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
sim_trend_stack         Generate a synthetic gridded time series with
                        known true trends
simulation_design       Build a factorial design of simulation
                        scenarios
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
