Implementation of Marker-Assisted Mini-Pooling with Algorithm
"Tao Liu <tliu@stat.brown.edu> [aut, cre]" "Yizhen Xu <yizhen_xu@brown.edu> [aut]"
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
ordering of the samples to form pools.

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

Metabolomic Quantitative Trait Locus Mapping
Lyamine Hedjazi and Jean-Baptiste Cazier
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
QTL mapping using R/qtl package.

R package for parallelized mRMR ensemble feature selection
Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains
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.

Nicolas De Jay, Simon Papillon-Cavanagh, Catharina Olsen,
Gianluca Bontempi, Benjamin Haibe-Kains

Nicolas De Jay, Simon Papillon-Cavanagh, Catharina Olsen, Gianluca Bontempi, Benjamin Haibe-Kains
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.

Parallelized Minimum Redundancy, Maximum Relevance (mRMR)
Ensemble Feature Selection
Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique.