LS-TreeBoost and LAD-TreeBoost for Gene Regulatory Network
Reconstruction
Raghvendra Mall [aut, cre],
Khalid Kunji [aut],
Melissa O'Neill [ctb]
c(person(given = "Raghvendra",family = "Mall",role = c("aut","cre"), email = "raghvendra5688@gmail.com"),
person(given="Khalid",family="Kunji",role="aut",email = "kkunji@hbku.edu.qa"),
person(given="Melissa",family="O'Neill",role="ctb"))
Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc).

Raghvendra Mall [aut, cre], Khalid Kunji [aut], Melissa O'Neill [ctb]
c(person(given = "Raghvendra",family = "Mall",role = c("aut","cre"), email = "rmall@hbku.edu.qa"),
person(given="Khalid",family="Kunji",role="aut",email = "kkunji@hbku.edu.qa"),
person(given="Melissa",family="O'Neill",role="ctb"))

Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc). See Mall et al (2017) <doi:10.1101/132670>.

Regularized Generalized Canonical Correlation Analysis

RGCCA and Sparse GCCA for multi-block data analysis
Multi-block data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: (i) to study the relationships between blocks and (ii) to identify subsets of variables of each block which are active in their relationships with the other blocks.

Regularized and Sparse Generalized Canonical Correlation
Analysis for Multiblock Data
Multiblock data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: (i) to study the relationships between blocks and (ii) to identify subsets of variables of each block which are active in their relationships with the other blocks.

Regularized and Sparse Generalized Canonical Correlation
Analysis for Multi-Block Data
Multi-block data analysis concerns the analysis of several sets
of variables (blocks) observed on the same group of individuals. The main aims
of the RGCCA package are: (i) to study the relationships between blocks and
(ii) to identify subsets of variables of each block which are active in their
relationships with the other blocks.