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    "text": "Historically, 4 packages make it possible to import, manipulate and transform spatial data:\n\nThe package rgdal (Bivand, Keitt, and Rowlingson 2022) which is an interface between R and the GDAL (GDAL/OGR contributors, n.d.) and PROJ (PROJ contributors 2021) libraries allow you to import and export spatial data (shapefiles for example) and also to manage cartographic projections\n\nThe package sp (E. J. Pebesma and Bivand 2005) provides class and methods for vector spatial data in R. It allows displaying background maps, inspectiong an attribute table etc.\n\nThe package rgeos (Bivand and Rundel 2021) gives access to the GEOS spatial operations library and therefore makes classic GIS operations available: calculation of surfaces or perimeters, calculation of distances, spatial aggregations, buffer zones, intersections, etc.\n\nThe package raster (Hijmans 2022a) is dedicated to the import, manipulation and modeling of raster data.\n\nToday, the main developments concerning vector data have moved away from the old 3 (sp, rgdal, rgeos) to rely mainly on the package sf ((E. Pebesma 2018a), (E. Pebesma 2018b)). In this manual we will rely exclusively on this package to manipulate vector data.\nThe packages stars (E. Pebesma 2021) and terra (Hijmans 2022b) come to replace the package raster for processing raster data. We have chosen to use the package here terra for its proximity to the raster."
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    "section": "1.2 The package sf",
    "text": "1.2 The package sf\n The package sf was released in late 2016 by Edzer Pebesma (also author of sp). Its goal is to combine the feature of sp, rgeos and rgdal in a single, more ergonomic package. This package offers simple objects (following the simple feature standard) which are easier to manipulate. Particular attention has been paid to the compatibility of the package with the pipe syntax and the operators of the tidyverse.\nsf directly uses the GDAL, GEOS and PROJ libraries.\n\n\n\n\n\nFrom r-spatial.org\n\n\n\n\n\n\nWebsite of package sf : Simple Features for R\n\n\n\n\n1.2.1 Format of spatial objects sf\n\n\n\n\n\nObjectssf are objects in data.frame which one of the columns contains geometries. This column is the class of sfc (simple feature column) and each individual of the column is a sfg (simple feature geometry). This format is very practical insofa as the data and the geometries are intrinsically linked in the same object.\n\n\n\n\n\n\nThumbnail describing the simple feature format: Simple Features for R\n\n\n\n\n\n\n\n\n\nTip\n\n\n\nA benchmark of vector processing libraries is available here."
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    "section": "1.3 Package mapsf",
    "text": "1.3 Package mapsf\nThe free R software spatial ecosystem is rich, dynamic and mature and several packages allow to import, process and represent spatial data. The package mapsf (Giraud 2022) relies on this ecosystem to integrate the creation of quality thematic maps into processing chains with R.\nOther packages can be used to make thematic maps. The package ggplot2 (Wickham 2016), in association with the package ggspatial (Dunnington 2021), allows for example to display spatial objects and to make simple thematic maps. The package tmap (Tennekes 2018) is dedicated to the creation of thematic maps, it uses a syntax close to that of ggplot2 (sequence of instructions combined with the ‘+’ sign). Documentation and tutorials for using these two packages are readily available on the web.\nHere, we will mainly use the package mapsf whose functionalities are quite complete and the handling rather simple. In addition, the package is relatively light.\n\nmapsf allows you to create most of the types of map usually used in statistical cartography (choropleth maps, typologies, proportional or graduated symbols, etc.). For each type of map, several parameters are used to customize the cartographic representation. These parameters are the same as those found in the usual GIS or cartography software (for example, the choice of discretizations and color palettes, the modification of the size of the symbols or the customization of the legends). Associated with the data representation functions, other functions are dedicated to cartographic dressing (themes or graphic charters, legends, scales, orientation arrows, title, credits, annotations, etc.), the creation of boxes or the exporting maps.\nmapsf is the successor of cartography (Giraud and Lambert 2016), it offers the same main functionalities while being lighter and more ergonomic.\nTo use this package several sources can be consulted:\n\nThe package documentation accessible on the internet or directly in R (?mapsf),\nA cheat sheet,\n\n\n\n\n\n\n\nThe vignettes associated with the package show sample scripts,\nThe R Geomatics blog which provides resources and examples related to the package and more generally to the R spatial ecosystem."
