Author: Charles Bouveyron, Yacine Jernite, Pierre Latouche, Laetitia Nouedoui
Description: Estimate the parameters, the number of classes and cluster vertices of a random network into groups with homogeneous connection profiles. The clustering is performed for directed graphs with typed edges (edges are assumed to be drawn from multinomial distributions) for which a partition of the vertices is available.

Packaged: 2013-11-13 09:34:42 UTC; pierrelatouche

Title: Tools For Managing File/function Dependencies In R
Description: A "define" function is provided that allows
for inclusion of external scripts without polluting
the global namespace and makes it easier to

Author: Rory Michelland <rory.michelland@gmail.com>, Genevieve
Description: A graphical user interface program to analyze microbial
profiles (taxonomic sequences, fingerprints and microarrays)
based on conditional inference random forest

Title: Efficient Numerical Algorithm for Exact Inference in Meta
Description: A novel numerical algorithm that provides functionality for estimating the exact 95% confidence interval of the location parameter in the random effects model, and is much faster than the naive method. Works best when the number of studies is between 6-20.

Description: Performs random projection using Johnson-Lindenstrauss (JL) Lemma (see William B.Johnson and Joram Lindenstrauss (1984) <doi:10.1090/conm/026/737400>). Random Projection is a technique, where the data in the high dimensional space is projected into the low dimensional space using JL transform. The original high dimensional data matrix is multiplied with the low dimensional random matrix which results in reduced matrix. The random matrix can be generated by Gaussian matrix or sparse matrix.

Title: Random Projection with Classification
Description: Performs random projection using Johnson-Lindenstrauss (JL) Lemma (see William B.Johnson and Joram Lindenstrauss (1984) <doi:10.1090/conm/026/737400>). Random Projection is a dimension reduction technique, where the data in the high dimensional space is projected into the low dimensional space using JL transform. The original high dimensional data matrix is multiplied with the low dimensional projection matrix which results in reduced matrix. The projection matrix can be generated using the projection function that is independent to the original data. Then finally apply the classification task on the projected data.