https://sowsalim01.r-universe.devRecent package updates in sowsalim01R-universehttps://github.com/sowsalim01.pnghttps://sowsalim01.r-universe.devSun, 11 Oct 2026 13:58:59 GMTsowsalim01@gmail.com (Mamadou Sow)A unified, fast, and extensible framework for calculating spectral and geospatial indices from multispectral raster datasets (e.g., NDVI - Normalized Difference Vegetation Index, NDWI - Normalized Difference Water Index, MNDWI - Modified Normalized Difference Water Index, NDBI - Normalized Difference Built-up Index, SAVI - Soil Adjusted Vegetation Index, EVI - Enhanced Vegetation Index, GNDVI - Green Normalized Difference Vegetation Index, NDMI - Normalized Difference Moisture Index, BSI - Bare Soil Index). Designed around 'terra' 'SpatRaster' objects, it supports multi-band rasters, automatic band resolution, sensor presets (Sentinel-2, Landsat-8/9), customizable index parameters, vectorized computations, and comprehensive validation. Methods based on Rouse et al. (1973) , McFeeters (1996) <doi:10.1080/01431169608948714>, Xu (2006) <doi:10.1080/01431160600589179>, Zha et al. (2003) <doi:10.1016/S0034-4257(03)00087-7>, Huete (1988) <doi:10.1016/0034-4257(88)90106-X>, Gitelson et al. (1996) <doi:10.1016/S0034-4257(96)00072-7>, Wilson and Sader (2002) <doi:10.1016/S0034-4257(01)00318-2>, and Rikimaru et al. (2002).https://github.com/r-universe/sowsalim01/actions/runs/38153763279Sun, 11 Oct 2026 13:58:59 GMTGeoIndexR0.1.0successhttps://sowsalim01.r-universe.devhttps://github.com/sowsalim01/geoindexrmultispectral-workflow.Rmdmultispectral-workflow.htmlEnd-to-End Multispectral Image Workflow2026-09-17 14:00:482026-09-17 14:00:48introduction.Rmdintroduction.htmlIntroduction to GeoIndexR2026-09-17 14:00:482026-09-17 17:49:24vegetation-indices.Rmdvegetation-indices.htmlVegetation Indices in GeoIndexR2026-09-17 14:00:482026-09-17 14:00:48water-indices.Rmdwater-indices.htmlWater and Moisture Indices in GeoIndexR2026-09-17 14:00:482026-09-17 14:00:48sowsalim01@gmail.com (Mamadou SOW)Spatial cross-validation and model evaluation for geospatial machine learning applications. Addresses spatial dependence in observations by implementing spatial block, buffered, and clustering cross-validation methods. Includes spatial leakage detection, model performance metrics, and spatial residual diagnostics for assessing model generalization across geographic space. Methods based on Brenning (2012) <doi:10.1016/j.cageo.2012.02.001>, Pohjankukka et al. (2017) <doi:10.1016/j.isprsjprs.2017.07.001>, and Roberts et al. (2017) <doi:10.1111/ecog.02881>.https://github.com/r-universe/sowsalim01/actions/runs/38139369470Sat, 26 Sep 2026 16:48:50 GMTspatialcvR0.1.0successhttps://sowsalim01.r-universe.devhttps://github.com/sowsalim01/spatialcvrintroduction.Rmdintroduction.htmlIntroduction to Spatial Cross-Validation2026-09-13 21:50:272026-09-13 21:56:14model-evaluation.Rmdmodel-evaluation.htmlModel Evaluation and Comparison2026-09-13 21:50:272026-09-26 16:48:50spatial-cross-validation.Rmdspatial-cross-validation.htmlSpatial Cross-Validation Methods2026-09-13 21:50:272026-09-13 21:50:27spatial-leakage.Rmdspatial-leakage.htmlSpatial Leakage Detection2026-09-13 21:50:272026-09-13 21:50:27