Title: Bayesian reconstruction of growth velocity
Author: Sara Lopez-Pintado and Ian W. McKeague
Description: A nonparametric empirical Bayes method for recovering
gradients (or growth velocities) from observations of smooth
functions (e.g., growth curves) at isolated time points.
Depends: Matrix, clime, fUtilities, mvtnorm

Description: A nonparametric empirical Bayes method for recovering gradients (or growth velocities) from observations of smooth functions (e.g., growth curves) at isolated time points.

Maintainer: Ian W. McKeague <im2131@columbia.edu>

Title: Estimate Growth Rates from Experimental Data
Description: A collection of methods to determine growth rates from
experimental data, in particular from batch experiments and
Imports: stats, graphics, methods, parallel, utils, FME

Authors@R: c(person("Thomas","Petzoldt", role = c("aut", "cre"),
Author: Thomas Petzoldt [aut, cre] (<https://orcid.org/0000-0002-4951-6468>)

Title: Penalized Cox Model for High-Dimensional Data with Grouped
Description: Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty. The algorithms combine the MM approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided.
Depends: Matrix (>= 1.2-10), MASS, colorspace

Description: Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation (SCAD), and group minimax concave penalty (MCP). The algorithms combine the Majorization Minimization (MM) approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided.

Title: Group Sorted L1 Penalized Estimation
Authors@R: c(person("Alexej", "Gossmann", email = "alexej.go@googlemail.com", role = c("aut", "cre")),
person("Damian", "Brzyski", role = "aut"),
person("Ewout", "van den Berg", role = "ctb",
comment = "A part of the optimization code was obtained from http://statweb.stanford.edu/~candes/SortedL1/software.html under GNU GPL-3"),
person("Emmanuel", "Candes", role = "ctb",
comment = "A part of the optimization code was obtained from http://statweb.stanford.edu/~candes/SortedL1/software.html under GNU GPL-3"))
Description: Group SLOPE is a penalized linear regression method that is used
for adaptive selection of groups of significant predictors in a
The Group SLOPE method can control the (group) false discovery rate at a
user-specified level (i.e., control the expected proportion of irrelevant
among all selected groups of predictors).
Ewout van den Berg [ctb] (A part of the optimization code was obtained
Emmanuel Candes [ctb] (A part of the optimization code was obtained