{ "_id": "6a1f17ffb401979e7341ec1f", "Package": "nimbleSCR", "Type": "Package", "Title": "Spatial Capture-Recapture (SCR) Methods Using 'nimble'", "Version": "0.2.1", "Maintainer": "Daniel Turek ", "Authors@R": "c(person(\"Richard\", \"Bischof\", role = \"aut\"),\nperson(\"Daniel\", \"Turek\", role = c(\"aut\", \"cre\"), email = \"danielturek@gmail.com\"),\nperson(\"Cyril\", \"Milleret\", role = \"aut\"),\nperson(\"Torbjørn\", \"Ergon\", role = \"aut\"),\nperson(\"Pierre\", \"Dupont\", role = \"aut\"),\nperson(\"Soumen\", \"Dey\", role = \"aut\"),\nperson(\"Wei\", \"Zhang\", role = \"aut\"),\nperson(\"Perry\", \"de Valpine\", role = \"aut\"))", "Date": "2022-11-30", "Description": "Provides utility functions, distributions, and fitting\nmethods for Bayesian Spatial Capture-Recapture (SCR) and Open\nPopulation Spatial Capture-Recapture (OPSCR) modelling using\nthe nimble package (de Valpine et al. 2017\n). Development of the\npackage was motivated primarily by the need for flexible and\nefficient analysis of large-scale SCR data (Bischof et al. 2020\n). Computational methods and\ntechniques implemented in nimbleSCR include those discussed in\nTurek et al. 2021 ; among others. For a\nrecent application of nimbleSCR, see Milleret et al. (2021)\n.", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "7.2.1", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Collate": "calcWindowSizes.R getWindowIndex.R\nintegrateIntensityLocal_normal.R integrateIntensityLocal_exp.R\nintegrateIntensity_normal.R integrateIntensity_exp.R\nstratRejectionSampler_normal.R stratRejectionSampler_exp.R\ndDispersal_exp.R dHabitatMask.R dbernppAC.R\ndbernppACmovement_normal.R dbernppACmovement_exp.R\ndbernppDetection_normal.R dbernppLocalACmovement_normal.R\ndbernppLocalACmovement_exp.R dbernppLocalDetection_normal.R\ndbinomLocal_normal.R dmultiLocal_normal.R dbinom_vector.R\ndnormalizer.R dpoisLocal_normal.R dbinomLocal_normalPlateau.R\ndbinomLocal_exp.R dpoisppAC.R dpoisppDetection_normal.R\ndpoisppLocalDetection_normal.R getLocalObjects.R\ngetMidPointNodes.R getWindowCoords.R getSparseY.R\ngetHomeRangeArea.R localTrapCalculations.R\nmakeConstantNimbleFunction.R marginalVoidProbIntegrand.R\nmarginalVoidProbNumIntegration.R scaleCoordsToHabitatGrid.R\ndcatState1Alive1Dead.R dcatState1Alive2Dead.R\ndcatState2Alive2Dead.R sampler_categorical_general.R\ncalculateDensity.R zzz.R", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-17 05:51:17 UTC", "User": "root" }, "Author": "Richard Bischof [aut], Daniel Turek [aut, cre], Cyril Milleret\n[aut], Torbjørn Ergon [aut], Pierre Dupont [aut], Soumen Dey\n[aut], Wei Zhang [aut], Perry de Valpine [aut]", "Config/pak/sysreqs": "libglpk-dev make libxml2-dev", "Repository": "https://danielturek.r-universe.dev", "Date/Publication": "2022-11-30 14:30:02 UTC", "RemoteUrl": "https://github.com/cran/nimbleSCR", "RemoteRef": "HEAD", "RemoteSha": "8449287983781e401dc883c7fd85e08f7f576025", "MD5sum": "34ee93c6d6273a57d616e3cf0df2caf2", "_user": "danielturek", "_type": "src", "_file": "nimbleSCR_0.2.1.tar.gz", "_fileid": "bbdf87dcf4b7caade13d122334317afc041e117fa214657ef7cc8c61289b9552", "_filesize": 7397166, "_sha256": "bbdf87dcf4b7caade13d122334317afc041e117fa214657ef7cc8c61289b9552", "_created": "2026-05-17T05:51:17.000Z", "_published": "2026-06-02T17:50:55.595Z", "_distro": "noble", "_jobs": [ { "job": 79136187688, "time": 165, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "7039779516" }, { "job": 79136187702, "time": 