breedR: Statistical Methods for Forest Genetic Resources Analysts

#' Frequentist and Bayesian methods for breeders, quantitative geneticists and 
#' forest genetic resources analysts.
#' 
#' This package provides statistical tools to build predictive models for the
#' breeders, quantitative genetists and forest genetic resources analysts
#' communities.  It aims to assess the genetic value of individuals under a
#' number of situations, including spatial autocorrelation, genetic/environment
#' interaction and competition. It is under active development as part of the
#' Trees4Future project, particularly developed having forest genetic trials in
#' mind. But can be used for animals or other situations as well.
#' 
#' The package functionality builds up on a wrapping up of Ignacy Misztal's 
#' progsf90 suite of Fortran programs. Particularly, the function \code{reml} 
#' performs classical Restricted-Maximum Likelihood inference by interfacing 
#' Misztal's programs with several high-level options such as spatial 
#' components, etc. The Fortran back-end allows for fast inference on rather
#' large datasets (hundreds of thousands of individuals) with complex pedigrees.
#' 
#' @name breedR-package
#' @aliases breedR-package breedR
#' @docType package
#' @seealso \code{\link[pedigreemm]{pedigreemm}}
#' @references Most functionality in the package is based on Ignacy Misztal's
#' suite of Fortran programs for mixed model computations in breeding.
#' \url{http://nce.ads.uga.edu/wiki/doku.php}
#' 
#' M. Lynch \& B. Walsh (1998). \emph{Genetics and Analysis of Quantitative
#' Traits}. Sinauer Associates, Inc.
#' @keywords package
#' @examples
#' 
#'   # Load, summarize and visualize data
#'   data(m4)
#'   summary(m4)
#'   plot(m4)
#' 
#'   # Fit Mixed Model using REML
#'   res.f90 <- remlf90(fixed = phe_X ~ gen, 
#'                      genetic = list(model = 'add_animal', 
#'                                     pedigree = get_pedigree(m4),
#'                                     id = 'self'),
#'                      data = as.data.frame(m4))
#'   
#'   # Summary of results
#'   summary(res.f90)
#'   
#'   # Observed phenotypes vs. Fitted values
#'   library(ggplot2)
#'   qplot(phe_X, fitted(res.f90), color=gen, data = as.data.frame(m4)) +
#'     geom_abline(intercept=0, slope=1)
#' 
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