stats, utils, optimr, VGAM, numDeriv, ggplot2, trust, dplyr,
magrittr, detectseparation, scales, phyloseq
knitr, rmarkdown, testthat, covr, limma, slam, R.rsp

Implements lasso and ridge regression for dichotomised outcomes (Rauschenberger et al. 2019). Such outcomes are not naturally but artificially binary. They indicate whether an underlying measurement is greater than a threshold.

Implements lasso and ridge regression for dichotomised outcomes (Rauschenberger et al. 2021). Such outcomes are not naturally but artificially binary. They indicate whether an underlying measurement is greater than a threshold.

Implements lasso and ridge regression for dichotomised outcomes (Rauschenberger et al. 2022). Such outcomes are not naturally but artificially binary. They indicate whether an underlying measurement is greater than a threshold.

Provides 'coroutines' for R, a family of functions that
can be suspended and resumed later on. This includes 'async'
functions (which await) and generators (which yield). 'Async'
functions are based on the concurrency framework of the 'promises'
package. Generators are based on a dependency free iteration
protocol defined in 'coro' and are compatible with iterators from
the 'reticulate' package.
knitr, later (>= 1.1.0), magrittr (>= 2.0.0), promises,
reticulate, rmarkdown, testthat (>= 3.0.0)

c(
person("Lionel", "Henry", , "lionel@rstudio.com", role = c("aut", "cre")),
person("RStudio", role = c("cph", "fnd"))
)
Provides 'coroutines' for R, a family of functions that can
be suspended and resumed later on. This includes 'async' functions
(which await) and generators (which yield). 'Async' functions are
based on the concurrency framework of the 'promises' package.
Generators are based on a dependency free iteration protocol defined
in 'coro' and are compatible with iterators from the 'reticulate'
package.
Lionel Henry [aut, cre],
RStudio [cph, fnd]

Confounding Robust Independent Component Analysis for Noisy and
Grouped Data
Contains an implementation of a confounding robust independent component analysis (ICA) for noisy and grouped data. The main function coroICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding based on user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/coroICA/>.