166, "config": "linux-release-x86_64", "r": "4.6.0", "check": "NOTE", "artifact": "7039779682" }, { "job": 79136188016, "time": 111, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "NOTE", "artifact": "7039773174" }, { "job": 79136188055, "time": 145, "config": "macos-release-arm64", "r": "4.6.0", "check": "NOTE", "artifact": "7039777293" }, { "job": 79136187491, "time": 301, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7039760167" }, { "job": 79136187000, "time": 114, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7365614842" }, { "job": 79136187926, "time": 106, "config": "windows-devel", "r": "4.7.0", "check": "NOTE", "artifact": "7039772613" }, { "job": 79136187760, "time": 123, "config": "windows-oldrel", "r": "4.5.3", "check": "NOTE", "artifact": "7039774955" }, { "job": 79136188082, "time": 151, "config": "windows-release", "r": "4.6.0", "check": "NOTE", "artifact": "7039777931" } ], "_buildurl": "https://github.com/r-universe/danielturek/actions/runs/25982722211", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/nimbleSCR", "_commit": { "id": "8449287983781e401dc883c7fd85e08f7f576025", "author": "Daniel Turek ", "committer": "cran-robot ", "message": "version 0.2.1\n", "time": 1669818602 }, "_maintainer": { "name": "Daniel Turek", "email": "danielturek@gmail.com", "login": "danielturek", "description": "", "uuid": 4511457 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "nimble", "role": "Depends" }, { "package": "methods", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "coda", "role": "Suggests" }, { "package": "basicMCMCplots", "role": "Suggests" } ], "_owner": "cran", "_selfowned": true, "_usedby": 0, "_updates": [], "_tags": [], "_stars": 0, "_contributors": [ { "user": "danielturek", "count": 6, "uuid": 4511457 } ], "_userbio": { "uuid": 4511457, "type": "user", "name": "Daniel Turek" }, "_downloads": { "count": 230, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/nimbleSCR" }, "_mentions": 1, "_searchresults": 237, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/nimbleSCR.html", "manual.pdf" ], "_realowner": "danielturek", "_cranurl": false, "_releases": [ { "version": "0.1.0", "date": "2020-08-20" }, { "version": "0.1.1", "date": "2021-01-07" }, { "version": "0.1.2", "date": "2021-07-01" }, { "version": "0.1.3", "date": "2021-10-25" }, { "version": "0.2.0", "date": "2022-09-02" }, { "version": "0.2.1", "date": "2022-11-30" } ], "_exports": [ "calcLocalTrapDists", "calcLocalTrapExposure", "calculateDensity", "calcWindowSizes", "dbernppAC", "dbernppACmovement_exp", "dbernppACmovement_normal", "dbernppDetection_normal", "dbernppLocalACmovement_exp", "dbernppLocalACmovement_normal", "dbernppLocalDetection_normal", "dbinom_vector", "dbinomLocal_exp", "dbinomLocal_normal", "dbinomLocal_normalPlateau", "dcatState1Alive1Dead", "dcatState1Alive2Dead", "dcatState2Alive2Dead", "dDispersal_exp", "dHabitatMask", "dmultiLocal_normal", "dnormalizer", "dpoisLocal_normal", "dpoisppAC", "dpoisppDetection_normal", "dpoisppLocalDetection_normal", "findLocalTraps", "getHomeRangeArea", "getLocalObjects", "getLocalTrapIndices", "getMidPointNodes", "getNumLocalTraps", "getSparseY", "getWindowCoords", "getWindowIndex", "integrateIntensity_exp", "integrateIntensity_normal", "integrateIntensityLocal_exp", "integrateIntensityLocal_normal", "makeConstantNimbleFunction", "makeGrid", "marginalVoidProbIntegrand", "marginalVoidProbNumIntegration", "rbernppAC", "rbernppACmovement_exp", "rbernppACmovement_normal", "rbernppDetection