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    "section": "1.4 The package terra",
    "text": "1.4 The package terra\n The package terra was release in early 2020 by Robert J. Hijmans (also author of raster). Its objective is to propose methods of treatment and analysis of raster data. This package is very similar to the package raster; but it has more features, it’s easier to use, and it’s faster.\n\n\n\n\n\n\nWebsite of package terra : Spatial Data Science with R and “terra”\n\n\n\n\n\n\n\n\n\nTip\n\n\n\nA benchmark of raster processing libraries is available here.\n\n\n\n\n\n\nBivand, Roger, Tim Keitt, and Barry Rowlingson. 2022. “Rgdal: Bindings for the ’Geospatial’ Data Abstraction Library.” https://CRAN.R-project.org/package=rgdal.\n\n\nBivand, Roger, and Colin Rundel. 2021. “Rgeos: Interface to Geometry Engine - Open Source (’GEOS’).” https://CRAN.R-project.org/package=rgeos.\n\n\nDunnington, Dewey. 2021. “Ggspatial: Spatial Data Framework for Ggplot2.” https://CRAN.R-project.org/package=ggspatial.\n\n\nGDAL/OGR contributors. n.d. GDAL/OGR Geospatial Data Abstraction Software Library. Open Source Geospatial Foundation. https://gdal.org.\n\n\nGiraud, Timothée. 2022. “Mapsf: Thematic Cartography.” https://CRAN.R-project.org/package=mapsf.\n\n\nGiraud, Timothée, and Nicolas Lambert. 2016. “Cartography: Create and Integrate Maps in Your r Workflow” 1. https://doi.org/10.21105/joss.00054.\n\n\nHijmans, Robert J. 2022a. “Raster: Geographic Data Analysis and Modeling.” https://CRAN.R-project.org/package=raster.\n\n\n———. 2022b. “Terra: Spatial Data Analysis.” https://CRAN.R-project.org/package=terra.\n\n\nPebesma, Edzer. 2018a. “Simple Features for r: Standardized Support for Spatial Vector Data” 10. https://doi.org/10.32614/RJ-2018-009.\n\n\n———. 2018b. “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal 10 (1): 439. https://doi.org/10.32614/rj-2018-009.\n\n\n———. 2021. “Stars: Spatiotemporal Arrays, Raster and Vector Data Cubes.” https://CRAN.R-project.org/package=stars.\n\n\nPebesma, Edzer J., and Roger S. Bivand. 2005. “Classes and Methods for Spatial Data in r” 5. https://CRAN.R-project.org/doc/Rnews/.\n\n\nPROJ contributors. 2021. PROJ Coordinate Transformation Software Library. Open Source Geospatial Foundation. https://proj.org/.\n\n\nTennekes, Martijn. 2018. “Tmap: Thematic Maps in r” 84. https://doi.org/10.18637/jss.v084.i06.\n\n\nWickham, Hadley. 2016. “Ggplot2: Elegant Graphics for Data Analysis.” https://ggplot2.tidyverse.org."
