Author: Jun Chen <Chen.Jun2@mayo.edu>, Ehsan Behnam <behnamgh@usc.edu>
Description: Introduces an improved Surrogate Variable Analysis
algorithm that automatically captures salient features from data in the presence
of confounding factors. The algorithm is based on the popular 'SVA' package and
proposes a revision on it that achieves 10 times faster running time while

Description: Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional genomic data. The algorithm is based on the popular 'SVA' package and proposes a revision on it that achieves more than 10 times faster running time while trading no accuracy loss in return.

Title: Fast and Robust Surrogate Variable Analysis
Description: Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional data sets. The algorithm is built on the 'irwsva.build' function of the 'sva' package and proposes a revision on it that achieves more than 10 times faster running time while trading no accuracy loss in return.

Description: Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional data sets. The algorithm is built on the 'irwsva.build' function of the 'sva' package and proposes a revision on it that achieves an order of magnitude faster running time while trading no accuracy loss in return.
Depends: R (>= 3.1.0), sva, isva, RSpectra

Title: Online Unsupervised Outlier Detection Using Finite Mixtures with
Description: Addressing the problem of outlier detection from the viewpoint of statistical learning theory. This method is proposed by Yamanishi, K., Takeuchi, J., Williams, G. et al. (2004) <DOI:10.1023/B:DAMI.0000023676.72185.7c>. It learns the probabilistic model (using a finite mixture model) through an on-line unsupervised process. After each datum is input, a score will be given with a high one indicating a high possibility of being a statistical outlier.