Packaged: Thu Feb 12 14:27:48 2009; davison

Title: Practicals for Use with Davison (2003) Statistical Models
Description: Contains the datasets and a few functions for use with
the practicals outlined in Appendix A of the book

Author: Ben Deivide de Oliveira Batista, Daniel Furtado Ferreira
Maintainer: Daniel Furtado Ferreira <danielff@dex.ufla.br>
Description: Computes the studentized midrange distribution (pdf, cdf
and quantil) and generates random numbers

Title: Externally Studentized Midrange Distribution
Description: Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers
Packaged: 2014-11-25 18:27:14 UTC; Benallanna

person("Ben", "Deivide", email = "ben.deivide@ufsj.edu.br",
person("Daniel", "Furtado", email = "danielff@ufla.br", role = c("aut", "cre"),
Description: Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers.
Author: Ben Deivide [aut] (<https://orcid.org/0000-0001-7019-8794>),
Daniel Furtado [aut, cre] (<https://orcid.org/0000-0002-4371-5239>)

Description: Computes the studentized midrange distribution (pdf, cdf and quantil) and generates random numbers

Title: Multi-SNP Mediation Intersection-Union Test
Description: Testing the mediation effect of multiple SNPs on an outcome through a mediator.

Description: Testing the mediation effect of multiple SNPs on an outcome through a gene.

Description: Implements the structural model for variances in order to detect differentially expressed genes from gene expression data
Packaged: Fri Oct  3 11:57:53 2008; ugengma

Packaged: Thu Feb 26 14:11:38 2009; ugengma

Description: Implements the structural model for variances in order to
detect differentially expressed genes from gene expression data

Description: Implementation of the structural model for variances in order to
detect differentially expressed genes from gene expression data.

Packaged: Mon Feb 16 15:30:11 2009; ugengma

Author: Bo Wang, Aziz Mezlini, Feyyaz Demir, Marc Fiume, Zhuowen Tu, Michael Brudno, Benjamin Haibe-Kains, Anna Goldenberg
Description: Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.