```
#======== todo =================================================================
#t1 imported packages: stats, reshape2, prodlim
#' pdmpsim: simulate PDMPs
#'
#' The core of this package is the S4 class \code{\link{pdmpModel}} that has the
#' aim to represent piecewise deterministic markov processes (PDMPs) in R. If a
#' PDMP is implemented as \code{pdmpModel}, it can be simulated with method
#' \code{\link{sim}}. The package provides another class named
#' \code{\link{multSim}} to store multiple simulations and some methods for
#' plotting and analysing the simulation results. Additionally the generator of
#' a PDMP can be calculated with method \code{\link{generator}}.
#'
#' @section PDMPs:
#' A PDMP is a stochastic process that consists of continous variables and
#' discrete variables. Discrete variables are simulated like a usual discrete
#' markov chain with finite state space. The jump rates for the different states
#' are defined in slot \code{ratefunc} where as the state of the variable after
#' a jump is generated using slot \code{jumpfunc}. The continous variables
#' evolve according to ODEs that are defined in slot \code{dynfunc}. These ODEs
#' usually depend on the states of the discrete variables. The number of the
#' continous variables is however fixed and does not change during the
#' simulation. This is one restriction compared to the more general definition
#' of PDMPs given in [Dav84]. The other restriction concernes borders for the
#' continous variables which are implemented in the \code{\link{pdmpBorder}} Subclass.
#' See [Zei09] and [Ben+15] for an introduction of PDMPs with definitions that
#' match with the implementation of this package.
#'
#' @section Provided Methods:
#' There is a bunch of methods that can be used to analyse the simulation
#' results. A single simulation stored in slot \code{out} of class
#' \code{pdmpModel} can be visualised with \code{\link{plot}} and summarized
#' with \code{\link{summarise}}. To store multiple simulations in a conventient
#' way, use \code{\link{multSim}} or \code{\link{multSimCsv}}.
#' The latter is only needed for simulations generating big data that can not be
#' loaded to the working memory anymore. Method \code{multSimCsv} stores the
#' results in csv files and returnes a class with links to the corresponding
#' files instead of returning the simulations themselves.
#'
#' @section Imported Packages:
#' The following packages are needed for package \pkg{pdmpsim} to work: \cr
#' For solving the ODEs during the simulation, package \pkg{deSolve} is required.
#' To store multiple simulations as \code{csv} files and work with them without
#' loading them into the working memory, package \pkg{LaF} is needed.
#' Package \pkg{Deriv} is only used in function \code{\link{generator}}.
#' Most of the plot methods are based on \pkg{ggplot2} and some additional
#' \code{ggplot2} packages.
#'
#' @references
#' \tabular{ll}{
#' \eqn{\,}{ }[Dav84] \tab Davis, M. H. (1984). Piecewise-deterministic Markov processes:
#' A general class of \cr
#' \tab non-diffusion stochastic models. \emph{Journal of the Royal
#' Statistical Society. Series B} \cr
#' \tab \emph{(Methodological)}, 353-388. \cr
#' [Zei09] \tab S. Zeiser. \emph{Classical and Hybrid Modeling of Gene
#' Regulatory Networks}. 2009. \cr
#' [Ben+15]\eqn{\,\,\,\,}{ } \tab Benaïm, M., Le Borgne, S., Malrieu, F.,
#' & Zitt, P. A. (2015). Qualitative properties \cr
#' \tab of certain piecewise deterministic Markov processes.
#' In \emph{Annales de l'Institut Henri} \cr
#' \tab \emph{Poincaré, Probabilités et Statistiques} (Vol. 51, No. 3,
#' pp. 1040-1075). Institut \cr
#' \tab Henri Poincaré. \cr
#' }
#'
#' @name pdmpsim
#' @docType package
NULL
```

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