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.
- Version1.0.0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- Last release09/03/2018
Team
Jonathan Lisic
Anton Grafström
Insights
Last 30 days
This package has been downloaded 342 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 13 times.
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Last 365 days
This package has been downloaded 3,480 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 12, 2024 with 61 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Reverse Depends1 package
- Reverse Imports1 package