Title: Portable and Performant Tour Animations
person("Casper", "Hart", email = "casperhart93@gmail.com", role = c("aut", "cre")),
person("Earo", "Wang", email = "earo.wang@gmail.com", role = c("aut", "ths"), comment = c(ORCID = "0000-0001-6448-5260"))
Description: Provides 2D and 3D tour animations as HTML widgets. The user can interact with the widgets using orbit controls, tooltips, brushing, and timeline controls. Linked brushing is supported using 'crosstalk', and widgets can be embedded in Shiny apps or HTML documents.
Imports: tourr, tibble, htmlwidgets, tidyselect, rlang, purrr,
Suggests: testthat (>= 3.0.0), crosstalk, shiny, htmltools, knitr,

Title: Density Estimation and Random Number Generation with
Description: Density estimation for possibly large data sets and conditional/unconditional random number generation with distribution element trees. 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>.
Imports: parallel, graphics, grDevices, stats

Description: Density estimation for possibly large data sets and conditional/unconditional random number generation 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>.

Description: 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>.