Title: Polygenic Inverse Gamma rate Shifts
Description: Fits models of gene expression evolution to expression data from
coregulated groups of genes, assuming inverse gamma distributed rate
Author: Joshua G. Schraiber <jgschraiber@berkeley.edu>
Maintainer: Joshua G. Schraiber <jgschraiber@berkeley.edu>
Packaged: 2014-02-03 02:03:42 UTC; joshuaschraiber

Author: Joshua G. Schraiber <schraib@uw.edu>
Maintainer: Joshua G. Schraiber <schraib@uw.edu>
Packaged: 2015-12-07 19:42:26 UTC; joshuaschraiber

Title: Estimates the parameters of a trading-tree model for the
computation of the probability of informed trading
Suggests: zoo, xts, highfrequency, TTR, timeDate, quantmod
Description: Using information for trade initiation, the package
computes the probability of informed trading of Easley and
Packaged: 1987-12-31 23:30:08 UTC; Paolo Zagaglia

Title: Perturbation Clustering for Data Integration and Disease
Author: Hung Nguyen <hungnp@nevada.unr.edu>, Sangam Shrestha <sangam@nevada.unr.edu> and Tin Nguyen <tinn@unr.edu>
Description: Clustering algorithm for determining the number of clusters and location of each sample in the clusters. Perturbation clustering for data INtegration and disease Subtyping (PINS - Nguyen T, Tagett R, Diaz D, Draghici S (2017) <doi:10.1101/gr.215129.116>) is a novel approach for integration of data and classification of diseases into various subtypes. PINS supports both single and multiple data types. 'PINSPlus' is designed to be user-friendly and primarily makes use of two main functions. 'PINSPlus' fasten PINS algorithm by supporting parallel processing and using efficient stopping criteria.
Depends: R (>= 2.10), cluster, entropy, parallel, pbmcapply, doParallel

Title: Clustering Algorithm for Data Integration and Disease Subtyping
Author: Hung Nguyen, Sangam Shrestha and Tin Nguyen
Description: Provides a robust approach for omics data integration and disease subtyping. It is an improved implementation of PINS - Perturbation clustering for data INtegration and disease Subtyping (Nguyen T, Tagett R, Diaz D, Draghici S, Genome Research, 2017, <doi:10.1101/gr.215129.116>). 'PINSPlus' supports both single and multiple data types. The software automatically determines the optimal number of clusters and then partitions the samples in a way such that the results are robust to noise and data perturbation. 'PINSPlus' is fast and it supports parallel computing on Windows, Linux, and Mac OS.