Suggests: knitr, rmarkdown, survival, tinytest
exponential lasso, the composite MCP, and the group bridge.  For more
information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>,
Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang
<doi:10.1111/biom.12300>, or visit the package homepage

Suggests: knitr, rmarkdown, splines, survival, tinytest

Title: Penalized Regression Models with Overlapping Grouped Covariates
Description: Fit the regularization path of linear, logistic or poisson models with
overlapping grouped covariates based on the latent group lasso approach. Latent
group MCP/SCAD as well as bi-level selection methods, namely the group exponential
lasso and the composite MCP are also available. This package serves as an
extension of R package 'grpreg' (by Dr. Patrick Breheny <patrick-breheny@uiowa.edu>)
for grouped variable selection involving overlaps between groups.

Description: Fit the regularization path of linear, logistic or Cox models with

Description: Provides tools for sparse regression modelling with grouped predictors using the group
subset selection penalty. Uses coordinate descent and local search algorithms to rapidly deliver
near optimal estimates. The group subset penalty can be combined with a group lasso or ridge
penalty for added shrinkage. Linear and logistic regression are supported, as are overlapping

Title: Group Sequential Analysis of Clinical Trials
Description: Design of group sequential trials, including non-binding futility analysis

Description: Contains the tools to screen grouped variables, and select screened grouped variables afterwards. The main function grpss() can perform the grouped variables screening as well as selection for ultra-high dimensional data with group structure. The screening step is primarily used to reduce the dimensions of data so that the selection procedure can easily handle the moderate or low dimensions instead of ultra-high dimensions.
Imports: MASS, doParallel, foreach, grpreg

Title: Alternative Implementations of Base R Functions
Description: Alternative implementations of some base R functions, including sort, order, and match.  Functions are simplified but can be faster or have other advantages.
Packaged: 2016-01-21 15:40:04 UTC; Craig_Varrichio