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    "text": "Bivand, Roger, Tim Keitt, and Barry Rowlingson. 2022. “Rgdal:\nBindings for the ’Geospatial’ Data Abstraction Library.” https://CRAN.R-project.org/package=rgdal.\n\n\nBivand, Roger, and Colin Rundel. 2021. “Rgeos: Interface to\nGeometry Engine - Open Source (’GEOS’).” https://CRAN.R-project.org/package=rgeos.\n\n\nDunnington, Dewey. 2021. “Ggspatial: Spatial Data Framework for\nGgplot2.” https://CRAN.R-project.org/package=ggspatial.\n\n\nGDAL/OGR contributors. n.d. GDAL/OGR Geospatial Data\nAbstraction Software Library. Open Source Geospatial Foundation. https://gdal.org.\n\n\nGiraud, Timothée. 2022. “Mapsf: Thematic Cartography.” https://CRAN.R-project.org/package=mapsf.\n\n\nGiraud, Timothée, and Nicolas Lambert. 2016. “Cartography: Create\nand Integrate Maps in Your r Workflow” 1. https://doi.org/10.21105/joss.00054.\n\n\nHijmans, Robert J. 2022a. “Raster: Geographic Data Analysis and\nModeling.” https://CRAN.R-project.org/package=raster.\n\n\n———. 2022b. “Terra: Spatial Data Analysis.” https://CRAN.R-project.org/package=terra.\n\n\nPebesma, Edzer. 2018b. “Simple Features for r:\nStandardized Support for Spatial Vector Data” 10. https://doi.org/10.32614/RJ-2018-009.\n\n\n———. 2018a. “Simple Features for R: Standardized Support for\nSpatial Vector Data.” The R Journal 10 (1): 439. https://doi.org/10.32614/rj-2018-009.\n\n\n———. 2021. “Stars: Spatiotemporal Arrays, Raster and Vector Data\nCubes.” https://CRAN.R-project.org/package=stars.\n\n\nPebesma, Edzer J., and Roger S. Bivand. 2005. “Classes and Methods\nfor Spatial Data in r” 5. https://CRAN.R-project.org/doc/Rnews/.\n\n\nPROJ contributors. 2021. PROJ Coordinate Transformation\nSoftware Library. Open Source Geospatial Foundation. https://proj.org/.\n\n\nTennekes, Martijn. 2018. “Tmap: Thematic\nMaps in r” 84. https://doi.org/10.18637/jss.v084.i06.\n\n\nWickham, Hadley. 2016. “Ggplot2: Elegant Graphics for Data\nAnalysis.” https://ggplot2.tidyverse.org."
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    "text": "The function st_as_sf() makes it possible to transform a data.frame container of geographic coordinates into an object sf. Here we use the data.frame places2 created in the previous point.\n\nlibrary(sf)\nplace_sf <- st_as_sf(read.csv(\"data_cambodia/adress.csv\"), coords = c(\"long\", \"lat\"), crs = 4326)\nplace_sf\n\nSimple feature collection with 2 features and 1 field\nGeometry type: POINT\nDimension:     XY\nBounding box:  xmin: 104.8443 ymin: 11.54366 xmax: 104.9047 ymax: 11.55349\nGeodetic CRS:  WGS 84\n                                                 address\n1 Phnom Penh International Airport, Phnom Penh, Cambodia\n2 Khmer Soviet Friendship Hospital, Phnom Penh, Cambodia\n                   geometry\n1 POINT (104.8443 11.55349)\n2 POINT (104.9047 11.54366)\n\n\nTo crate a sf POINT type object with only one pair of coordinate (WGS84, longitude=0.5, latitude = 45.5) :\n\nlibrary(sf)\ntest_point <- st_as_sf(data.frame(x = 0.5, y = 45.5), coords = c(\"x\", \"y\"), crs = 4326)\ntest_point\n\nSimple feature collection with 1 feature and 0 fields\nGeometry type: POINT\nDimension:     XY\nBounding box:  xmin: 0.5 ymin: 45.5 xmax: 0.5 ymax: 45.5\nGeodetic CRS:  WGS 84\n          geometry\n1 POINT (0.5 45.5)\n\n\nWe can display this object sf on an OpenStreetMap basesmap with the package maptiles maptiles (Giraud 2021).\n\nlibrary(maptiles)\nosm <- get_tiles(x = place_sf, zoom = 12)\nplot_tiles(osm)\nplot(st_geometry(place_sf), pch = 2, cex = 2, col = \"red\", add = TRUE)"
