9e1792d64a83f3f4b39b710795bf08f60dc5bc2cd398e017685759d9d73f536", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/26146842459" }, { "r": "4.6.0", "os": "linux", "version": "0.1.0", "date": "2026-05-20T07:06:41.000Z", "distro": "noble", "commit": "98b31a6ecc7f54c6abdf89a8b2d8010751769d9b", "fileid": "3189300ba29d4d4ad7b5ce581ebc79f1db09560778afca1f2901c1ed4cac5dc8", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/26146842459" }, { "r": "4.6.0", "os": "wasm", "version": "0.1.0", "date": "2026-06-02T13:55:11.000Z", "commit": "98b31a6ecc7f54c6abdf89a8b2d8010751769d9b", "fileid": "eb187a9d664ecb72b8765fb5e55500ddfb47460eee180ad4458a9fd07bb5fca4", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/26146842459" } ] }