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#' Training of Neural Networks
#'
#' Training of neural networks using the backpropagation, resilient
#' backpropagation with (Riedmiller, 1994) or without weight backtracking
#' (Riedmiller, 1993) or the modified globally convergent version by
#' Anastasiadis et al. (2005). The package allows flexible settings through
#' custom-choice of error and activation function. Furthermore, the calculation
#' of generalized weights (Intrator O & Intrator N, 1993) is implemented.
#'
#' @name neuralnet-package
#' @docType package
#' @note This work has been supported by the German Research Foundation\cr
#' (DFG: \url{http://www.dfg.de}) under grant scheme PI 345/3-1.
#' @author Stefan Fritsch, Frauke Guenther \email{guenther@@leibniz-bips.de},
#'
#' Maintainer: Frauke Guenther \email{guenther@@leibniz-bips.de}
#' @seealso \code{\link{plot.nn}} for plotting of the neural network.
#'
#' \code{\link{gwplot}} for plotting of the generalized weights.
#'
#' \code{\link{compute}} for computation of the calculated network.
#'
#' \code{\link{confidence.interval}} for calculation of a confidence interval
#' for the weights.
#'
#' \code{\link{prediction}} for calculation of a prediction.
#' @references Riedmiller M. (1994) \emph{Rprop - Description and
#' Implementation Details.} Technical Report. University of Karlsruhe.
#'
#' Riedmiller M. and Braun H. (1993) \emph{A direct adaptive method for faster
#' backpropagation learning: The RPROP algorithm.} Proceedings of the IEEE
#' International Conference on Neural Networks (ICNN), pages 586-591. San
#' Francisco.
#'
#' Anastasiadis A. et. al. (2005) \emph{New globally convergent training scheme
#' based on the resilient propagation algorithm.} Neurocomputing 64, pages
#' 253-270.
#'
#' Intrator O. and Intrator N. (1993) \emph{Using Neural Nets for
#' Interpretation of Nonlinear Models.} Proceedings of the Statistical
#' Computing Section, 244-249 San Francisco: American Statistical Society
#' (eds).
#' @keywords neural
#' @examples
#'
#' AND <- c(rep(0,7),1)
#' OR <- c(0,rep(1,7))
#' binary.data <- data.frame(expand.grid(c(0,1), c(0,1), c(0,1)), AND, OR)
#' print(net <- neuralnet(AND+OR~Var1+Var2+Var3, binary.data, hidden=0,
#' rep=10, err.fct="ce", linear.output=FALSE))
#'
#' XOR <- c(0,1,1,0)
#' xor.data <- data.frame(expand.grid(c(0,1), c(0,1)), XOR)
#' print(net.xor <- neuralnet(XOR~Var1+Var2, xor.data, hidden=2, rep=5))
#' plot(net.xor, rep="best")
#'
#' data(infert, package="datasets")
#' print(net.infert <- neuralnet(case~parity+induced+spontaneous, infert,
#' err.fct="ce", linear.output=FALSE, likelihood=TRUE))
#' gwplot(net.infert, selected.covariate="parity")
#' gwplot(net.infert, selected.covariate="induced")
#' gwplot(net.infert, selected.covariate="spontaneous")
#' confidence.interval(net.infert)
#'
#' Var1 <- runif(50, 0, 100)
#' sqrt.data <- data.frame(Var1, Sqrt=sqrt(Var1))
#' print(net.sqrt <- neuralnet(Sqrt~Var1, sqrt.data, hidden=10,
#' threshold=0.01))
#' predict(net.sqrt, data.frame(Var1 = (1:10)^2))
#'
#' Var1 <- rpois(100,0.5)
#' Var2 <- rbinom(100,2,0.6)
#' Var3 <- rbinom(100,1,0.5)
#' SUM <- as.integer(abs(Var1+Var2+Var3+(rnorm(100))))
#' sum.data <- data.frame(Var1,Var2,Var3, SUM)
#' print(net.sum <- neuralnet(SUM~Var1+Var2+Var3, sum.data, hidden=1,
#' act.fct="tanh"))
#' prediction(net.sum)
#'
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