library('mfx')

### Name: probitmfx
### Title: Marginal effects for a probit regression.
### Aliases: probitmfx print.probitmfx

### ** Examples

# simulate some data
set.seed(12345)
n = 1000
x = rnorm(n)

# binary outcome
y = ifelse(pnorm(1 + 0.5*x + rnorm(n))>0.5, 1, 0)

data = data.frame(y,x)
probitmfx(formula=y~x, data=data)