Title: Gene Enrichment Analysis from ACSN Maps or GMT Files
Description: Compute and represent gene set enrichment or depletion from your data based on pre-saved maps from the Atlas of Cancer Signalling Networks (ACSN) or user imported maps.
User imported maps must be complying with the GMT format as defined by the Broad Institute, that is to say that the file should be tab-separated, that the first column should contain the module name, the second column can contain comments that will be overwritten with the number of genes in the module, and subsequent columns must contain the list of genes (HUGO symbols; tab-separated) inside the module.
Depends: R (>= 3.1.0),ggplot2, gridExtra,scales,

Description: Compute and represent gene set enrichment or depletion from your
data based on pre-saved maps from the Atlas of Cancer Signalling Networks (ACSN)
or user imported maps. User imported maps must be complying with the GMT format
as defined by the Broad Institute, that is to say that the file should be tab-
separated, that the first column should contain the module name, the second
column can contain comments that will be overwritten with the number of genes
in the module, and subsequent columns must contain the list of genes (HUGO
symbols; tab-separated) inside the module. The gene set enrichment can be run
with hypergeometric test or Fisher exact test, and can use multiple corrections.

Title: A Companion Package for the Book "A Course in Statistics with R"
Description: A book designed to meet the requirements of masters students. Tattar, P.N., Suresh, R., and Manjunath, B.G. "A Course in Statistics with R", J. Wiley, ISBN 978-1-119-15272-9.
Packaged: 2015-09-05 14:57:16 UTC; prabhanjan

Title: Asymptotic Classification Theory for Cognitive Diagnosis
Author: Chia-Yi Chiu (Rutgers, the State University of New Jersey) and Wenchao Ma (Rutgers, the State University of New Jersey)
Description: This package includes functions used to perform cluster analysis for cognitive
diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009). Given the
sample statistic of sum-scores and the item-by-attribute association matrix (Q-matrix; Tatsuoka, 1985),
cluster analysis techniques can be used to classify examinees into latent
classes based on their attribute patterns. In addition to the algorithms used to classify data, three
labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.