@article{Camacho201549,
author = "Camacho, Jos{\'e} and Villegas, Alejandro P{\'e}rez and Rodr{\'i}guez-G{\'o}mez, Rafael A. and Elena Jim{\'e}nez-Ma{\~n}as",
abstract = "Abstract The Multivariate Exploratory Data Analysis (MEDA) Toolbox in Matlab is a set of multivariate analysis tools for the exploration of data sets. In the \{MEDA\} Toolbox, traditional exploratory plots based on Principal Component Analysis (PCA) or Partial Least Squares (PLS), such as score, loading and residual plots, are combined with new methods like MEDA, oMEDA and \{SVI\} plots. The latter are aimed at solving some of the limitations found in the former to adequately extract conclusions from a data set. Also, other useful tools such as cross-validation algorithms, Multivariate Statistical Process Control (MSPC) charts and data simulation/approximation algorithms (ADICOV) are included in the toolbox. Finally, most of the exploratory tools are extended for their use with very large data sets (Big Data), with unlimited number of observations. ",
doi = "http://dx.doi.org/10.1016/j.chemolab.2015.02.016",
issn = "0169-7439",
journal = "Chemometrics and Intelligent Laboratory Systems ",
keywords = "Exploratory Data Analysis",
pages = "49 - 57",
title = "{M}ultivariate {E}xploratory {D}ata {A}nalysis ({MEDA}) {T}oolbox for {M}atlab ",
url = "http://www.sciencedirect.com/science/article/pii/S0169743915000465",
volume = "143",
year = "2015",
}