person("Carson", "Sievert", role = c("aut", "cre"), email = "carson@rstudio.com", comment = c(ORCID = "0000-0002-4958-2844")),
Imports: grDevices, htmltools (>= 0.5.1), jsonlite, sass (>= 0.4.0),
Suggests: shiny (>= 1.6.0), rmarkdown (>= 2.7), thematic, knitr,
'deprecated.R' 'files.R' 'imports.R' 'onLoad.R' 'precompiled.R'
Packaged: 2021-05-18 14:17:35 UTC; cpsievert
Author: Carson Sievert [aut, cre] (<https://orcid.org/0000-0002-4958-2844>),

Packaged: 2021-05-12 18:25:18 UTC; cpsievert

Imports: grDevices, htmltools (>= 0.5.2), jsonlite, sass (>= 0.4.0),
testthat, withr, rappdirs, curl, magrittr
'nav-update.R' 'navs-legacy.R' 'navs.R' 'onLoad.R' 'page.R'
Packaged: 2021-09-01 21:39:14 UTC; cpsievert

Config/Needs/website: rsconnect, renv, r-lib/pkgdown, apreshill/quillt
Packaged: 2021-10-06 14:12:58 UTC; cpsievert

jquerylib (>= 0.1.3), rlang, cachem, memoise
Config/Needs/website: brio, dplyr, DT, glue, purrr, rprojroot,
Config/Needs/deploy: BH, DT, ggplot2, hexbin, lattice, reactable,
Packaged: 2022-07-16 14:17:56 UTC; cpsievert

jquerylib (>= 0.1.3), rlang, cachem, memoise (>= 2.0.1)
Packaged: 2022-11-02 20:21:31 UTC; cpsievert

Title: Basis Selection from Multiple Libraries
Author: Junqing Wu <wjqpu@yahoo.com>, Jeff Sklar <jsklar@calpoly.edu>,
Yuedong Wang <yuedong@pstat.ucsb.edu>, Wendy Meiring
Description: BSML stands for Basis Selection from Multiple Libraries
and it is a methodology for spatially-adaptive multivariate
regression. To fit a nonparametric or semiparametric regression
model, multiple libraries may be used to approximate the mean
function. The program will adaptively select basis functions
from each library and find the optimal model complexity by
using the Generalized Degrees of Freedom or Covariance Penalty.

Title: Build Decision Trees with Optimal Multivariate Splits
Description: Functions for training an optimal decision tree classifier, making predictions and generating latex code for plotting. Works for two-class and multi-class classification problems. The algorithm seeks the optimal Boolean rule consisting of multiple variables to split a node, resulting in shorter trees. Use bsnsing() to build a tree, predict() to make predictions and plot() to plot the tree into latex and PDF. See Yanchao Liu (2022) <arXiv:2205.15263> for technical details. Source code and more data sets are at <https://github.com/profyliu/bsnsing/>.