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    "text": "2.2 Online databases"
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    "section": "2.3 OpenStreetMap",
    "text": "2.3 OpenStreetMap\n\n\n\nOpenStreetMap (OSM) is a participatory mapping project that aim s to buil a free geographic database on a global scale. OpenStreetMap lets you view, edit and use geographic data around the world.\nTerms of use\n\nOpenStreetMap is open data : you are free to use it for ant purpose as long as you credit OpenStreetMap and its contributers. If you modify or rely data in any way, you may distribute the result only under the same license. (…)\n\nContributors\n\n(…) Our contributors incloude enthusiastic mapmakers, GIS professional, engineers running OSM servers, humanitarians mapping disaster-stricken areas and many mmore.(…)\n\n\n2.3.1 Display and interactive map\nThe two main packages that allow to display as interactive map based on OSM are leaflet (Cheng, Karambelkar, and Xie 2022) and mapview (Appelhans et al. 2022).\n\n2.3.1.1 leaflet\n leaflet uses the javascript library Leaflet (Agafonkin 2015) to create interactive maps.\n\nlibrary(sf)\nlibrary(leaflet)\n\ndistrict <- st_read(\"data_cambodia/cambodia.gpkg\", layer = \"district\", quiet = TRUE)\nhospital <- st_read(\"data_cambodia/cambodia.gpkg\", layer = \"hospital\", quiet = TRUE)\n\n\nbanan <- district[district$ADM2_PCODE == \"KH0201\", ]     #Select one district (Banan district: KH0201)\nhealth_banan <- hospital[hospital$DCODE == \"201\", ]      #Select Health centers in Banan\n\nbanan <- st_transform(banan, 4326)                       #Transform coordinate system to WGS84\nhealth_banan <- st_transform(health_banan, 4326)\n\nbanan_map <- leaflet(banan) %>%                          #Create interactive map\n  addTiles() %>%\n  addPolygons() %>%\n  addMarkers(data = health_banan)\nbanan_map\n\n\n\n\n\n\n\n\n\n\n\nWebsite of leaflet\nLeaflet for R\n\n\n\n\n\n2.3.1.2 mapview\n mapview relies on leaflet to create interactive maps, its use is easier and its documentation is a bit dense.\n\nlibrary(mapview)\nmapview(banan) + mapview(health_banan)\n\n\n\n\n\n\n\n\n\n\n\n\nWebsite of mapview\nmapview\n\n\n\n\n\n\n2.3.2 Import basemaps\nThe package maptiles (Giraud 2021) allows downlaoding and displaying raster basemaps.\nThe function get_tiles() allow you to download OSM background maps and the function plot_tiles() allows to display them.\nRenders are better if the input data used the same coordinate system as the tiles (EPSG:3857).\n\nlibrary(sf)\nlibrary(maptiles)\ndistrict <- st_read(\"data_cambodia/cambodia.gpkg\", layer = \"district\", quiet = TRUE)\ndistrict <- st_transform(district, 3857)\nosm_tiles <- get_tiles(x = district, zoom = 10, crop = TRUE)\nplot_tiles(osm_tiles)\nplot(st_geometry(district), border = \"grey20\", lwd = .7, add = TRUE)\nmtext(side = 1, line = -2, text = get_credit(\"OpenStreetMap\"), col=\"tomato\")\n\n\n\n\n\n\n2.3.3 Import OSM data\n\n2.3.3.1 osmdata\n The package osmdata (Padgham et al. 2017) allows extracting vector data from OSM using the Overpass turbo API.\n\nlibrary(sf)\nlibrary(osmdata)\nlibrary(sf)\n\ncountry <- st_read(\"data_cambodia/cambodia.gpkg\", layer = \"country\", quiet = TRUE)\next <- opq(bbox = st_bbox(st_transform(country, 4326)))                    #Define the bounding box\nquery <- add_osm_feature(opq = ext, key = 'amenity', value = \"hospital\")   #Health Center Extraction\nhospital <- osmdata_sf(query)\nhospital <- unique_osmdata(hospital)                                       #Result reduction (points composing polygon are detected)\n\nThe result contains a point layer and a polygon layer. The polygon layer contains polygons that represent fast food-food place. To obtain a coherent point layer we can use the centroids of the polygons.