abikoushi

makeLmat <- function (h){
    x = seq(0,1,by=h)
    N=length(x)
    invh2 <- 1/(h^2)
    res = diag(invh2, N)
    res[cbind(1:(N-1),2:N)] <- -0.5*invh2
    res[cbind(2:N,1:(N-1))] <- -0.5*invh2
    return(list(L=res,x=x))
}

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library(ggplot2)
library(dplyr)
library(mvtnorm)
set.seed(1)
x1 = rnorm(10, 0, 1)
x2 = x1+rnorm(10, 0, 0.1)
y = x1+x2 + rnorm(10, 0, 1)

print(cor(x1,x2))

abikoushi

library(deSolve)

SEIRmod <- function(Time, State, Pars) {
  with(as.list(c(State, Pars)), {
    dS <- - beta*I*S
    dE <- beta*I*S - alpha*E
    dI <- alpha*E - gamma*I
    dR <- gamma*I
    return(list(c(dS, dE, dI, dR)))
  })

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# Reference:
# Hoffman et al. "Stochastic Variational Inference" 
# browseURL("https://arxiv.org/abs/1206.7051")
####

# learning rate
lr <-function(t, lr_param){
  (t + lr_param[1])^(-lr_param[2])
}

abikoushi

#intensity function
lambda <- function(x, shape){
  shape*x^(shape-1)
}

#number of truncated observation
counttrunc <- function(tau, WS, weibull_param,
                       shape=1, maxit=10000){
  Et = 0
  count = 0L

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#install.packages('RcppPlanc', repos = c('https://welch-lab.r-universe.dev', 'https://cloud.r-project.org'))
library(rliger)
library(RcppPlanc)
library(bench)
pbmc <- normalize(pbmc)
pbmc <- selectGenes(pbmc)
pbmc <- scaleNotCenter(pbmc)
getOption("ligerVerbose", TRUE)

bm <-  bench::mark({

abikoushi

library(animation)

make_initial <- function(np, S){
  X = rep(S/np, np)
  return(X)
}

randomwalk <- function(X){
  np = length(X) 
  i = sample.int(np, 1)

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#参考文献：永井佑紀『1週間で学べる！Julia数値計算プログラミング』（講談社）
library(animation)
library(reshape2)

make_initial <- function(n_people,n_chip){
  basket = integer(n_people)
  for(i in 1:n_chip){
    target = sample.int(n_people, 1)
    basket[target] = basket[target] + 1
  }

abikoushi

library(pdftools)
library(tidyr)
library(dplyr)
library(ggplot2)
browseURL("https://www.jstage.jst.go.jp/article/jph/50/8/50_686/_article/-char/ja/")
text = pdf_text("~/Downloads/50_686.pdf")

tab2raw = unlist(strsplit(text[grep("表２", text)],"  "))
tab2raw = tab2raw[sapply(tab2raw, nchar) > 0]
tab2raw = tab2raw[16:145]

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library(animation)

sim_pois_1d <- function(t_n, g_n, p, seed){
  set.seed(seed)
  counts <- integer(t_n)
  x <- seq(0, 1, length.out=g_n)
  delta_list <- vector("list", t_n)
  hit <- matrix(0, t_n, g_n)
  pud = 10
  for(i in 1:t_n){