The package provides global- and smallarea estimators for
twophase and threephase forest inventories under simple and
cluster sampling, which have been developed by Daniel Mandallaz at
ETH Zurich. The implemented methods have been published and applied in various studies
References) and can be used for double- and triple sampling for stratification,
double- and triple sampling for regression and double- and triple sampling for regression within strata.
The package provides three main functions to apply the various estimators for twophase and threephase forest inventories:
twophase Function to apply global- and various smallarea estimation techniques for twophase inventories
threephase Function to apply global- and various smallarea estimation techniques for threephase inventories
onephase Function to apply estimations for onephase inventories, mainly for comparison
The Motivation of writing this package was to provide an extensive and consistent collection of state-of-the-art design-based estimation techniques for forest inventories. It was especially designed to facilitate the application of the available estimators in forest practice as well as in scientifically related studies. The work on this package was also the trigger to complete the range of the allready published estimators, especially in the framework of three-phase smallarea estimators.
Massey, A. F. (2015). Multiphase estimation procedures for forest inventories under the design-based Monte Carlo approach (Doctoral dissertation, Diss., ETH Zurich, Nr. 23025).
Mandallaz, D. (2013). Design-based properties of some small-area estimators in forest inventory with two-phase sampling. Canadian Journal of Forest Research, 43(5), 441-449.
Mandallaz, D., Breschan, J., & Hill, A. (2013). New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based monte carlo approach with applications to small-area estimation. Canadian Journal of Forest Research, 43(11), 1023-1031.
Mandallaz, D. (2013). A three-phase sampling extension of the generalized regression estimator with partially exhaustive information. Canadian Journal of Forest Research, 44(4), 383-388.
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