\n\nhospital_point <- hospital$osm_points\nhospital_poly <- hospital$osm_polygons                                                             #Extracting centroids of polygons\nhospital_poly_centroid <- st_centroid(hospital_poly)\n\ncambodia_point <- intersect(names(hospital_point), names(hospital_poly_centroid))                  #Identify fields in Cambodia boundary\nhospitals <- rbind(hospital_point[, cambodia_point], hospital_poly_centroid[, cambodia_point])     #Gather the 2 objects\n\nResult display\n\nlibrary(mapview)\nmapview(country) + mapview(hospitals)\n\n\n\n\n\n\n\n\n\n\n\n\nWebsite of osmdata\nosmdata\n\n\n\n\n\n2.3.3.2 osmextract\n The package osmextract (Gilardi and Lovelace 2021) allows to extract data from an OSM database directly. This package make it possible to work on very large volumes of data.\n\n\n\n\n\n\nWebsite of osmextract\nosmextract\n\n\n\nFor administrative boundaries, check here the administrative levels by country:\n\nlibrary(osmextract)\nlibrary(mapsf)\nprovince <- oe_get(\n  place = \"Cambodia\",\n  download_directory = \"data_cambodia/\",\n  layer = \"multipolygons\",\n  extra_tags = c(\"wikidata\", \"ISO3166-2\", \"wikipedia\", \"name:en\"),\n  vectortranslate_options = c(\n    \"-t_srs\", \"EPSG:32648\",\n    \"-nlt\", \"PROMOTE_TO_MULTI\",\n    \"-where\", \"type = 'boundary' AND boundary = 'administrative' AND admin_level = '4'\"\n  ))\n\n0...10...20...30...40...50...60...70...80...90...100 - done.\nReading layer `multipolygons' from data source \n  `/home/lucas/Documents/GitHub/rspatial-for-onehealth/data_cambodia/geofabrik_cambodia-latest.gpkg' \n  using driver `GPKG'\nSimple feature collection with 25 features and 29 fields\nGeometry type: MULTIPOLYGON\nDimension:     XY\nBounding box:  xmin: 211418.1 ymin: 1047956 xmax: 784614.9 ymax: 1625621\nProjected CRS: WGS 84 / UTM zone 48N\n\nmf_map(x = province)\n\n\n\n\n\nroads <- oe_get(\n  place = \"Cambodia\",\n  download_directory = \"data_cambodia/\",\n  layer = \"lines\",\n  extra_tags = c(\"access\", \"service\", \"maxspeed\"),\n  vectortranslate_options = c(\n  \"-t_srs\", \"EPSG:32648\",\n  \"-nlt\", \"PROMOTE_TO_MULTI\",\n  \"-where\", \"\n    highway IS NOT NULL\n    AND\n    highway NOT IN (\n    'abandonded', 'bus_guideway', 'byway', 'construction', 'corridor', 'elevator',\n    'fixme', 'escalator', 'gallop', 'historic', 'no', 'planned', 'platform',\n    'proposed', 'cycleway', 'pedestrian', 'bridleway', 'footway',\n    'steps', 'path', 'raceway', 'road', 'service', 'track'\n    )\n    \"\n),\n  boundary = subset(province, name_en == \"Phnom Penh\"),\n  boundary_type = \"clipsrc\"\n)\n\n0...10...20...30...40...50...60...70...80...90...100 - done.\nReading layer `lines' from data source \n  `/home/lucas/Documents/GitHub/rspatial-for-onehealth/data_cambodia/geofabrik_cambodia-latest.gpkg' \n  using driver `GPKG'\nSimple feature collection with 18794 features and 12 fields\nGeometry type: MULTILINESTRING\nDimension:     XY\nBounding box:  xmin: 469524.2 ymin: 1263268 xmax: 503494.3 ymax: 1296780\nProjected CRS: WGS 84 / UTM zone 48N\n\nmf_map(x = roads)"
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    "section": "2.4 Geocoding",
