{ "_id": "6a590f9009eb0996dd68bcda", "Package": "Saylac", "Type": "Package", "Title": "Spatial Analysis of Yearly, Longitudinal, and Areal Change", "Version": "0.1.3", "Author": "Abdisalam Hassan Muse [aut, cre] (ORCID:\n)", "Maintainer": "Abdisalam Hassan Muse ", "Authors@R": "person(given = \"Abdisalam Hassan\", family = \"Muse\", role = c(\"aut\", \"cre\"), email = \"abdisalam.hassan@amoud.edu.so\", comment = c(ORCID = \"0000-0003-4905-0044\"))", "Description": "Provides the Saylac Shiny application for\nmultidisciplinary analysis of global, national, and regional\nindicators observed across places and years. SAYLAC abbreviates\nSpatial Analysis of Yearly, Longitudinal, and Areal Change. The\nplatform supports spatial diagnostics, longitudinal data\nexploration, time-series diagnostics, comparative forecasting,\nand automated reporting for development, health, education,\neconomic, environmental, and social indicators. The application\nimplements three connected modules: Spatial Analysis Workflow\nfor exploratory spatial data analysis, Single Model Diagnostics\nfor stationarity testing, trend diagnostics, and comparative\nforecasting, and Spatial Uncertainty and Reporting Analysis\nDashboard for forecast mapping, spatial clustering, and\nreporting. The application supports choropleth mapping, Moran's\nI, Geary's C, Local Indicators of Spatial Association,\nGetis-Ord Gi star statistics, spatial correlograms, Theil-Sen\ntrend estimation, Mann-Kendall testing, Autoregressive\nIntegrated Moving Average models, Exponential Smoothing State\nSpace models, neural network autoregression, BATS, TBATS, theta\nforecasting, symmetric mean absolute percentage error model\ncomparison, and report generation. The platform was first\napplied in Touryare and Mohamud (2026)\n for integrated\nspatial-temporal forecasting of educational attainment in\nEastern Africa toward Sustainable Development Goal 4.", "License": "GPL (>= 3)", "Encoding": "UTF-8", "URL": "https://muse252.shinyapps.io/Saylac_Shiny_App_Ready/", "NeedsCompilation": "no", "Packaged": { "Date": "2026-07-16 17:02:12 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-07-16 13:30:08 UTC", "RemoteUrl": "https://github.com/cran/Saylac", "RemoteRef": "HEAD", "RemoteSha": "23a5edde3209a6d1dfc94ea73a28a8f6f48cd36f", "_user": "cran", "_type": "src", "_file": "Saylac_0.1.3.tar.gz", "_fileid": "https://r2.ropensci.org/52c72d4c76b62720719e468a1a8f2256002b5b3bd5f95c60e3e59fe9b679347f", "_filesize": 122106, "_sha256": "52c72d4c76b62720719e468a1a8f2256002b5b3bd5f95c60e3e59fe9b679347f", "_expires": "2026-10-24T17:06:22.000Z", "_created": "2026-07-16T17:02:12.000Z", "_published": 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