detpack: Distribution Element Trees for Density Estimation and...

In detpack: Density Estimation and Random Number Generation with Distribution Element Trees

Description

Distribution element trees (DETs) enable the estimation of probability densities based on (possibly large) datasets. Moreover, DETs can be used for random number generation or smooth bootstrapping both in unconditional and conditional modes. In the latter mode, information about certain probability-space components is taken into account when sampling the remaining probability-space components.

Details

The function det.construct translates a dataset into a DET. To evaluate the probability density based on a precomputed DET at arbitrary query points, det.query is used. The functions det1 and det2 provide density estimation and plotting for one- and two-dimensional datasets. (Un)conditional smooth bootstrapping from an available DET, can be performed by det.rnd. To inspect the structure of a DET, the functions det.de and det.leafs are useful. While det.de enables the extraction of an individual distribution element from the tree, det.leafs extracts all leaf elements at branch ends.

Author(s)

Daniel Meyer, [e-mail]

References

Distribution element tree basics and density estimation, see Meyer, D.W. (2016) http://arxiv.org/abs/1610.00345 or Meyer, D.W., Statistics and Computing (2017) https://doi.org/10.1007/s11222-017-9751-9.

DETs for smooth bootstrapping, see Meyer, D.W. (2017) http://arxiv.org/abs/1711.04632 or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) https://doi.org/10.1080/10618600.2018.1482768.

detpack documentation built on July 24, 2019, 5:03 p.m.