    "text": "2.4 Geocoding\nServeral pakages alow you to geocode addresses.  The package tidygeocoder (Cambon et al. 2021) allow the use of a large number of online geocoding sevices. The package banR (Gombin and Chevalier 2022), which is based on the National Address Base, is the particularly suitable for geocoding addresses in France.\n\n2.4.1 tidygeocoder\n\nlibrary(tidygeocoder)\ntest_adresses <- data.frame(\n  address = c(\"Phnom Penh International Airport, Phnom Penh, Cambodia\",\n              \"Khmer Soviet Friendship Hospital, Phnom Penh, Cambodia\"))\nplaces1 <- geocode(test_adresses, address)\nplaces1\n\n# A tibble: 2 × 3\n  address                                                  lat  long\n  <chr>                                                  <dbl> <dbl>\n1 Phnom Penh International Airport, Phnom Penh, Cambodia  11.6  105.\n2 Khmer Soviet Friendship Hospital, Phnom Penh, Cambodia  11.5  105.\n\n\n\n\n\n\n\n\nWebsite by tidygeocoder :\ntidygeocoder\n\n\n\n\n\n2.4.2 banR (Base Adresse Nationale)\n\n# remotes::install_github(\"joelgombin/banR\")\nlibrary(banR)\nmes_adresses <- data.frame(\n  address = c(\"19 rue Michel Bakounine, 29600 Morlaix, France\",\n              \"2 Allee Emile Pouget, 920128 Boulogne-Billancourt\")\n)\nplaces2 <- geocode_tbl(tbl = mes_adresses, adresse = address)\nplaces2\n\n# A tibble: 2 × 17\n  address        latit…¹ longi…² resul…³ resul…⁴ resul…⁵ resul…⁶ resul…⁷ resul…⁸\n  <chr>            <dbl>   <dbl> <chr>     <dbl> <chr>   <chr>   <chr>   <chr>  \n1 19 rue Michel…    48.6   -3.82 19 Rue…    0.81 housen… 29151_… 19      Rue Mi…\n2 2 Allee Emile…    48.8    2.24 2 Allé…    0.83 housen… 92012_… 2       Allée …\n# … with 8 more variables: result_street <chr>, result_postcode <chr>,\n#   result_city <chr>, result_context <chr>, result_citycode <chr>,\n#   result_oldcitycode <chr>, result_oldcity <chr>, result_district <chr>, and\n#   abbreviated variable names ¹​latitude, ²​longitude, ³​result_label,\n#   ⁴​result_score, ⁵​result_type, ⁶​result_id, ⁷​result_housenumber, ⁸​result_name\n\n\n\n\n\n\n\n\nWebsite of banR :\nAn R client for the BAN API"
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    "section": "2.5 Digitization",
    "text": "2.5 Digitization\nThe package mapedit (Appelhans, Russell, and Busetto 2020) allows you to digitize base map directly in R. Although it can be practical in some cases, in package cannot replace the functionalities of a GIS for important digitization tasks.\n\n\n\nGif taken from mapedit website\n\n\n\n\n\n\nAgafonkin, Vladimir. 2015. “Leaflet Javascript Libary.”\n\n\nAppelhans, Tim, Florian Detsch, Christoph Reudenbach, and Stefan Woellauer. 2022. “Mapview: Interactive Viewing of Spatial Data in r.” https://CRAN.R-project.org/package=mapview.\n\n\nAppelhans, Tim, Kenton Russell, and Lorenzo Busetto. 2020. “Mapedit: Interactive Editing of Spatial Data in r.” https://CRAN.R-project.org/package=mapedit.\n\n\nCambon, Jesse, Diego Hernangómez, Christopher Belanger, and Daniel Possenriede. 2021. “Tidygeocoder: An r Package for Geocoding” 6: 3544. https://doi.org/10.21105/joss.03544.\n\n\nCheng, Joe, Bhaskar Karambelkar, and Yihui Xie. 2022. “Leaflet: Create Interactive Web Maps with the JavaScript ’Leaflet’ Library.” https://CRAN.R-project.org/package=leaflet.\n\n\nGilardi, Andrea, and Robin Lovelace. 2021. “Osmextract: Download and Import Open Street Map Data Extracts.” https://CRAN.R-project.org/package=osmextract.\n\n\nGiraud, Timothée. 2021. “Maptiles: Download and Display Map Tiles.” https://CRAN.R-project.org/package=maptiles.\n\n\nGombin, Joel, and Paul-Antoine Chevalier. 2022. “banR: R Client for the BAN API.”\n\n\nPadgham, Mark, Bob Rudis, Robin Lovelace, and Maëlle Salmon. 2017. “Osmdata” 2. https://doi.org/10.21105/joss.00305."
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