R: Spatial Parallel Computing by Hierarchical Data Partitioning

# Spatial Parallel Computing by Hierarchical Data Partitioning

## Documentation for package ‘chopin’ version 0.9.9-5

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

extract_at | Extract raster values with point buffers or polygons
extract_at-method | Extract raster values with point buffers or polygons
ncpoints | Mildly clustered points in North Carolina, United States
par_convert_f | Map specified arguments to others in literals
par_grid | Parallelize spatial computation over the computational grids
par_grid_mirai | Parallelize spatial computation over the computational grids
par_hierarchy | Parallelize spatial computation by hierarchy in input data
par_hierarchy_mirai | Parallelize spatial computation by hierarchy in input data
par_make_dggrid | Convert DGGRID indices to sf object
par_make_h3 | Convert a input sf object to H3 hexagons
par_merge_grid | Merge adjacent grid polygons with given rules
par_multirasters | Parallelize spatial computation over multiple raster files
par_multirasters_mirai | Parallelize spatial computation over multiple raster files
par_pad_balanced | Extension of par_make_balanced for padded grids
par_pad_grid | Get a set of computational grids
par_split_list | Split grid list to a nested list of row-wise data frames
prediction_grid | Regular grid points in the mainland United States at 1km spatial resolution
summarize_aw | Area weighted summary using two polygon objects
summarize_aw-method | Area weighted summary using two polygon objects
summarize_pp | Point to polygon summary using target polygons and source points
summarize_pp-method | Point to polygon summary using target polygons and source points
summarize_sedc | Calculate Sum of Exponentially Decaying Contributions (SEDC) covariates
summarize_st | Summarize Data by Time or Space