rigraph: Network Analysis and Visualization

## -----------------------------------------------------------------------
##
##   IGraph R package
##   Copyright (C) 2015  Gabor Csardi <csardi.gabor@gmail.com>
##   334 Harvard street, Cambridge, MA 02139 USA
##
##   This program is free software; you can redistribute it and/or modify
##   it under the terms of the GNU General Public License as published by
##   the Free Software Foundation; either version 2 of the License, or
##   (at your option) any later version.
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##   This program is distributed in the hope that it will be useful,
##   but WITHOUT ANY WARRANTY; without even the implied warranty of
##   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
##   GNU General Public License for more details.
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##   You should have received a copy of the GNU General Public License
##   along with this program; if not, write to the Free Software
##   Foundation, Inc.,  51 Franklin Street, Fifth Floor, Boston, MA
##   02110-1301 USA
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## -----------------------------------------------------------------------

#' SIR model on graphs
#'
#' Run simulations for an SIR (susceptible-infected-recovered) model, on a
#' graph
#'
#' The SIR model is a simple model from epidemiology. The individuals of the
#' population might be in three states: susceptible, infected and recovered.
#' Recovered people are assumed to be immune to the disease. Susceptibles
#' become infected with a rate that depends on their number of infected
#' neighbors. Infected people become recovered with a constant rate.
#'
#' The function `sir()` simulates the model. This function runs multiple
#' simulations, all starting with a single uniformly randomly chosen infected
#' individual. A simulation is stopped when no infected individuals are left.
#'
#' Function `time_bins()` bins the simulation steps, using the
#' Freedman-Diaconis heuristics to determine the bin width.
#'
#' Function `median` and `quantile` calculate the median and
#' quantiles of the results, respectively, in bins calculated with
#' `time_bins()`.
#'
#' @aliases median.sir quantile.sir
#' @param graph The graph to run the model on. If directed, then edge
#'   directions are ignored and a warning is given.
#' @param beta Non-negative scalar. The rate of infection of an individual that
#'   is susceptible and has a single infected neighbor. The infection rate of a
#'   susceptible individual with n infected neighbors is n times beta. Formally
#'   this is the rate parameter of an exponential distribution.
#' @param gamma Positive scalar. The rate of recovery of an infected
#'   individual. Formally, this is the rate parameter of an exponential
#'   distribution.
#' @param no.sim Integer scalar, the number simulation runs to perform.
#' @param x A `sir` object, returned by the `sir()` function.
#' @param middle Logical scalar, whether to return the middle of the time bins,
#'   or the boundaries.
#' @param na.rm Logical scalar, whether to ignore `NA` values.  `sir`
#'   objects do not contain any `NA` values currently, so this argument is
#'   effectively ignored.
#' @param comp Character scalar. The component to calculate the quantile of.
#'   `NI` is infected agents, `NS` is susceptibles, `NR` stands
#'   for recovered.
#' @param prob Numeric vector of probabilities, in \[0,1\], they specify the
#'   quantiles to calculate.
#' @param \dots Additional arguments, ignored currently.
#' @return For `sir()` the results are returned in an object of class
#'   \sQuote{`sir`}, which is a list, with one element for each simulation.
#'   Each simulation is itself a list with the following elements. They are all
#'   numeric vectors, with equal length: \describe{
#'   \item{times}{The times of the events.}
#'   \item{NS}{The number of susceptibles in the population, over time.}
#'   \item{NI}{The number of infected individuals in the population, over
#'     time.}
#'   \item{NR}{The number of recovered individuals in the population, over
#'     time.}
#'   }
#'
#'   Function `time_bins()` returns a numeric vector, the middle or the
#'   boundaries of the time bins, depending on the `middle` argument.
#'
#'   `median` returns a list of three named numeric vectors, `NS`,
#'   `NI` and `NR`. The names within the vectors are created from the
#'   time bins.
#'
#'   `quantile` returns the same vector as `median` (but only one, the
#'   one requested) if only one quantile is requested. If multiple quantiles are
#'   requested, then a list of these vectors is returned, one for each quantile.
#' @author Gabor Csardi \email{csardi.gabor@@gmail.com}. Eric Kolaczyk
#' (<http://math.bu.edu/people/kolaczyk/>) wrote the initial version in R.
#' @seealso [plot.sir()] to conveniently plot the results
#' @references Bailey, Norman T. J. (1975). The mathematical theory of
#' infectious diseases and its applications (2nd ed.). London: Griffin.
#' @keywords graphs
#' @examples
#'
#' g <- sample_gnm(100, 100)
#' sm <- sir(g, beta = 5, gamma = 1)
#' plot(sm)
#' @family processes
#' @export
sir <- sir_impl