purrr, rlang, sjstats, skimr, stats, tibble, tidyr

Imports: broomExtra, dplyr, effectsize, glue, haven, ipmisc, lme4,
parameters, purrr, rlang, skimr, stats, tibble, tidyr

Imports: broomExtra, dplyr (>= 1.0.0), effectsize, glue, haven, ipmisc,
lme4, parameters, purrr, rlang, skimr, stats, tibble, tidyr
Suggests: gapminder, ggplot2, knitr, rmarkdown, spelling

Suggests: gapminder, ggplot2, knitr, rmarkdown

Imports: broomExtra, dplyr, effectsize, glue, lme4, magrittr,

Description: The 'groupr' package provides a more powerful version of grouped
tibbles from 'dplyr'. It allows groups to be marked inapplicable,
which is a simple but widely useful way to express structure in a dataset.
It also provides powerful pivoting and other group manipulation functions.
Imports: dplyr, rlang, vctrs, purrr, tidyr, tibble
Author: Nicholas Griffiths [aut, cre] (<https://orcid.org/0000-0001-8166-9045>)

Authors@R: c(person("Yi", "Guo", role = c("aut","cre"),
Description: Group subset selection for linear regression models is provided in this package. Given response variable, and explanatory variables, which are organised in groups, group subset selection selects a small number of groups to explain response variable linearly using least squares.

Title: Implement the Group Walk Algorithm
person("Jack", "Freestone", email = "jfre0619@uni.sydney.edu.au", role = c("aut", "cre", "cph")),
person("Uri", "Keich", email = "uri.keich@sydney.edu.au", role = c("aut", "cph")))
Description: A procedure that allows the user to reject multiple hypotheses in the presence of group structure, while controlling the false discovery rate (FDR).
Packaged: 2022-02-14 01:15:45 UTC; jackfreestone

Description: A procedure that uses target-decoy competition (or knockoffs) to reject multiple hypotheses in the presence of group structure. The procedure controls the false discovery rate (FDR) at a user-specified threshold.
Packaged: 2022-02-15 08:35:38 UTC; jackfreestone

Packaged: 2022-06-17 23:49:57 UTC; jackfreestone

Title: Wavelet Functional ANOVA Through Markov Groves
Description: Functional denoising and functional ANOVA through wavelet-domain
Markov groves. Fore more details see: Ma L. and Soriano J. (2016)
Efficient functional ANOVA through wavelet-domain Markov groves.
Imports: methods, Rcpp (>= 0.11.2), wavethresh