examples: Example scripts and user provided functions for the Gaussian...
In GaDiFPT: First Passage Time Simulation for Gaussian Diffusion Processes

For a generic Gaussian Diffusion process X(t) the drift can be written as a(t)*X(t)+b(t) and the infinitesimal variance as cc(t)^2. User should provide the functional form for a(t),b(t) and cc(t) in the main script. The threshold to be crossed has to be also provided through the function S(t) and its derivative Sp(t) in the same script. Examples of such a script are given for a Wiener process with or without drift through different thresholds (scripts Wiener, Wiener1, WienerDrift) for an Ornstein Uhlenbeck process also in presence of an additional current (scripts OrnUhl, OrnUhlCurrent) and for a more complex process with time-varying coefficients (Logistic).

Wiener.R

A. Buonocore, M.F. Carfora

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

# Wiener process through a periodic boundary: the process is built, its FPT pdf 
# evaluated by numerical quadrature and a train of crossing times is simulated. 
# Results are shown and saved to a file


library(GaDiFPT)

cat('#################################################################### \n')
cat('#####               First Passage Time Simulation              ##### \n')
cat('#####                   for the Wiener process                 ##### \n')
cat('#####                 through a periodic boundary              ##### \n')
cat('#################################################################### \n \n \n')

Wiener1 <- diffusion(c("mu","sigma2"))

mu <- 0.0
sigma2 <- 1.0

S0 <- 10
S1 <- 3
Sfr <- 0.005
Stype <- "periodic"

t0 <- 0.0
x0 <- 0.0
Tfin <- 1000    
deltat <- 0.5
N <- floor((Tfin - t0)/deltat)  
M <- 3000

quadflag <- 1
RStudioflag <- TRUE

param <- inputlist(mu,sigma2,Stype,t0,x0,Tfin,deltat,M,quadflag,RStudioflag)

fileout <- "results_Wiener1.out"

aaa <- function(t) {
  aaa <- 0.0 + 0.0*t
}

bbb <- function(t) {
 bbb <- mu + 0.0*t
}

SSS <- function(t) {
  SSS <- S0+S1*cos(Sfr*t)
}

SSSp <- function(t) {
  SSSp <- -S1*Sfr*sin(Sfr*t)
}

mp <- numeric(N+1)
up <- numeric(N+1)
vp <- numeric(N+1)
app <- numeric(N)

tempi <- seq(t0, by=deltat, length=N+1)

dum <- vectorsetup(param)
mp <- dum[,1]
up <- dum[,2]
vp <- dum[,3]

splot <- S(tempi)
mp1 <- mp - sqrt(2*sigma2)
mp2 <- mp + sqrt(2*sigma2)
matplot(tempi, cbind(mp,mp1,mp2,splot),type="l",lty=c(1,2,2,1),lwd=1,
        main="mean of the process vs. threshold",xlab="time(ms)",ylab="")
legend("bottomright",c("mean","threshold"),
       lty=c(1,1),col=c("black","blue"))


Nmax <- which.min(abs(mp[2:(N+1)]-mp[1:N]))

  N1 <- N
  if (quadflag == 0)   N1 <- max(c(Nmax,N/4))
  
  N1p1 <- N1+1

answer <- FPTdensity_byint(param,N1)
plot(answer)

spikes <- FPTsimul(answer,M)
histplot(spikes,answer)
  
res_summary(answer,M,fileout)