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SamplingBigData

Sampling Methods for Big Data

Select sampling methods for probability samples using large data sets. This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer.

Versions across snapshots

VersionRepositoryFileSize
1.0.0 rolling linux/jammy R-4.5 SamplingBigData_1.0.0.tar.gz 29.5 KiB
1.0.0 rolling linux/noble R-4.5 SamplingBigData_1.0.0.tar.gz 29.3 KiB
1.0.0 rolling source/ R- SamplingBigData_1.0.0.tar.gz 13.0 KiB
1.0.0 latest linux/jammy R-4.5 SamplingBigData_1.0.0.tar.gz 29.5 KiB
1.0.0 latest linux/noble R-4.5 SamplingBigData_1.0.0.tar.gz 29.3 KiB
1.0.0 latest source/ R- SamplingBigData_1.0.0.tar.gz 13.0 KiB
1.0.0 2026-04-26 source/ R- SamplingBigData_1.0.0.tar.gz 13.0 KiB
1.0.0 2026-04-23 source/ R- SamplingBigData_1.0.0.tar.gz 13.0 KiB
1.0.0 2026-04-09 windows/windows R-4.5 SamplingBigData_1.0.0.zip 35.3 KiB
1.0.0 2025-04-20 source/ R- SamplingBigData_1.0.0.tar.gz 13.0 KiB