_normal", "rbernppLocalACmovement_exp", "rbernppLocalACmovement_normal", "rbernppLocalDetection_normal", "rbinom_vector", "rbinomLocal_exp", "rbinomLocal_normal", "rbinomLocal_normalPlateau", "rcatState1Alive1Dead", "rcatState1Alive2Dead", "rcatState2Alive2Dead", "rDispersal_exp", "rHabitatMask", "rmultiLocal_normal", "rnormalizer", "rpoisLocal_normal", "rpoisppAC", "rpoisppDetection_normal", "rpoisppLocalDetection_normal", "sampler_categorical_general", "scaleCoordsToHabitatGrid", "stratRejectionSampler_exp", "stratRejectionSampler_normal" ], "_help": [ { "page": "calculateDensity", "title": "NIMBLE function to calculate the density of individuals alive in each habitat cell.", "topics": [ "calculateDensity" ] }, { "page": "calcWindowSizes", "title": "Window size calculation", "topics": [ "calcWindowSizes" ] }, { "page": "dbernppAC", "title": "Bernoulli point process for the distribution of activity centers", "topics": [ "dbernppAC", "rbernppAC" ] }, { "page": "dbernppACmovement_exp", "title": "Bernoulli point process for activity center movement (exponential kernel)", "topics": [ "dbernppACmovement_exp", "rbernppACmovement_exp" ] }, { "page": "dbernppACmovement_normal", "title": "Bernoulli point process for activity center movement (normal kernel)", "topics": [ "dbernppACmovement_normal", "rbernppACmovement_normal" ] }, { "page": "dbernppDetection_normal", "title": "Bernoulli point process detection model", "topics": [ "dbernppDetection_normal", "rbernppDetection_normal" ] }, { "page": "dbernppLocalACmovement_exp", "title": "Local evaluation of a Bernoulli point process for activity center movement (exponential kernel)", "topics": [ "dbernppLocalACmovement_exp", "rbernppLocalACmovement_exp" ] }, { "page": "dbernppLocalACmovement_normal", "title": "Local evaluation of a Bernoulli point process for activity center movement (normal kernel)", "topics": [ "dbernppLocalACmovement_normal", "rbernppLocalACmovement_normal" ] }, { "page": "dbernppLocalDetection_normal", "title": "Local evaluation for a Bernoulli point process detection model", "topics": [ "dbernppLocalDetection_normal", "rbernppLocalDetection_normal" ] }, { "page": "dbinom_vector", "title": "Vectorized binomial distribution", "topics": [ "dbinom_vector", "rbinom_vector" ] }, { "page": "dbinomLocal_exp", "title": "Local evaluation of a binomial SCR observation process", "topics": [ "dbinomLocal_exp", "rbinomLocal_exp" ] }, { "page": "dbinomLocal_normal", "title": "Local evaluation of a binomial SCR detection process", "topics": [ "dbinomLocal_normal", "rbinomLocal_normal" ] }, { "page": "dbinomLocal_normalPlateau", "title": "Local evaluation of a binomial SCR observation process", "topics": [ "dbinomLocal_normalPlateau", "rbinomLocal_normalPlateau" ] }, { "page": "dcatState1Alive1Dead", "title": "Density and random generation of a categorical distribution describing state transition with one alive and one dead states.", "topics": [ "dcatState1Alive1Dead", "rcatState1Alive1Dead" ] }, { "page": "dcatState1Alive2Dead", "title": "Density and random generation of a categorical distribution describing state transition with one alive and two dead states.", "topics": [ "dcatState1Alive2Dead", "rcatState1Alive2Dead" ] }, { "page": "dcatState2Alive2Dead", "title": "Density and random generation of a categorical distribution describing state transition with two alive and two dead states.", "topics": [ "dcatState2Alive2Dead", "rcatState2Alive2Dead" ] }, { "page": "dDispersal_exp", "title": "Bivariate