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Implement several\nmethods for spatial analysis on network: Network Kernel Density\nestimation, building of spatial matrices based on network\ndistance ('listw' objects from 'spdep' package), K functions\nestimation for point pattern analysis on network, k nearest\nneighbours on network, reachable area calculation, and graph\ngeneration References: Okabe et al (2019)\n; Okabe et al (2012,\nISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).", "License": "GPL-2", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr", "URL": "https://jeremygelb.github.io/spNetwork/", "BugReports": "https://github.com/JeremyGelb/spNetwork/issues", "RdMacros": "Rdpack", "Language": "en-CA", "SystemRequirements": "C++17", "Config/pak/sysreqs": "libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev\nlibglpk-dev make libxml2-dev libssl-dev libproj-dev\nlibsqlite3-dev libudunits2-dev", "Repository": "https://jeremygelb.r-universe.dev", "Date/Publication": "2025-10-04 22:42:21 UTC", "RemoteUrl": "https://github.com/jeremygelb/spnetwork", "RemoteRef": "HEAD", "RemoteSha": "53127c019770784db8d8a1bf0f284bbaa9523ab9", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-09 06:50:52 UTC", "User": "root" }, "Author": "Jeremy Gelb [aut, cre] (ORCID: ),\nPhilippe Apparicio [ctb] (ORCID:\n)", "Maintainer": "Jeremy Gelb ", "MD5sum": "655125f838860528bbc9cc9cd5744d0a", "_user": "jeremygelb", "_type": "src", "_file": "spNetwork_0.4.4.7.tar.gz", "_fileid": "41cc72fb6e96cbbfe945e8677abec1523a4c0782e0a5609e2cc11a776e37a166", "_filesize": 9017542, "_sha256": "41cc72fb6e96cbbfe945e8677abec1523a4c0782e0a5609e2cc11a776e37a166", "_created": "2026-06-09T06:50:52.000Z", "_published": "2026-06-09T07:10:36.962Z", "_distro": "noble", "_jobs": [ { "job": 80264970613, "time": 290, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7500570708" }, { "job": 80264970541, "time": 314, 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road network of Montreal", "object": "small_mtl_network", "class": [ "sf", "data.frame" ], "fields": [ "ClsRte", "nbAccident", "geom" ], "rows": 1244, "table": true, "tojson": true } ], "_help": [ { "page": "spNetwork-package", "title": "spNetwork: Spatial Analysis on Network", "topics": [ "spNetwork-package" ] }, { "page": "adaptive_bw_tnkde_cpp", "title": "The exposed function to calculate adaptive bandwidth with space-time interaction for TNKDE (INTERNAL)", "topics": [ "adaptive_bw_tnkde_cpp" ] }, { "page": "adaptive_bw_tnkde_cpp2", "title": "The exposed function to calculate adaptive bandwidth with space-time interaction for TNKDE (INTERNAL)", "topics": [ "adaptive_bw_tnkde_cpp2" ] }, { "page": "aggregate_points", "title": "Events aggregation", "topics": [ "aggregate_points" ] }, { "page": "bike_accidents", "title": "Road accidents including a bicyle in Montreal in 2016", "topics": [ "bike_accidents" ] }, { "page": "build_graph", "title": "Network generation with igraph", "topics": [ "build_graph" ] }, { "page": "build_graph_directed", "title": "Directed network generation", "topics": [ "build_graph_directed" ] }, { "page": "bw_cv_likelihood_calc", "title": "Bandwidth selection by likelihood cross validation", "topics": [ "bw_cv_likelihood_calc" ] }, { "page": "bw_cv_likelihood_calc_tkde", "title": "Bandwidth selection for Temporal Kernel density estimate by likelihood cross validation", "topics": [ "bw_cv_likelihood_calc_tkde" ] }, { "page": "bw_cv_likelihood_calc.mc", "title": "Bandwidth selection by likelihood cross validation (multicore)", "topics": [ "bw_cv_likelihood_calc.mc" ] }, { "page": "bw_cvl_calc", "title": "Bandwidth selection by Cronie and Van Lieshout's Criterion", "topics": [ "bw_cvl_calc" ] }, { "page": "bw_cvl_calc.mc", "title": "Bandwidth selection by Cronie and Van Lieshout's Criterion (multicore version)", "topics": [ "bw_cvl_calc.mc" ] }, { "page": "bw_tnkde_cv_likelihood_calc", "title": "Bandwidth selection by likelihood cross validation for temporal NKDE", "topics": [ "bw_tnkde_cv_likelihood_calc" ] }, { "page": "bw_tnkde_cv_likelihood_calc.mc", "title": "Bandwidth selection by likelihood cross validation for temporal NKDE (multicore)", "topics": [ "bw_tnkde_cv_likelihood_calc.mc" ] }, { "page": "calc_isochrones", "title": "Isochrones calculation", "topics": [ "calc_isochrones" ] }, { "page": "closest_points", "title": "Find closest points", "topics": [ "closest_points" ] }, { "page": "cosine_kernel", "title": "Cosine kernel", "topics": [ "cosine_kernel" ] }, { "page": "cross_gfunc_cpp", "title": "c++ cross g function", "topics": [ "cross_gfunc_cpp" ] }, { "page": "cross_kfunc_cpp", "title": "c++ cross k function", "topics": [ "cross_kfunc_cpp" ] }, { "page": "cross_kfunctions", "title": "Network cross k and g functions (maturing)", "topics": [ "cross_kfunctions" ] }, { "page": "cross_kfunctions.mc", "title": "Network cross k and g functions (maturing, multicore)", "topics": [ "cross_kfunctions.mc" ] }, { "page": "epanechnikov_kernel", "title": "Epanechnikov