car, MASS, parallel, pbapply, plotrix, rgl
Tools for performing variable selection in three-way data using N-PLS in combination with L1 penalization.

car, ks, MASS, Matrix, parallel, pbapply, plotrix, rgl

Tools for performing variable selection in three-way data using N-PLS
in combination with L1 penalization. The N-PLS model (Rasmus Bro, 1996
<DOI:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) is the
natural extension of PLS (Partial Least Squares) to N-way structures, and tries
to maximize the covariance between X and Y data arrays. The package also adds
variable selection through L1 penalization.

car, ggplot2, ks, MASS, Matrix, parallel, pbapply, plotrix,
rgl

clickR, future, future.apply, ggplot2, ggrepel, ks, MASS,
Matrix, pbapply
Tools for performing variable selection in three-way data using N-PLS
in combination with L1 penalization, Selectivity Ratio and VIP scores.
The N-PLS model (Rasmus Bro, 1996 <DOI:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>)
is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries
to maximize the covariance between X and Y data arrays. The package also adds
variable selection through L1 penalization, Selectivity Ratio and VIP scores.

c(person("Attila", "Csala", role = c("aut", "cre"), email = "a@csala.me"),
person("Koos", "Zwinderman", role = "ctb", email = "a.h.zwinderman@amc.uva.nl"))
Attila Csala [aut, cre],
Koos Zwinderman [ctb]
Sparse redundancy analysis
for high dimensional (biomedical) data. Directional multivariate analysis
to express the maximum variance in the predicted data set by a linear
combination of variables of the predictive data set. Implemented in a
partial least squares framework, for more details see Csala et al. (2017) <doi:10.1093/bioinformatics/btx374>.
R (>= 2.7), Matrix, doParallel, elasticnet, foreach, mvtnorm

Analysis of Small RNA Expression Changes in Hybrid Plants
The most important function of the R package is the genetic effects analysis of small RNA in hybrid plants via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the small RNA in progeny with the expression level in the parent species.
DESeq2, futile.logger, ggplot2, ggsci, plyr, VennDiagram