Statistical Inference for Spatiotemporal Trends in Gridded Data
sptrends provides a reproducible framework for statistical inference of spatiotemporal trends in gridded environmental data. It offers modular methods for serial-correlation treatment, trend testing, spatially explicit inference, global and local spatial-autocorrelation diagnostics, robust slope estimation, multiple-testing correction, simulation, benchmarking, visualisation, mapping, and reporting.
Install the released version from CRAN:
install.packages("sptrends")
Or install the development version from GitHub:
# remotes::install_github("Olive-r/sptrends")
install.packages("sptrends", repos = c('https://olive-r.r-universe.dev', 'https://cloud.r-project.org'))
A bundled annual NDVI dataset is available for reproducible examples:
library(sptrends)
r <- read_ordered_stack(example_data("vhp_ndvi"))
result <- workflow_tst(r, report = FALSE, verbose = FALSE)
plot(result)
With your own data, replace the file path:
# One raster file per time step, ordered from numbers in the file names.
r <- read_ordered_stack("path/to/rasters")
# Published True Significant Trends workflow
# (Gutierrez-Hernandez and Garcia, 2025, https://doi.org/10.1016/j.rsase.2024.101377).
result <- workflow_tst(r)
print(result)
summary(result)
plot(result)
For seasonal data, declare the temporal structure and remove the cycle before running a trend workflow. For example, for consecutive monthly layers beginning in August:
# Supply your files in chronological order; files may contain multiple layers.
monthly <- read_ordered_stack(
files = ordered_files, cycle_type = "monthly",
start = as.Date("2001-08-01"), report = FALSE
)
anomalies <- compute_anomalies(monthly, cycle_type = "monthly",
start_position = 8)
result <- workflow_tst(anomalies$anomalies, report = FALSE)
start is the first period's beginning; the default assigned date for a
monthly layer is its centre. Annual layers use 1 January. Anomalies group
layers by position, not by their date metadata; the user supplies the
correct cycle and starting position. files plus time also supports an
explicit date for every layer. See the
loading guide.
workflow_tst() implements True Significant Trends: selective
prewhitening, Contextual Mann-Kendall, Theil-Sen and adaptive FDR
(Gutiérrez-Hernández and García, 2025).workflow_rta() implements Robust Trend Analysis: Contextual
Mann-Kendall, Theil-Sen and FDR-BH without prewhitening
(Gutiérrez-Hernández and García, 2024).workflow_trends() combines user-selected preprocessing, trend, slope and
FDR modules through the same validated interfaces.Each analytical stage is also available independently:
pw <- prewhiten(r, method = "TFPW_WS")
trend <- trend_test(pw$series, method = "CMK")
slope <- slope_estimator(pw$series, method = "TS")
fdr <- fdr_correction(trend$stats$p, method = "BKY")
spatial_autocorrelation() provides independent global and local permutation
inference for environmental variables, residuals, coefficients or inferential
fields. Local p-value rasters can be passed to fdr_correction() for BH, BKY
or BY control.
read_ordered_stack() and read_netcdf_stack() — raster import with
temporal-order checks.compute_anomalies() — deseasonalising, when the series has a
known cycle.prewhiten() — serial-correlation treatment, trend-preserving.trend_test() — CMK with configurable odd neighbourhoods (3 by 3 by
default), MK, OLS and modified MK.slope_estimator() — Theil-Sen, OLS and Siegel repeated median.fdr_correction() — BH, adaptive BKY and BY.spatial_autocorrelation() — permutation-based spatial diagnostics.sim_trend_stack(), simulation_design(), compare_detections(),
benchmark_methods() and benchmark_summary() — known-truth
simulation and reproducible method benchmarking, including S3
performance plots and Monte Carlo summaries (empirical FDR and FWER
where applicable) across scenario factors.The current calculations are vectorised and selected stages can use parallel processing, but analytical rasters are materialised in memory. Test representative dimensions before processing very large datasets.
The function help pages describe assumptions, alternatives, computational trade-offs, references and method-specific quality assurance. The vignettes provide concise guides to preprocessing, trend tests, slope estimation, FDR correction and complete workflows:
browseVignettes("sptrends")
The 1.6.3 local check completed in under 10 minutes with 0 errors,
0 warnings and 0 notes. Coverage is 100% across every R source file with
SPTRENDS_TEST_PARALLEL=true. win-builder checks (R-devel, R-release,
R-oldrelease) had 0 errors and 0 warnings, with only the routine
submission/terminology note. See cran-comments.md for the current
validation status. The 19 parallel-execution tests remain optional to
keep routine checks within CRAN's time limit. Website building is a
separate step. Style, spelling and link integrity are checked
periodically with lintr, goodpractice, spelling and urlchecker.
Beyond the automated tests/testthat/ suite, the package's Mann-Kendall
and Theil-Sen implementations have been cross-checked against five
independent CRAN packages (Kendall, trend, modifiedmk,
robslopes, zyp) across five test series (clear trend, no trend,
tied values, short, long), with no unexplained discrepancies. The
package has also undergone a comprehensive integral audit of code,
citations and documentation.
trend_test(method = "CMK") has additionally been cross-checked against an
installed copy of ConMK (Antiphon,
GitHub, not on CRAN -- also available as a fork at
geoporttishare/ConMK), the
closest available external reference implementation of the contextual
Mann-Kendall test. The base statistic matched to floating-point precision,
and the optional continuity = TRUE argument reproduces ConMK's own
p-values exactly at the specific edge case where the two implementations
would otherwise be expected to diverge. Full details in ?trend_test's
"External validation" section.
Every change is documented transparently in NEWS.md.
To cite the package itself:
Gutiérrez-Hernández, O., & García, L. V. (2026). sptrends: Statistical Inference for Spatiotemporal Trends in Gridded Data. R package version 1.6.3, CRAN. https://doi.org/10.32614/CRAN.package.sptrends
Archived source code snapshots for each release are also available via Zenodo: https://doi.org/10.5281/zenodo.21822842
Or, from R:
citation("sptrends")
Published workflows:
GPL (>= 3)