Get pictures of cats and their meows from your R
console.
beepr, dplyr, graphics, imager, magrittr, purrr, rvest,
stringr, tibble

beepr, dplyr, graphics, imager, magrittr, purrr, rvest (>=
1.0.0), stringr, tibble

A Wrapper for Querying KISTERS 'WISKI' Databases via the 'KiWIS'
API
c(person("Ryan", "Whaley", role = c("aut", "cre"), email = "rdgwhaley@gmail.com"),
person("Sam", "Albers", role = "ctb", email = "sam.albers@gov.bc.ca")
)
A wrapper for querying 'WISKI' databases via the 'KiWIS' 'REST' API. 'WISKI' is an 'SQL' relational database
used for the collection and storage of water data developed by KISTERS and 'KiWIS' is a 'REST' service that provides
access to 'WISKI' databases via HTTP requests (<https://water.kisters.de/en/technology-trends/kisters-and-open-data/>).
Contains a list of default databases (called 'hubs') and also allows users to provide their own 'KiWIS' URL.
Supports the entire query process- from metadata to specific time series values. All data is returned as tidy tibbles.
Ryan Whaley [aut, cre],
Sam Albers [ctb]

tibble, jsonlite, lubridate, httr, dplyr

tibble, jsonlite, lubridate, httr, dplyr, purrr

Weighted k-Nearest Neighbors Classification and Regression

Weighted k-Nearest Neighbors Classification, Regression
and Clustering

Weighted k-Nearest Neighbors Classification, Regression and Clustering

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

c(
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"))
igraph (>= 1.0), Matrix, stats, graphics
Klaus Schliep [aut, cre],
Klaus Hechenbichler [aut],
Antoine Lizee [ctb]

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"),
person("Lexin", "Li", role = c("aut")))
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>.
grpreg, knockoff, doParallel, parallel, foreach, ExtDist
Xiaowu Dai [aut],
Xiang Lyu [aut, cre],
Lexin Li [aut]