dirichlet.mle(x, weights=NULL, eps=10^(-5), convcrit=1e-05, maxit=1000,
     oldfac=.3, progress=FALSE)

#############################################################################
# EXAMPLE 1: Simulate and estimate Dirichlet distribution
#############################################################################

# (1) simulate data
set.seed(789)
N <- 200
probs <- c(.5, .3, .2 )
alpha0 <- .5
alpha <- alpha0*probs
alpha <- matrix( alpha, nrow=N, ncol=length(alpha), byrow=TRUE  )
x <- sirt::dirichlet.simul( alpha )

# (2) estimate Dirichlet parameters
dirichlet.mle(x)
  ##   $alpha
  ##   [1] 0.24507708 0.14470944 0.09590745
  ##   $alpha0
  ##   [1] 0.485694
  ##   $xsi
  ##   [1] 0.5045916 0.2979437 0.1974648

## Not run: 
#############################################################################
# EXAMPLE 2: Fitting Dirichlet distribution with frequency weights
#############################################################################

# define observed data
x <- scan( nlines=1)
    1 0   0 1   .5 .5
x <- matrix( x, nrow=3, ncol=2, byrow=TRUE)

# transform observations x into (0,1)
eps <- .01
x <- ( x + eps ) / ( 1 + 2 * eps )

# compare results with likelihood fitting package maxLik
miceadds::library_install("maxLik")
# define likelihood function
dirichlet.ll <- function(param) {
    ll <- sum( weights * log( ddirichlet( x, param ) ) )
    ll
}

#*** weights 10-10-1
weights <- c(10, 10, 1 )
mod1a <- sirt::dirichlet.mle( x, weights=weights )
mod1a
# estimation in maxLik
mod1b <- maxLik::maxLik(loglik, start=c(.5,.5))
print( mod1b )
coef( mod1b )

#*** weights 10-10-10
weights <- c(10, 10, 10 )
mod2a <- sirt::dirichlet.mle( x, weights=weights )
mod2a
# estimation in maxLik
mod2b <- maxLik::maxLik(loglik, start=c(.5,.5))
print( mod2b )
coef( mod2b )

#*** weights 30-10-2
weights <- c(30, 10, 2 )
mod3a <- sirt::dirichlet.mle( x, weights=weights )
mod3a
# estimation in maxLik
mod3b <- maxLik::maxLik(loglik, start=c(.25,.25))
print( mod3b )
coef( mod3b )

## End(Not run)