Imports: tibble, jsonlite, lubridate, httr, dplyr

Imports: tibble, jsonlite, lubridate, httr, dplyr, purrr

Author: Klaus Schliep & Klaus Hechenbichler
Description: Weighted k-Nearest Neighbors Classification and Regression
Packaged: Mon Nov 22 11:13:00 2004; kpschlie

Packaged: Thu Sep 13 16:38:43 2007; kpschlie

Packaged: Thu Oct 11 17:44:59 2007; kpschlie

Packaged: Mon Jul 21 02:37:13 2008; kpschlie

Packaged: Mon Apr 10 12:48:37 2006; kpschlie

Description: Weighted k-Nearest Neighbors Classification, Regression

Description: Weighted k-Nearest Neighbors Classification, Regression and Clustering

Description: Weighted k-Nearest Neighbors for Classification, Regression and Clustering.
Imports: igraph (>= 0.6), Matrix, stats, graphics

person("Klaus", "Schliep", email = "klaus.schliep@gmail.com", role = c("aut", "cre")),
person("Klaus", "Hechenbichler", role = "aut"),
person("Antoine", "Lizee", email = "antoine.lizee@gmail.com", role = "ctb"))
Imports: igraph (>= 1.0), Matrix, stats, graphics

Title: Kernel Knockoffs Selection for Nonparametric Additive Models
c(person("Xiaowu", "Dai", role = c("aut")),
person("Xiang", "Lyu", role = c("aut","cre"), email="xianglyu@berkeley.edu"),
Description: A variable selection procedure, dubbed KKO, for nonparametric additive model with finite-sample false discovery rate control guarantee. The method integrates three key components: knockoffs, subsampling for stability, and random feature mapping for nonparametric function approximation. For more information, see the accompanying paper: Dai, X., Lyu, X., & Li, L. (2021). “Kernel Knockoffs Selection for Nonparametric Additive Models”. arXiv preprint <arXiv:2105.11659>.
Imports: grpreg, knockoff, doParallel, parallel, foreach, ExtDist

Author: Christian Roever, Nils Raabe, Karsten Luebke, Uwe Ligges
Description: Miscellaneous functions for classification and visualization
developed at the Department of Statistics, University of Dortmund
Packaged: Fri May 14 16:43:47 2004; ligges

Packaged: Fri Jul  2 13:45:38 2004; ligges