hipPartition

Partition the resource into boxes with the same nested structure as Halton boxes.
The spbal parameter iterations defines the number of boxes used in the HIP partition and
should be larger than the sample size but less than the population size.

internal

Encapsulates a number of spatially balanced sampling algorithms,
namely, Balanced Acceptance Sampling (equal, unequal, seed point, panels), Halton frames
(for discretizing a continuous resource), Halton Iterative Partitioning (equal probability)
and Simple Random Sampling.
Robertson, B. L., Brown, J. A., McDonald, T. and Jaksons, P. (2013) <doi:10.1111/biom.12059>.
Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2017) <doi:10.1016/j.spl.2017.05.004>.
Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2018) <doi:10.1007/s10651-018-0406-6>.
Robertson, B. L., van Dam-Bates, P. and Gansell, O. (2021a) <doi:10.1007/s10651-020-00481-1>.
Robertson, B. L., Davies, P., Gansell, O., van Dam-Bates, P., McDonald, T. (2025) <doi:10.1111/anzs.12435>.

Phil Davies

spbal

Spatially Balanced Sampling Algorithms

Blair Robertson

Paul van Dam-Bates

Oliver Gansell

hipPartition function

<dl><dt>pts</dt>
<dd>The population of points.</dd>
<dt>its</dt>
<dd>The number of partitioning iterations.</dd></dl>

Arguments

Author

Partition the population. — hipPartition

<dl>

<dt>pts</dt>
<dd>The population of points.</dd>


<dt>its</dt>
<dd>The number of partitioning iterations.</dd>

</dl>

hipPartition: Partition the population.

Description

Usage

Value

Arguments

Author