dimstosplit

data in element given as matrix with <code>d</code> rows or components and <code>n</code> columns or samples.

vector representing the element size.

element order and split mode as detailed in <code><a rd-options="" href="/link/det.construct?package=detpack&version=1.1.3" data-mini-rdoc="detpack::det.construct">det.construct</a></code>.

mode

significance levels for goodness-of-fit and independence tests, respectively, in element refinement or splitting process. <code>alphad</code> is irrelevant for univariate data <code>x</code> with <code>d = 1</code>.

alphag, alphad

Determine the split dimensions of an existing distribution element in the DET refinement-process based on statistical tests.

Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function 'det.construct' translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function 'det.query' is available. The functions 'det1' and 'det2' provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by 'det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632> or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) <doi:10.1080/10618600.2018.1482768>.

Daniel Meyer

detpack

Density Estimation and Random Number Generation with
Distribution Element Trees

dimstosplit function

element order and split mode as detailed in <code><a rd-options='' href='det.construct'>det.construct</a></code>.

dimstosplit: Determine Split Dimension(s)

Description

Usage

Arguments

Value