Nothing
#' An R package for running the GUESS program. GUESS is a computationally optimised C++ implementation of a fully Bayesian variable selection approach that can analyse, in a genome-wide context, single and multiple responses in an integrated way. The program uses packages from the GNU Scientific Library (GSL) and offers the possibility to re-route computationally intensive linear algebra operations towards the Graphical Processing Unit (GPU)
#' through the use of proprietary CULA-dense library.
#' The multi-SNP model of GUESS typically seeks for the best combinations of SNPs to predict the (possibly multivariate) outcome of interest.
#' In its current implementation, using its GPU capacities, GUESS is able to handle hundreds of thousands of predictors, which enables genome-wide
#' sized datasets to be analysed. However, the use of GPU-based numerical libraries implies extensive data transfer between the memory/CPU and the GPU, which, in turn, can be computationally expensive. As a consequence, for smaller datasets (as the example provided in the package) for which the matrix operations are not rate-limiting, the CPU version of GUESS may be more efficient.
#' Hence, to ensure both an optimal use of the algorithm, and to enable running GUESS on non-CULA compatible systems, the call to GPU-based calculations within GUESS can easily be switched off with the argument \cite{CUDA} of the function \cite{\link{R2GUESS}}.
#' A documentation of the C++ code is presenting at \url{http://www.bgx.org.uk/software/GUESS_Doc_short.pdf}.
#' @name R2GUESS-package
#' @aliases R2GUESS-package
#' @docType package
#' @title Sparse Bayesian variable selection method for multiple correlated outcomes in a regression context.
#' @author Benoit Liquet, \email{benoit.liquet@@isped.u-bordeaux2.fr} , Marc Chadeau \email{m.chadeau@@imperial.ac.uk}, Leonardo Botollo \email{l.bottolo@@imperial.ac.uk}, Gianluca Campanelle \email{g.campanella11@@imperial.ac.uk}
#' @references
#' Bottolo L and Richardson S (2010). Evolutionary Stochastic Search for Bayesian model exploration. Bayesian Analysis 5(3), 583-618.
#'
#' Petretto E, Bottolo L, Langley SR, Heinig M, McDermott-Roe C, Sarwar R, Pravenec M, Hubner N, Aitman TJ, Cook SA and Richardson S (2010).
#' New insights into the genetic control of gene expression using a Bayesian multi-tissue approach.
#' PLoS Comput. Biol., 6(4), e1000737.
#' @keywords package
#' @seealso \code{\link{R2GUESS}}, \code{\link{as.ESS.object}}, \code{\link{plotMPPI}}, \code{\link{plot.ESS}}
NULL
#' Data set relative to the regulation genetic of the experssion level of the gene HOPX from the Rats.
#'
#' This data set contains the gene expresion of the gene Hopx from 4 tissues of rats (Adrenal gland, Fat, Heart, Kidney).
#' @name data.Y.Hopx
#' @docType data
#' @format A dataframe with 29 rows and 4 columns (ADR, FAT, Heart, Kidney)
NULL
#' Data set relative to the regulation genetic of the Rats.
#'
#' This data set contains the genetic variation (SNPs) of 29 rats.
#' @name data.X
#' @docType data
#' @format A dataframe with 29 rows and 770 columns
NULL
#' MAP file relative to the genotype performed on the rats .
#'
#' This data set contains the information on the SNPs: SNP name (\code{SNPName}), Chromosome (\code{Chr}), start position (\code{Posn}), end position (\code{End})
#' @name MAP.file
#' @docType data
#' @format A data frame with 770 rows and 4 columns
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.