{ "_id": "6aca46cffe5cc25786e86695", "Package": "sptrends", "Title": "Statistical Inference for Spatiotemporal Trends in Gridded Data", "Version": "1.6.3", "Language": "en-GB", "Authors@R": "c(\nperson(\"Oliver\", \"Gutiérrez-Hernández\", , \"olivergh@uma.es\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-2580-5465\", affiliation = \"Department of Geography, University of Málaga, Málaga, Spain\")),\nperson(\"Luis V.\", \"García\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-5514-2941\", affiliation = \"Institute of Natural Resources and Agrobiology of Seville (IRNAS), Spanish National Research Council (CSIC), Seville, Spain\")))", "Description": "Provides a unified and reproducible framework for\nstatistical inference of spatiotemporal trends in gridded\nenvironmental data. The framework addresses the interconnected\nchallenges of serial correlation, spatial dependence and\nmultiple testing that commonly arise when analysing gridded\nenvironmental time series. Its core methods support\nserial-correlation treatment through trend-preserving\nprewhitening, pixel-wise and spatially explicit trend\ninference, slope estimation and multiple-testing correction.\nThese methods may be applied independently or integrated within\nconfigurable analytical workflows. Dedicated workflows are also\nprovided to reproduce methodologies published in the scientific\nliterature: Gutiérrez-Hernández and García (2025)\n for the True Significant\nTrends workflow, Gutiérrez-Hernández and García (2024)\n for the Robust Trend Analysis\nworkflow, and Gutiérrez-Hernández and García (2025)\n for the adaptive false discovery\nrate procedure. Supporting utilities facilitate raster data\nimport and inspection, anomaly calculation, spatial\nautocorrelation diagnostics, simulation studies, benchmarking,\nvisualisation, mapping, and reporting.", "License": "GPL (>= 3)", "URL": "https://github.com/Olive-r/sptrends,\nhttps://olive-r.github.io/sptrends/", "BugReports": "https://github.com/Olive-r/sptrends/issues", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.1.0", "Config/pak/sysreqs": "libgdal-dev gdal-bin libgeos-dev libproj-dev\nlibsqlite3-dev", "Repository": "https://olive-r.r-universe.dev", "Date/Publication": "2026-10-10 13:21:13 UTC", "RemoteUrl": "https://github.com/olive-r/sptrends", "RemoteRef": "HEAD", "RemoteSha": "4c3413de8dd2efe86edeb7ffad4c9aa655313436", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-10 13:58:55 UTC", "User": "root" }, "Author": "Oliver Gutiérrez-Hernández [aut, cre] (ORCID:\n, affiliation: Department of\nGeography, University of Málaga, Málaga, Spain),\nLuis V. García [aut] (ORCID: ,\naffiliation: Institute of Natural Resources and Agrobiology of\nSeville (IRNAS), Spanish National Research Council (CSIC), Seville,\nSpain)", "Maintainer": "Oliver Gutiérrez-Hernández ", "_user": "olive-r", "_type": "src", "_file": "sptrends_1.6.3.tar.gz", "_fileid": "https://r2.ropensci.org/9573c5545a48cbaa660c2a071ac135b4ec41dc915497c0d8dd723172d39dce3a", "_filesize": 8746027, "_sha256": "9573c5545a48cbaa660c2a071ac135b4ec41dc915497c0d8dd723172d39dce3a", "_expires": "2027-01-18T14:08:13.000Z", "_created": "2026-10-10T13:58:55.000Z", "_published": "2026-10-10T14:08:15.300Z", "_buildurl": "https://github.com/r-universe/olive-r/actions/runs/38057576921", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 114229851850, "time": 326, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11672400142" }, { "job": 114229851769, "time": 334, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11670939041" }, { "job": 114229851818, "time": 211, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11672161016" }, { "job": 114229851775, "time": 230, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11671393533" }, { "job": 114229142437, "time": 235, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11671949091" }, { "job": 114229851774, "time": 142, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11671739632" }, { "job": 114229851844, "time": 277, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11671794867" }, { "job": 114229851781, "time": 197, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11671219990" }, { "job": 