abikoushi

library(dplyr)
library(ggplot2)
library(patchwork)

get_NHPP <-function(Tmax, lambda, ...,
                    lambda2=NULL, maxit=10000){
  if(is.null(lambda2)){
    opt <- optimise(lambda, lower = 0, upper = Tmax, maximum = TRUE,
                    ...)
    lambda2 <- opt$objective

abikoushi

library(estatapi)
library(tidyverse)

myappId <- scan("appId.txt", what = character()) #ここには自分のアプリケーションIDを入れる
# list1 <- estat_getStatsList(appId = myappId, searchWord = "人口動態調査")
# view(list1)
dat1 <-estat_getStatsData(appId = myappId, statsDataId = "0003411856")


dat_filt <- dplyr::filter(dat1, `夫の初婚・再婚`=="夫_総数", `妻の初婚・再婚`=="妻_総数") |> 

abikoushi

library(ggplot2)
library(tidyr)
library(dplyr)
library(deSolve)

sir_ctmc <- function(S0, I0, R0,
                     beta, gamma,
                     t_max = 100) {
  
  # 初期状態

abikoushi

library(ggplot2)
library(tidyr)
library(dplyr)
library(deSolve)

sir_ctmc <- function(S0, I0, R0,
                     beta, gamma,
                     t_max = 100) {
  
  # 初期状態

abikoushi

library(ggplot2)

BirthDeathSim <-function(Ninit, mu, lambda, Tmax){
  N <- rpois(1, Tmax*(mu+lambda))
  CTs <- sort(runif(N, 0, Tmax))
  tmp <-sample(c(1,-1), N, prob=c(lambda/(mu+lambda),mu/(mu+lambda)), replace=TRUE)
  CTs <- c(0,CTs)
  path <-cumsum(c(Ninit,tmp))
  data.frame(time = c(CTs, Tmax), population = c(path, path[length(path)]))
}

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)