exponential dispersal distribution for activity centers", "topics": [ "dDispersal_exp", "rDispersal_exp" ] }, { "page": "dHabitatMask", "title": "Ones trick distribution for irregular habitat shapes", "topics": [ "dHabitatMask", "rHabitatMask" ] }, { "page": "dmultiLocal_normal", "title": "Local evaluation of a multinomial SCR detection process", "topics": [ "dmultiLocal_normal", "rmultiLocal_normal" ] }, { "page": "dnormalizer", "title": "Normalizing constant generator", "topics": [ "dnormalizer", "rnormalizer" ] }, { "page": "dpoisLocal_normal", "title": "Local evaluation of a Poisson SCR detection process", "topics": [ "dpoisLocal_normal", "rpoisLocal_normal" ] }, { "page": "dpoisppAC", "title": "Poisson point process for the distribution of activity centers", "topics": [ "dpoisppAC", "rpoisppAC" ] }, { "page": "dpoisppDetection_normal", "title": "Poisson point process detection model", "topics": [ "dpoisppDetection_normal", "rpoisppDetection_normal" ] }, { "page": "dpoisppLocalDetection_normal", "title": "Local evaluation for a Poisson point process detection model", "topics": [ "dpoisppLocalDetection_normal", "rpoisppLocalDetection_normal" ] }, { "page": "getHomeRangeArea", "title": "Computation of home range radius and area", "topics": [ "getHomeRangeArea" ] }, { "page": "getLocalObjects", "title": "Local Objects Identification", "topics": [ "getLocalObjects" ] }, { "page": "getMidPointNodes", "title": "Generate midpoint integration nodes", "topics": [ "getMidPointNodes" ] }, { "page": "getSparseY", "title": "Sparse Matrix Preparation", "topics": [ "getSparseY" ] }, { "page": "getWindowCoords", "title": "Get lower and upper windows coordinates", "topics": [ "getWindowCoords" ] }, { "page": "getWindowIndex", "title": "Get window index", "topics": [ "getWindowIndex" ] }, { "page": "integrateIntensity_exp", "title": "Integrate the multivariate exponential intensity", "topics": [ "integrateIntensity_exp" ] }, { "page": "integrateIntensity_normal", "title": "Integrate the multivariate normal intensity", "topics": [ "integrateIntensity_normal" ] }, { "page": "integrateIntensityLocal_normal", "title": "Integrate the multivariate normal intensity with local evaluation", "topics": [ "integrateIntensityLocal_normal" ] }, { "page": "localTrapCalculations", "title": "Local Trap Calculations", "topics": [ "calcLocalTrapDists", "calcLocalTrapExposure", "findLocalTraps", "getLocalTrapIndices", "getNumLocalTraps", "localTrapCalculations", "makeGrid" ] }, { "page": "marginalVoidProbIntegrand", "title": "Integrand of the marginal void probability integral", "topics": [ "marginalVoidProbIntegrand" ] }, { "page": "marginalVoidProbNumIntegration", "title": "Marginal void probability", "topics": [ "marginalVoidProbNumIntegration" ] }, { "page": "sampler_categorical_general", "title": "'nimble' MCMC sampler function for general categorial distributions", "topics": [ "sampler_categorical_general" ] }, { "page": "scaleCoordsToHabitatGrid", "title": "Scale x- and y-coordinates to grid cells coordinates.", "topics": [ "scaleCoordsToHabitatGrid" ] }, { "page": "stratRejectionSampler_exp", "title": "Stratified rejection sampler for multivariate exponential point process", "topics": [ "stratRejectionSampler_exp" ] }, { "page": "stratRejectionSampler_normal", "title": "Stratified rejection sampler for multivariate normal point process", "topics": [ "stratRejectionSampler_normal" ] } ], "_rundeps": [ "cli", "coda", "cpp11", "glue", "igraph", "lattice", "lifecycle", "magrittr", "Matrix", "nimble", "numDeriv", "pkgconfig", "pracma", "R6", "rlang", "vctrs" ], "_vignettes": [ { "source": "Fit_with_dbinomLocal_normalPlateau_and_HomeRangeAreaComputation.rmd", "filename": "Fit_with_dbinomLocal_normalPlateau_and_HomeRangeAreaComputation.html", "title": "Fit with half-normal plateau detection function and home range size estimation using nimbleSCR package", "author": "Soumen Dey", "engine": "knitr::rmarkdown", "headings": [ "1. Binomial SCR Model with half-normal plateau detection function", "1.1 Habitat and trapping grid", "1.2 Define SCR model with half-normal plateau detection function", "1.3 Set parameter values to simulate", "1.4 Create data, constants and inits objects", "1.5 Create NIMBLE model", "1.6 Simulate SCR data from the NIMBLE model", "1.7. MCMC with NIMBLE", "1.7.1 Compile the nimble model and create MCMC configuration", "1.7.2 Update the 'adaptive' arguments of MCMC samplers for sigma and w", "1.7.3 Run MCMC", "1.8. Computation of home range size", "2. Calculating home range radius and area with other detection functions", "Half-normal (detFun = 0)", "Exponential (detFun = 2)", "Asymmetric logistic (detFun = 3)", "Bimodal (detFun = 4)", "Donut (detFun = 5)", "REMARK", "REFERENCES" ], "created": "2022-09-02 09:40:02", "modified": "2022-09-02 09:40:02", "commits": 1 }, { "source": "Point_Process.Rmd", "filename": "Point_Process.html", "title": "Point process Bayesian SCR models with nimbleSCR", "author": "Cyril Milleret, Wei Zhang, Pierre Dupont and Richard Bischof", "engine": "knitr::rmarkdown", "headings": [ "1. Simulate SCR data", "1.1 Habitat and trapping grid", "1.2 Rescale coordinates", "1.3 Model definition", "1.4 Set up parameter values", "1.5 Create data, constants and initial values", "1.6 Create NIMBLE model", "1.7 Simulate data", "2. Fit model with data augmentation", "2.1. Prepare the input data", "2.2. Run MCMC with NIMBLE", "3. Fit model without data augmentation", "3.1. Model definition", "3.2. Prepare the input data", "3.3. Run MCMC with NIMBLE", "REFERENCES" ], "created": "2021-10-25 14:50:02", "modified": "2022-09-02 09:40:02", "commits": 2 }, { "source": "WolverinePointProcess.Rmd", "filename": "WolverinePointProcess.html", "title": "Using nimbleSCR to fit 'point process SCR models' to wolverine (Gulo gulo) non-invasive genetic sampling data", "author": "Pierre Dupont", "engine": "knitr::rmarkdown", "headings": [ "Load Libraries", "Define 'nimbleSCR' Models", "Load Wolverine Data", "Create NIMBLE Model", "Configure and Build MCMC", "Compile and Run MCMC", "Results", "Conclusion", "References" ], "created": "2022-09-02 09:40:02", "modified": "2022-09-02 09:40:02", "commits": 1 }, { "source": "Simulate_and_fit_SCR_models_with_dbinomLocal_normal.Rmd", "filename": "Simulate_and_fit_SCR_models_with_dbinomLocal_normal.html", "title": "Using nimbleSCR to simulate and fit Bayesian SCR models", "author": "Cyril Milleret", "engine": "knitr::rmarkdown", "headings": [ "1. SIMULATE BINOMIAL SCR DATA", "1.1 Habitat and trapping grid", "1.2 Rescale coordinates and local evaluation", "1.3 Define model code", "1.4 Define parameter values to simulate", "1.5 Create data, constants and inits objects", "1.6 Create NIMBLE model", "1.7 Simulate SCR data from the NIMBLE model", "2. RUN MCMC WITH NIMBLE", "3. SIMULATE BINOMIAL SCR DATA WITH TRAP COVARIATES ON $p_0$", "3.1 Simulate trap covariates", "3.2 Define model code", "3.3 Create NIMBLE model", "3.4 Simulate SCR from the nimble