Daniela R. Recchia <daniela_recchia@yahoo.com.br>, Emanuel P.
Barbosa, Elias de Jesus Goncalves <eliasjg@gmail.com>
Builds statistical control charts with exact limits for
univariate and multivariate cases.

Emanuel P. Barbosa,
Flavio M. M. Barros <flaviomargarito@gmail.com>,
Elias de Jesus Goncalves <eliasjg@gmail.com>,
Daniela R. Recchia <daniela_recchia@yahoo.com.br>

c(person("Flavio", "Barros", email = "flaviomargarito@gmail.com", role = c("aut", "cre")),
person("Emanuel", "Barbosa", role = "ctb"),
person("Elias", "Goncalves", role = "ctb"),
person("Daniela", "Recchia", role = "ctb"))
Flavio Barros [aut, cre],
Emanuel Barbosa [ctb],
Elias Goncalves [ctb],
Daniela Recchia [ctb]

Daniela R. Recchia <daniela_recchia@yahoo.com.br>, Emanuel P.
Barbosa

Identification and Quantification of Metabolites by Using 1D 1H
NMR FID
R (>= 2.10.0), neldermead, Rmpi, papply, signal, outliers
Critical to metabolomics is the unambiguous identification
and absolute quantification of metabolites. One-dimensional
(1D) proton (1H) NMR has formed the bedrock of metabolomics
studies to date. Automated and high throughput identification
and quantification of metabolites is a big challenge for
One-dimensional (1D) proton (1H) NMR. This package present a
new automated technique based on Message Passing Interface
(MPI), relaxation algorithm, pattern identification, Madison
Metabolomics Consortium Database (MMCD), and 1D 1H NMR free
induction decay (FID), for the unambiguous identification and
absolute quantification of targeted metabolites.

R (>= 2.12.0), neldermead, Rmpi, papply, signal, outliers

R (>= 2.10.0), ff, neldermead, Rmpi, papply, signal, outliers

A R Interface for the Instantaneous RATEs (IRATE) Model
A R interface to setup, run and read IRATE model runs to assess band recovery (conventional tagging) data (i.e. age-dependent or independent fishing and natural mortality rates).

person(given = "Donald",
family = "Williams",
role = c("aut", "cre"),
email = "drwwilliams@ucdavis.edu")
Check the irrepresentable condition (IRC) in both L1-regularized regression <doi:10.1109/TIT.2006.883611>
and Gaussian graphical models. The IRC requires that the important and unimportant variables
are not correlated, at least not all that much, and it is necessary for consistent model
selection. Exploring the IRC as a function of the number of variables, assumed sparsity,
and effect size can provide valuable insights into the model selection
properties of L1-regularization.
glmnet, MASS, Rdpack, GGMncv, corpcor, parallel