The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed.

Multivariate Analysis and Visualization for Biological Data
multiDimBio is a package designed to support a systems biology research program from inception through publication.  It focuses on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations.  The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques.  As a result, we encourage suggested additions to the package, both methodological and graphical.

Code to support a systems biology research program from inception through publication.  The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations.  The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques.  As a result, we encourage suggested additions to the package, both methodological and graphical.

Latent Variable Model to Infer Food Intake from Multiple
Biomarkers
A latent variable model based on factor analytic and mixture of experts models, designed to infer food intake from multiple biomarkers data. The model is framed within a Bayesian hierarchical framework, which provides flexibility to adapt to different biomarker distributions and facilitates inference on food intake from biomarker data alone, along with the associated uncertainty. Details are in D'Angelo, et al. (2020) <arXiv:2006.02995>.
c( person("Silvia", "D\'Angelo", role = c("aut", "cre"),
email = "silvia.dangelo@ucd.ie"),
person("Claire", "Gormley", role = "ctb"),
person("Lorraine", "Brennan", role = "ctb")
)
Silvia D'Angelo [aut, cre],
Claire Gormley [ctb],
Lorraine Brennan [ctb]