Title: Processing of Accelerometry Data with 'GGIR' in mMARCH
role = c("aut", "cre"), email = "wei.guo3@nih.gov"),
person(given = "Andrew", family = "Leroux",
role = c("aut"), email = "andrew.leroux@cuanschutz.edu"),
person(given = "Vadim", family = "Zipunnikov",
role = c("aut"), email = "vzipunni@jhsph.edu"),
person(given = "Kathleen", family = "Merikangas",
role = c("aut"), email = "merikank@mail.nih.gov")
Imports: refund, denseFLMM, dplyr, xlsx, survival, stats, tidyr, zoo,
ineq, cosinor, cosinor2, abind, accelerometry, ActCR, ActFrag,

Title: Implementation of Marker-Assisted Mini-Pooling with Algorithm
Author: "Tao Liu <tliu@stat.brown.edu> [aut, cre]" "Yizhen Xu <yizhen_xu@brown.edu> [aut]"
Description: To determine the number of quantitative assays needed for a sample
of data using pooled testing methods, which include mini-pooling (MP), MP
with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number
of assays needed, the package also provides a tool to conduct Monte Carlo (MC)
to simulate different orders in which the sample would be collected to form pools.
Using MC avoids the dependence of the estimated number of assays on any specific

Author: "Tao Liu, PhD <tliu@stat.brown.edu> [aut, cre]" "Yizhen Xu, ScM <yizhen_xu@brown.edu> [aut]"
Maintainer: "Tao Liu, PhD" <tliu@stat.brown.edu>

Title: Metabolomic Quantitative Trait Locus Mapping
Author: Lyamine Hedjazi and Jean-Baptiste Cazier
Description: mQTL provides a complete QTL analysis pipeline for metabolomic data.
Distinctive features include normalisation using PQN approach, peak alignment
using RSPA approach, dimensionality reduction using SRV approach and finally
Depends: R (>= 2.15.0), qtl, MASS, outliers
Packaged: 2013-10-21 09:31:01 UTC; Utilisateur

Title: R package for parallelized mRMR ensemble feature selection
Author: Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains
Description: This package contains a set of function to compute mutual
information matrices from continuous, categorical and survival
variables. It also contains function to perform feature
selection with mRMR and a new ensemble mRMR technique.