abikoushi

library(ggplot2)
library(dplyr)

simulate_branching_death <- function(alpha,
                                     beta,
                                     Tmax) {
  death_time <- rexp(1,beta)
  death <- min(Tmax, death_time)
  N0 <- rpois(1, death * alpha)
  

abikoushi

library(RHawkes)

simulate_branching_death <- function(mu,
                                     alpha,
                                     beta,
                                     Tmax) {
  
  N0 <- rpois(1, Tmax * mu)
  if(N0==0L){
    return(

abikoushi

library(dplyr)
library(ggplot2)
simulate_branching_death <- function(mu,
                                     lambda,
                                     beta,
                                     Tmax) {
  
  N0 <- rpois(1, Tmax * mu)
  
  t0 <- sort(runif(N0, 0, Tmax))

abikoushi

library(ggplot2)
library(dplyr)

branching_process <- function(mu, lambda, Tmax, G){
  gen <- vector("list", G)
  X <- rpois(1, mu*Tmax) #第1世代
  if(X>0){
    id <- 1L:X
    parent <- 0L
    birth_time <- sort(runif(X, 0, Tmax))

abikoushi

library(dplyr)
library(ggplot2)

simulate_multitype_gillespie <- function(
    R,
    Tmax,
    initial_type = 1L
) {
  
  K <- nrow(R)

abikoushi

library(hawkes)
mu <- 0.5
a  <- 0.7
b  <- 1.0
Tau <- 100
set.seed(12345)
dat <- simulateHawkes(
  lambda0 = mu,
  alpha = a*b,
  beta = b,

abikoushi

library(ggplot2)
library(dplyr)
multitype_branching_process <- function(M, G, initial_type = 1L) {
  # M[i, j] = type i の個体1個体あたりの
  #          type j の平均子孫数
  K <- nrow(M)
  if (ncol(M) != K) {
    stop("M must be a square matrix.")
  }
  if (any(M < 0)) {

abikoushi

library(dplyr)
library(ggplot2)

simulate_branching <- function(mu,
                               lambda,
                               Tmax) {
  
  N0 <- rpois(1, Tmax*mu)
  t <- sort(runif(N0, 0, Tmax))
  individuals <- data.frame(

abikoushi

library(dplyr)
library(ggplot2)
###
branching_process <- function(theta, gamma, G){
  gen <- vector("list", G)
  K <- rpois(1, theta)
  id <- 1L:K
  parent <- 1L
  gen[[1]] <- data.frame(id = id,
                         parent = parent,

abikoushi

iter_nextstep <- function(n, K){
  I <- c(1, 0)
  I_hist <- matrix(0, nrow = 2, ncol = n+1)
  I_hist[, 1] <- I
  for(i in 1:n){
    I <- K%*%I
    I_hist[,i+1] <- I  
  }
  return(t(I_hist))  
}