Simple illustration of getting maximum likelihood estimates of Wiener diffusion model (and profile likelihood confidence intervals) in R using RWiener and bbmle packages

RWiener.R

library("RWiener")
library("bbmle")

# alpha = boundary separation parameter.
# tau = non-decision time parameter.
# beta = bias parameter.
# delta = drift rate parameter
dat <- rwiener(200, alpha=2, tau=.3, beta=.5, delta=1)
str(dat)
wiener_plot(dat)

# wiener_plot adjusts margin options... set them back to default
par(mar=c(5,4,4,2)+.1)

# fit model
mll <- function(alpha, tau, beta, delta){
  -wiener_likelihood(x=c(alpha, tau, beta, delta), dat=dat)
}
system.time({
  mod <- mle2(mll, start=list(alpha=2, tau=.3, beta=.5, delta=1))
}) # 4.3 seconds


# inspect model
summary(mod)
system.time({
  prof <- profile(mod)
}) # 91 seconds
confint(prof)
plot(prof)