correlation coefficient in independent ad dependent samples, sequential triangular
test for the product-moment correlation coefficient, partial and semipartial correlation,
simulation of bivariate normal and non-normal distribution with a specified correlation.

Maintainer: Sven E. Templer <sven.templer@gmail.com>
Description: A set of miscellaneous R tools to simplify the work with several

Imports: Rcpp (>= 0.11.1), parallel, xtable

Imports: parallel, Rcpp (>= 0.11.1), xtable

Imports: parallel, data.table, gridExtra, Rcpp (>= 0.11.1), xtable

Description: A collection of miscellaneous methods to simplify various tasks,
including plotting, data.frame and matrix transformations, environment
functions, regular expression methods, and string and logical operations, as
well as numerical and statistical tools. Most of the methods are simple but
useful wrappers of common base R functions, which extend S3 generics or
provide default values for important parameters.
Imports: ggplot2, gridExtra, parallel, Rcpp (>= 0.11.1), xtable

Imports: data.table, devtools, ggplot2, gridExtra, parallel, Rcpp (>=

Title: Clears the Workspace (Mise en Place)
Authors@R: person("Patrick", "Applegate", email = "applegatepj@gmail.com",
Description: Clears the workspace. Useful for the beginnings of R scripts, to
avoid potential problems with accidentally using information from variables
or functions from previous script evaluations, too many figure windows open
at the same time, packages that you don't need any more, or a cluttered
console.  Uses code from various StackOverflow users.  See help(mise) for
pointers to the relevant StackOverflow pages.

Title: Variable Selection for Multiply Imputed Data
person("Alexander", "Rix", email = "alexrix@umich.edu", role = c("aut", "cre")),
person("Jiacong", "Du", email = "jiacong@umich.edu", role = "aut")
Penalized regression methods, such as lasso and elastic net, are used in
many biomedical applications when simultaneous regression coefficient
estimation and variable selection is desired. However, missing data
complicates the implementation of these methods, particularly when
missingness is handled using multiple imputation. Applying a variable
selection algorithm on each imputed dataset will likely lead
to different sets of selected predictors, making it difficult
to ascertain a final active set without resorting to ad hoc
combination rules. 'miselect' presents Stacked Adaptive Elastic Net (saenet)
and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes,
developed by Du et al (2020), currently under review. They, by construction,
force selection of the same variables across multiply imputed data.
'miselect' also provides cross validated variants of these methods.
Suggests: mice, knitr, rmarkdown, testthat