The analysis of highdimensional datasets often requires to extract meaningful variables from the data or compress the data into a more tractable number of features. In technical terms, for a highdimensional dataset X with N samples and P dimensions (traits), dimensionality reduction techniques aim: i) to provide a meaningful lowdimensional representation Z of K dimensions while only losing minor amounts of information, ii) to use only a small number of free parameters, iii) to preserve the quantities of interest in the data. There are a variety of approaches for dimensionality reduction with different underlying mathematical concepts and parameters. This package provides a simple function to access 12 dimensionality reduction methods (DiffusionMap, DRR, ICA, LLE, Isomap, LaplacianEigenmap, MDS, PCA, kPCA, nMDS, tSNE and UMAP). It introduces a new stability criterion, which identifies the lower dimensional componet Z that can reliably be found in crossvalidation on different subsets of M samples and P traits.
Package details 


Maintainer  
License  MIT + file LICENSE 
Version  0.0.1 
URL  https://github.com/HannahVMeyer/drStable 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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