kernel", "topics": [ "epanechnikov_kernel" ] }, { "page": "esc_kernel_loo_nkde", "title": "The worker function to calculate continuous TNKDE likelihood cv", "topics": [ "esc_kernel_loo_nkde" ] }, { "page": "esc_kernel_loo_tnkde", "title": "The worker function to calculate continuous TNKDE likelihood cv", "topics": [ "esc_kernel_loo_tnkde" ] }, { "page": "esd_kernel_loo_nkde", "title": "The worker function to calculate discontinuous TNKDE likelihood cv", "topics": [ "esd_kernel_loo_nkde" ] }, { "page": "esd_kernel_loo_tnkde", "title": "The worker function to calculate discontinuous TNKDE likelihood cv", "topics": [ "esd_kernel_loo_tnkde" ] }, { "page": "gaussian_kernel", "title": "Gaussian kernel", "topics": [ "gaussian_kernel" ] }, { "page": "gaussian_kernel_scaled", "title": "Scaled gaussian kernel", "topics": [ "gaussian_kernel_scaled" ] }, { "page": "gfunc_cpp", "title": "c++ g function", "topics": [ "gfunc_cpp" ] }, { "page": "graph_checking", "title": "Topological error", "topics": [ "graph_checking" ] }, { "page": "k_nt_functions", "title": "Network k and g functions for spatio-temporal data (experimental, NOT READY FOR USE)", "topics": [ "k_nt_functions" ] }, { "page": "k_nt_functions.mc", "title": "Network k and g functions for spatio-temporal data (multicore, experimental, NOT READY FOR USE)", "topics": [ "k_nt_functions.mc" ] }, { "page": "kfunc_cpp", "title": "c++ k function", "topics": [ "kfunc_cpp" ] }, { "page": "kfunctions", "title": "Network k and g functions (maturing)", "topics": [ "kfunctions" ] }, { "page": "kfunctions.mc", "title": "Network k and g functions (multicore)", "topics": [ "kfunctions.mc" ] }, { "page": "lines_center", "title": "Centre points of lines", "topics": [ "lines_center" ] }, { "page": "lines_direction", "title": "Unify lines direction", "topics": [ "lines_direction" ] }, { "page": "lines_extremities", "title": "Get lines extremities", "topics": [ "lines_extremities" ] }, { "page": "lines_points_along", "title": "Points along lines", "topics": [ "lines_points_along" ] }, { "page": "lixelize_lines", "title": "Cut lines into lixels", "topics": [ "lixelize_lines" ] }, { "page": "lixelize_lines.mc", "title": "Cut lines into lixels (multicore)", "topics": [ "lixelize_lines.mc" ] }, { "page": "main_network_mtl", "title": "Primary road network of Montreal", "topics": [ "main_network_mtl" ] }, { "page": "mtl_libraries", "title": "Libraries of Montreal", "topics": [ "mtl_libraries" ] }, { "page": "mtl_network", "title": "Road network of Montreal", "topics": [ "mtl_network" ] }, { "page": "mtl_theatres", "title": "Theatres of Montreal", "topics": [ "mtl_theatres" ] }, { "page": "network_knn", "title": "K-nearest points on network", "topics": [ "network_knn" ] }, { "page": "network_knn.mc", "title": "K-nearest points on network (multicore version)", "topics": [ "network_knn.mc" ] }, { "page": "network_listw", "title": "Network distance listw", "topics": [ "network_listw" ] }, { "page": "network_listw.mc", "title": "Network distance listw (multicore)", "topics": [ "network_listw.mc" ] }, { "page": "nkde", "title": "Network Kernel density estimate", "topics": [ "nkde" ] }, { "page": "nkde_get_loo_values", "title": "The exposed function to calculate NKDE likelihood cv", "topics": [ "nkde_get_loo_values" ] }, { "page": "nkde.mc", "title": "Network Kernel density estimate (multicore)", "topics": [ "nkde.mc" ] }, { "page": "quartic_kernel", "title": "Quartic kernel", "topics": [ "quartic_kernel" ] }, { "page": "simple_lines", "title": "LineString to simple Line", "topics": [ "simple_lines" ] }, { "page": "simplify_network", "title": "Simplify a network", "topics": [ "simplify_network" ] }, { "page": "small_mtl_network", "title": "Smaller subset road network of Montreal", "topics": [ "small_mtl_network" ] }, { "page": "split_graph_components", "title": "Split graph components", "topics": [ "split_graph_components" ] }, { "page": "split_lines_at_vertex", "title": "Split lines at vertices in a feature collection of linestrings", "topics": [ "split_lines_at_vertex" ] }, { "page": "st_bbox_by_feature", "title": "Obtain all the bounding boxes of a feature collection", "topics": [ "st_bbox_by_feature" ] }, { "page": "tkde", "title": "Temporal Kernel density estimate", "topics": [ "tkde" ] }, { "page": "tnkde", "title": "Temporal Network Kernel density estimate", "topics": [ "tnkde" ] }, { "page": "tnkde_get_loo_values", "title": "The exposed function to calculate TNKDE likelihood cv", "topics": [ "tnkde_get_loo_values" ] }, { "page": "tnkde_get_loo_values2", "title": "The exposed function to calculate TNKDE likelihood cv", "topics": [ "tnkde_get_loo_values2" ] }, { "page": "tnkde_worker_bw_sel", "title": "Worker function fo Bandwidth selection by likelihood cross validation for temporal NKDE", "topics": [ "tnkde_worker_bw_sel" ] }, { "page": "tnkde.mc", "title": "Temporal Network Kernel density estimate 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