Package index • tjdc

All functions

case_medium_zoom1: Medium Challenge Changepoint Timeseries DataCube

case_medium_zoom1_details: Medium Challenge Changepoint Timeseries DataCube

check_x(): Check 1D timeseries

tj_cell_neighbours(): Raster Cell Neighbourhoods

tj_cell_neighbours_old(): Raster Cell Neighbourhoods

tj_cfg_m0.3(): Compile paramaters for m0.3

tj_cfg_m0.6(): Compile paramaters for m0.6

tj_contextual_mann_kendall(): Contextual Mann-Kendall Trend Test

tj_divide_bbox(): Divide bounding box into subsets, return polygons

tj_divide_raster(): Tile stars raster

tj_fast_sample(): Sample Element Fast

tj_fit_m0.3(): Simultaneous linear regression with correlated changepoint estimation

tj_fit_m0.3_dac(): Wrapper to fit model 3 to a mosaic, old version

tj_fit_m0.3s(): Fit m0.3 alternative parametrisation

tj_fit_m0.5(): Wrapper to fit model 3 to a mosaic

tj_fit_m0.6(): Simultaneous linear regression with correlated changepoint estimation

tj_fit_m0.6_v1(): Simultaneous linear regression with correlated changepoint estimation

tj_fit_m0.6s(): Fit m0.6 alternative parametrisation

tj_fit_m0.x(): Wrapper to fit models of the 0.x-family to a mosaic

tj_i2rc(): Cell to Row-Column

tj_mann_kendall(): Mann-Kendall Trend Test

tj_predict_m0.3(): Prediction the series per cell

tj_rc2i(): Row-Col to Cell

tj_stars_to_data(): Process input stars to legacy data format

tj_stars_to_polygon_union(): Raster to Polygons Take stars-raster and create a polygons of those pixels with >0 values using st_union

tj_stitcher_m0.5_v1.1(): Stitch a mosaic model fit

tj_stitcher_m0.x(): Stitch a mosaic model fit

tj_summarise_m0.3(): Summarise fit of M0.3 as a tibble

tj_summarise_m0.5(): Summarise fit of M0.5 stiched results

tj_trace_m0.3(): Get the history

tj_trace_m0.3_dac(): Retrieve trace for particular cell from list of mosaic fits

tj_trace_m0.5(): Retrieve trace for particular cell from list of mosaic fits

tjdc tjdc-package: Trends and Jumps in Thin DataCubes