Statistical Inference for Spatiotemporal Trends in Gridded Data R package sptrends version 1.6.3 Provides a unified and reproducible framework for statistical inference of spatiotemporal trends in gridded environmental data. The framework addresses the interconnected challenges of serial correlation, spatial dependence and multiple testing that commonly arise when analysing gridded environmental time series. Its core methods support serial-correlation treatment through trend-preserving prewhitening, pixel-wise and spatially explicit trend inference, slope estimation and multiple-testing correction. These methods may be applied independently or integrated within configurable analytical workflows. Dedicated workflows are also provided to reproduce methodologies published in the scientific literature: Gutiérrez-Hernández and García (2025) <doi:10.1016/j.rsase.2024.101377> for the True Significant Trends workflow, Gutiérrez-Hernández and García (2024) <doi:10.3390/rs16203886> for the Robust Trend Analysis workflow, and Gutiérrez-Hernández and García (2025) <doi:10.3390/math13223630> for the adaptive false discovery rate procedure. Supporting utilities facilitate raster data import and inspection, anomaly calculation, spatial autocorrelation diagnostics, simulation studies, benchmarking, visualisation, mapping, and reporting. Software Depends: R (>= 4.1) Imports: terra (>= 1.7-0), Matrix, parallel, stats, utils, graphics, grDevices, withr (>= 2.2.0) Suggests: fields, testthat (>= 3.2.0), knitr, rmarkdown, ncdf4, Kendall, modifiedmk, rkt, robslopes, trend, zyp Oliver Gutiérrez-Hernández <olivergh@uma.es> Comprehensive R Archive Network (CRAN) Oliver Gutiérrez-Hernández [aut, cre] (ORCID: <https://orcid.org/0000-0003-2580-5465>, affiliation: Department of Geography, University of Málaga, Málaga, Spain), Luis V. García [aut] (ORCID: <https://orcid.org/0000-0002-5514-2941>, affiliation: Institute of Natural Resources and Agrobiology of Seville (IRNAS), Spanish National Research Council (CSIC), Seville, Spain) GPL (>= 3) 2026-09-22 application/tgz https://CRAN.R-project.org/package=sptrends doi:10.32614/CRAN.package.sptrends en-GB