Table 6.

A summary of PLS software.

Number	Software	Author/year	Language	Features
1	PLS Discriminant Analysis	Barker and Rayens [24]	C/C++, Visual Basic	PLS for discriminant analysis
2	Least Squares–PLS	Jørgensen et al. [25]	R	Implementation combining PLS and ordinary least squares
3	Powered PLS Discriminant Analysis	Liland and Indahl [26]	R	Extraction of information for multivariate classification problems
4	Penalized PLS	Kr$\ddot{\text{a}}$mer et al. (2008) [27]	R	Extension of PLS regression using penalization technique
5	SlimPLS	Gutkin et al. [22]	R	Multivariate feature extraction method which incorporates feature dependencies
6	Sparse PLS Discriminant Analysis, Sparse Generalized PLS	Chung and Keles [28]	R	Sparse version techniques employing feature extraction and dimension reduction simultaneously
7	PLS Degrees of Freedom	Kramer and Sugiyama [29]	R	Using an unbiased estimation of the degrees of freedom for PLS regression
8	Surrogate Variable Analysis PLS	Chakraborty and Datta [30]	R	Extraction of the informative features with hidden confounders which are unaccounted for
9	PLS Path Modelling	Sanchez and Trinchera [31]	R	A multivariate feature extraction analysis technique based on the cause-effect relationships of the unobserved and observed features
10	PLS Regression for Generalized Linear Models	Bertrand et al. (2013) [32]	R	PLS regression is used to extract the predictive features from the generalized linear models