model", "4. RUN MCMC WITH NIMBLE", "REFERENCES" ], "created": "2021-07-01 19:20:03", "modified": "2022-09-02 09:40:02", "commits": 3 }, { "source": "wolverine_example.Rmd", "filename": "wolverine_example.html", "title": "Wolverine Example", "author": "Cyril Milleret, Daniel Turek and Pierre Dupont", "engine": "knitr::rmarkdown", "headings": [ "Load Libraries", "Load nimbleSCR package", "Define Model Structure", "Load and Process Data", "Use of custom distribution to increase MCMC efficiency", "1. Local detector evaluation", "2. Sparse representation of the observation matrix", "3. Skip unnecessary calculations", "Create NIMBLE Model", "Configure and Build MCMC", "Block sampling to increase MCMC efficiency", "Compile and Run MCMC", "Results", "References" ], "created": "2021-07-01 19:20:03", "modified": "2021-07-01 19:20:03", "commits": 1 } ], "_score": 4.073718350346123, "_indexed": true, "_nocasepkg": "nimblescr", "_universes": [ "danielturek" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.2.1", "date": "2026-05-17T05:53:40.000Z", "distro": "noble", "commit": "8449287983781e401dc883c7fd85e08f7f576025", "fileid": "1231ed02f2ecc5fdbe17c1718a0dc0317dbd5ee53f20339bb2ba197833f00d38", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/danielturek/actions/runs/25982722211" }, { "r": "4.6.0", "os": "linux", "version": "0.2.1", "date": "2026-05-17T05:53:40.000Z", "distro": "noble", "commit": "8449287983781e401dc883c7fd85e08f7f576025", "fileid": "ffebb681a8b767eb2c8b3c0b709ced2ff1ad58faccf474be8b540bc175c6b3e7", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/danielturek/actions/runs/25982722211" }, { "r": "4.5.3", "os": "mac", "version": "0.2.1", "date": "2026-05-17T05:53:02.000Z", "commit": "8449287983781e401dc883c7fd85e08f7f576025", "fileid": "46851ec3171619f749c7b5391b1d1e0205e79df68b90ee909301c22e02ba91a5", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/danielturek/actions/runs/25982722211" }, { "r": "4.6.0", "os": "mac", "version": "0.2.1", "date": "2026-05-17T05:53:21.000Z", "commit": "8449287983781e401dc883c7fd85e08f7f576025", "fileid": "1067f7cbb646f318690f0b70beb02679c3f2043487c56124ae591ee23b35441f", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/danielturek/actions/runs/25982722211" }, { "r": "4.7.0", "os": "win", "version": "0.2.1", "date": "2026-05-17T05:52:41.000Z", "commit": "8449287983781e401dc883c7fd85e08f7f576025", "fileid": "9960ffbbc2d0fa5c462c658f421db6ecbd85761eff5d3e2f586ed5ea6364c44e", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/danielturek/actions/runs/25982722211" }, { "r": "4.5.3", "os": "win", "version": "0.2.1", "date": "2026-05-17T05:52:57.000Z", "commit": "8449287983781e401dc883c7fd85e08f7f576025", "fileid": "7ed0e78128a44554674e12e61c659ed22410615fddc023d81067f9b563e5559d", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/danielturek/actions/runs/25982722211" }, { "r": "4.6.0", "os": "win", "version": "0.2.1", "date": "2026-05-17T05:53:07.000Z", "commit": "8449287983781e401dc883c7fd85e08f7f576025", "fileid": "6290cd48e0b93f79846fa6ffb154718e7638bc0a503b30ade3aa3da303780415", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/danielturek/actions/runs/25982722211" }, { "r": "4.6.0", "os": "wasm", "version": "0.2.1", "date": "2026-06-02T17:50:35.000Z", "commit": "8449287983781e401dc883c7fd85e08f7f576025", "fileid": "b178f9aa335c6e8de5b2caa68190ddf23be3d39cd49c6406e57a2b9c39ae45b7", "status": "success", "buildurl": "https://github.com/r-universe/danielturek/actions/runs/25982722211" } ] }