114229851780, "time": 282, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11672205331" } ], "_upstream": "https://github.com/olive-r/sptrends", "_commit": { "id": "4c3413de8dd2efe86edeb7ffad4c9aa655313436", "author": "Oliver Gutierrez-Hernandez <59426319+Olive-r@users.noreply.github.com>", "committer": "GitHub ", "message": "Add CRAN DOI and checks badges to README\n\nAdded badges for CRAN DOI and CRAN checks to README.", "time": 1791638473 }, "_maintainer": { "name": "Oliver Gutiérrez-Hernández", "email": "olivergh@uma.es", "login": "olive-r", "orcid": "0000-0003-2580-5465", "description": "Profesor Titular de Universidad (Associate Professor), Universidad de Málaga (RoR: 036b2ww28). ", "uuid": 59426319 }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1", "role": "Depends" }, { "package": "terra", "version": ">= 1.7-0", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "parallel", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "withr", "version": ">= 2.2.0", "role": "Imports" }, { "package": "fields", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.2.0", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "ncdf4", "role": "Suggests" }, { "package": "Kendall", "role": "Suggests" }, { "package": "modifiedmk", "role": "Suggests" }, { "package": "rkt", "role": "Suggests" }, { "package": "robslopes", "role": "Suggests" }, { "package": "trend", "role": "Suggests" }, { "package": "zyp", "role": "Suggests" } ], "_owner": "olive-r", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2026-39", "n": 1 }, { "week": "2026-41", "n": 2 } ], "_tags": [ { "name": "v1.0", "date": "2026-08-05" }, { "name": "v.1.1", "date": "2026-08-07" }, { "name": "v1.3.4", "date": "2026-08-08" }, { "name": "v1.4.2", "date": "2026-08-09" }, { "name": "v1.5", "date": "2026-08-10" }, { "name": "v1.5.9", "date": "2026-09-03" }, { "name": "v1.6.1", "date": "2026-09-06" }, { "name": "v1.6.2", "date": "2026-09-10" }, { "name": "v1.6.3", "date": "2026-09-22" } ], "_stars": 1, "_contributors": [ { "user": "olive-r", "count": 3, "uuid": 59426319 } ], "_userbio": { "uuid": 59426319, "type": "user", "name": "Oliver Gutierrez-Hernandez", "followers": 0, "description": "Profesor Titular de Universidad (Associate Professor), Universidad de Málaga (RoR: 036b2ww28). " }, "_downloads": { "count": 421, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/sptrends" }, "_devurl": "https://github.com/olive-r/sptrends", "_pkgdown": "https://olive-r.github.io/sptrends/", "_searchresults": 0, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/sptrends.html" ], "_homeurl": "https://github.com/olive-r/sptrends", "_realowner": "olive-r", "_cranurl": true, "_releases": [ { "version": "1.6.3", "date": "2026-09-22" } ], "_exports": [ "benchmark_methods", "benchmark_summary", "compare_detections", "compute_anomalies", "example_data", "fdr_correction", "inspect_ts_cell", "prepare_cmk_neighbourhood", "prewhiten", "read_netcdf_stack", "read_ordered_stack", "sim_trend_stack", "simulation_design", "slope_estimator", "spatial_autocorrelation", "trend_test", "workflow_rta", "workflow_trends", "workflow_tst" ], "_help": [ { "page": "benchmark_methods", "title": "Benchmark statistical methods across known-truth simulation scenarios", "concept": [ "validation functions" ], "topics": [ "benchmark_methods" ] }, { "page": "benchmark_summary", "title": "Summarise a method benchmark across Monte Carlo replicates", "concept": [ "validation functions" ], "topics": [ "benchmark_summary" ] }, { "page": "compare_detections", "title": "Compare detection methods against a known ground truth", "concept": [ "validation functions" ], "topics": [ "compare_detections" ] }, { "page": "compute_anomalies", "title": "Remove the seasonal cycle from raster time series", "concept": [ "preprocessing functions" ], "topics": [ "compute_anomalies" ] }, { "page": "example_data", "title": "Path to sptrends' bundled example dataset", "concept": [ "example data functions" ], "topics": [ "example_data" ] }, { "page": "fdr_correction", "title": "Apply false discovery rate (FDR) correction to multiple p-values", "concept": [ "FDR correction functions" ], "topics": [ "fdr_correction" ] }, { "page": "inspect_ts_cell", "title": "Inspect a single cell's (or area's) raw time series interactively", "concept": [ "reporting functions" ], "topics": [ "inspect_ts_cell" ] }, { "page": "plot.sptrends", "title": "Plot a sptrends result", "topics": [ "plot.sptrends" ] }, { "page": "prepare_cmk_neighbourhood", "title": "Precompute a CMK spatial neighbourhood", "concept": [ "Contextual Mann-Kendall functions" ], "topics": [ "prepare_cmk_neighbourhood" ] }, { "page": "prewhiten", "title": "AR(1) prewhitening of raster time series", "concept": [ "Prewhitening functions" ], "topics": [ "prewhiten" ] }, { "page": "print.sptrends", "title": "Print a sptrends result", "topics": [ "print.sptrends" ] }, { "page": "read_netcdf_stack", "title": "Read and chronologically order a single multi-temporal NetCDF file", "concept": [ "Data import functions" ], "topics": [ "read_netcdf_stack" ] }, { "page": "read_ordered_stack", "title": "Read and chronologically order a folder of raster files", "concept": [ "Data import functions" ], "topics": [ "read_ordered_stack" ] }, { "page": "sim_trend_stack", "title": "Generate a synthetic gridded time series with known true trends", "concept": [ "example data functions" ], "topics": [ "sim_trend_stack" ] }, { "page": "simulation_design", "title": "Build a factorial design of simulation scenarios", "concept": [ "validation functions" ], "topics": [ "simulation_design" ] }, { "page": "slope_estimator", "title": "Slope estimators for raster time series", "concept": [ "Slope estimation functions" ], "topics": [ "slope_estimator" ] }, { "page": "spatial_autocorrelation", "title": "Permutation-based spatial autocorrelation tests", "concept": [ "Spatial autocorrelation diagnostic functions" ], "topics": [ "spatial_autocorrelation" ] }, { "page": "summary.sptrends", "title": "Summarise a sptrends result", "topics": [ "summary.sptrends" ] }, { "page": "trend_test", "title": "Trend tests for raster time series", "concept": [ "Trend test functions" ], "topics": [ "trend_test" ] }, { "page": "workflow_rta", "title": "Robust Trend Analysis (RTA): the full pipeline in one call", "concept": [ "pipeline functions" ], "topics": [ "workflow_rta" ] }, { "page": "workflow_trends", "title": "Configure a monotonic or linear trend-analysis workflow", "concept": [ "pipeline functions" ], "topics": [ "workflow_trends" ] }, { "page": "workflow_tst", "title": "True Significant Trends (TST): the full pipeline in one call", "concept": [ "pipeline functions" ], "topics": [ "workflow_tst" ] } ], "_pkglogo": "https://github.com/olive-r/sptrends/raw/HEAD/man/figures/logo.png", "_readme": "https://github.com/olive-r/sptrends/raw/HEAD/README.md", "_rundeps": [ "lattice", "Matrix", "Rcpp", "terra", "withr" ], "_vignettes": [ { "source": "a-getting-started.Rmd", "filename": "a-getting-started.html", "title": "Loading and exploring spatiotemporal data ", "engine": "knitr::rmarkdown", "headings": [ "Why this matters", "What read_ordered_stack() and read_netcdf_stack() do", "Basic workflow", "Understanding the results", "Declaring seasonal input", "Analytical outputs", "Choosing the main options", "Common mistakes", "Next steps", "Further details", "References" ], "created": "2026-09-23 00:13:25", "modified": "2026-09-23 00:13:25", "commits": 1 }, { "source": "b-prewhitening.Rmd", "filename": "b-prewhitening.html", "title": "Dealing with serial correlation ", "engine": "knitr::rmarkdown", "headings": [ "Why this matters", "What prewhiten() does", "Basic workflow", "Understanding the results", "Choosing the main options", "Common mistakes", "Next steps", "Further details", "References" ], "created": "2026-09-23 00:13:25", "modified": "2026-09-23 00:13:25", "commits": 1 }, { "source": "c-trend-test.Rmd", "filename": "c-trend-test.html", "title": "Trend inference and statistical significance ", "engine": "knitr::rmarkdown", "headings": [ "Why this matters", "What trend_test() does", "Basic workflow", "Understanding the results", "Choosing the main options", "Common mistakes", "Next steps", "Further details", "References" ], "created": "2026-09-23 00:13:25", "modified": "2026-09-23 00:13:25", "commits": 