pcaExplorer - Interactive exploration of Principal Components of Samples and Genes in RNA-seq data

DA incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis). These discriminant analyses can be used to do ecological and evolutionary inference. We show the examples of demographic history inference, species identification, and population structure inference in the vignettes …

This repository provides code in R for the computer vision problem of human face recognition.

Supervised learning and unsupervised in R, with a focus on regression and classification methods.

Running through some R refresher

5 analytical tasks have been completed using VAT validated gower-PAM clustering, Correspondence Analysis (CA), Asym-Biplot, Multiple Correspondence Analysis (MCA), Chi-Squared test, Regression, and predictive classification models with KNN, SVM, and Random Forest.