Statistical Inference for Spatiotemporal Trends in Gridded Data

Documentation for package ‘sptrends’ version 1.6.3

benchmark_methods	Benchmark statistical methods across known-truth simulation scenarios
benchmark_summary	Summarise a method benchmark across Monte Carlo replicates
compare_detections	Compare detection methods against a known ground truth
compute_anomalies	Remove the seasonal cycle from raster time series
example_data	Path to sptrends' bundled example dataset
fdr_correction	Apply false discovery rate (FDR) correction to multiple p-values
inspect_ts_cell	Inspect a single cell's (or area's) raw time series interactively
plot.sptrends	Plot a sptrends result
prepare_cmk_neighbourhood	Precompute a CMK spatial neighbourhood
prewhiten	AR(1) prewhitening of raster time series
print.sptrends	Print a sptrends result
read_netcdf_stack	Read and chronologically order a single multi-temporal NetCDF file
read_ordered_stack	Read and chronologically order a folder of raster files
simulation_design	Build a factorial design of simulation scenarios
sim_trend_stack	Generate a synthetic gridded time series with known true trends
slope_estimator	Slope estimators for raster time series
spatial_autocorrelation	Permutation-based spatial autocorrelation tests
summary.sptrends	Summarise a sptrends result
trend_test	Trend tests for raster time series
workflow_rta	Robust Trend Analysis (RTA): the full pipeline in one call
workflow_trends	Configure a monotonic or linear trend-analysis workflow
workflow_tst	True Significant Trends (TST): the full pipeline in one call