1 }, { "source": "d-slope-estimation.Rmd", "filename": "d-slope-estimation.html", "title": "Trend magnitude estimation ", "engine": "knitr::rmarkdown", "headings": [ "Why this matters", "What slope_estimator() does", "Basic workflow", "Understanding the results", "Choosing the main options", "Common mistakes", "Next steps", "Further details", "References" ], "created": "2026-09-23 00:13:25", "modified": "2026-09-23 00:13:25", "commits": 1 }, { "source": "e-fdr-correction.Rmd", "filename": "e-fdr-correction.html", "title": "Correcting for multiple testing ", "engine": "knitr::rmarkdown", "headings": [ "Why this matters", "What fdr_correction() does", "Basic workflow", "Understanding the results", "Choosing the main options", "Common mistakes", "Next steps", "Further details", "References" ], "created": "2026-09-23 00:13:25", "modified": "2026-09-23 00:13:25", "commits": 1 }, { "source": "g-workflow-trends.Rmd", "filename": "g-workflow-trends.html", "title": "Trend workflows and published methods ", "engine": "knitr::rmarkdown", "headings": [ "Why this matters", "What workflow_trends() does", "Basic workflow", "Understanding the results", "Choosing the main options", "Published workflows", "Common mistakes", "Next steps", "Further details", "References" ], "created": "2026-09-23 00:13:25", "modified": "2026-09-23 00:13:25", "commits": 1 } ], "_score": 4.778151250383644, "_indexed": true, "_nocasepkg": "sptrends", "_universes": [ "olive-r" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.6.3", "date": "2026-10-10T14:02:25.000Z", "distro": "resolute", "commit": "4c3413de8dd2efe86edeb7ffad4c9aa655313436", "fileid": "https://r2.ropensci.org/b345af4b39d74ba81c28fc87bd17beda955e479c78103fe44b1636eb1acd926e", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/olive-r/actions/runs/38057576921" }, { "r": "4.6.1", "os": "linux", "version": "1.6.3", "date": "2026-10-10T14:02:27.000Z", "distro": "resolute", "commit": "4c3413de8dd2efe86edeb7ffad4c9aa655313436", "fileid": "https://r2.ropensci.org/81b058c86da7bcbf6ffcde1b5596046621f2627102093b289cd7488e3ab5a2a1", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/olive-r/actions/runs/38057576921" }, { "r": "4.5.3", "os": "mac", "version": "1.6.3", "date": "2026-10-10T14:05:40.000Z", "commit": "4c3413de8dd2efe86edeb7ffad4c9aa655313436", "fileid": "https://r2.ropensci.org/98c6fba768eece965cf1132b61dce14f47a746972611fde68284a83a9b9a7ef2", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/olive-r/actions/runs/38057576921" }, { "r": "4.6.1", "os": "mac", "version": "1.6.3", "date": "2026-10-10T14:05:12.000Z", "commit": "4c3413de8dd2efe86edeb7ffad4c9aa655313436", "fileid": "https://r2.ropensci.org/021ae104c3d103fb6b303a08c70c8f6a5e2d7804673e2304d9626d66eec85ce3", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/olive-r/actions/runs/38057576921" }, { "r": "4.6.0", "os": "wasm", "version": "1.6.3", "date": "2026-10-10T14:02:13.000Z", "commit": "4c3413de8dd2efe86edeb7ffad4c9aa655313436", "fileid": "https://r2.ropensci.org/521efac9c0d2a91f05cfab937bed5dde2776e24156ebcffbf1c2494688d34e10", "status": "success", "buildurl": "https://github.com/r-universe/olive-r/actions/runs/38057576921" }, { "r": "4.7.0", "os": "win", "version": "1.6.3", "date": "2026-10-10T14:00:33.000Z", "commit": "4c3413de8dd2efe86edeb7ffad4c9aa655313436", "fileid": "https://r2.ropensci.org/92ef6e1d5d616bd87adf17145c72e511846b3a1e5c1b901248e29024eaa8b551", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/olive-r/actions/runs/38057576921" }, { "r": "4.5.3", "os": "win", "version": "1.6.3", "date": "2026-10-10T14:00:21.000Z", "commit": "4c3413de8dd2efe86edeb7ffad4c9aa655313436", "fileid": "https://r2.ropensci.org/d1bbe22909b9fc3604838a655618c1452747a68a3e9b6ec2522a43bb6802d71c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/olive-r/actions/runs/38057576921" }, { "r": "4.6.1", "os": "win", "version": "1.6.3", "date": "2026-10-10T14:00:33.000Z", "commit": "4c3413de8dd2efe86edeb7ffad4c9aa655313436", "fileid": "https://r2.ropensci.org/a1fd4c6316c32c7fc135ad5f883e182c4841b424080f10d7cee36e4fcf0233e6", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/olive-r/actions/runs/38057576921" } ] }