maximum likelihood sstimation of beta-normal in R

I want to estimate the parameters of beta-normal distribution.I've used the maxLik package in r: library(VGAM) library(maxLik) alfa=2;beta=3;mu=0;sigma=1 n=100 x=rbetanorm(n,alfa,beta,mu,sigma) logLikFun=function(w){ alfa=w[1] beta=w[2] mu=w[3] sigma=w[4] ll={-n*log(beta(alfa,beta))+(alfa-1)*sum(log(pnorm((x-mu)/sigma,mean=0,sd=1)))+(beta-1)*sum(log(1-pnorm((x-mu)/sigma,mean=0,sd=1)))-n*log(sigma)+sum(log(dnorm((x-mu)/sigma,mean=0,sd=1)))} ll } mle=maxLik(logLikFun,start=c(alfa=3,beta=2,mu=1,sigma=2)) summary(mle) i want to estimate the parameters of beta-normal distribution.I've used the maxLik package library(VGAM) library(maxLik) alfa=2;beta=3;mu=0;sigma=1 n=100 x=rbetanorm(n,alfa,beta,mu,sigma) logLikFun=function(w){ alfa=w[1] beta=w[2] mu=w[3] sigma=w[4] ll={-n*log(beta(alfa,beta))+(alfa-1)*sum(log(pnorm((x-mu)/sigma,mean=0,sd=1)))+(beta-1)*sum(log(1-pnorm((x-mu)/sigma,mean=0,sd=1)))-n*log(sigma)+sum(log(dnorm((x-mu)/sigma,mean=0,sd=1)))} ll } mle=maxLik(logLikFun,start=c(alfa=3,beta=2,mu=1,sigma=2)) summary(mle) but it gives error: ---------------------------------- Maximum Likelihood estimation Newton-Raphson maximisation, 4 iterations Return code 2: successive function values within tolerance limit Log-Likelihood: -86.16515 4 free parameters Estimates: Estimate Std. error t value Pr(> t) alfa 3.000 Inf 0 1 beta 2.941 Inf 0 1 mu 1.000 Inf 0 1 sigma 2.000 Inf 0 1 -------------------------------------------- the problem is the infinite value for the errors which is not acceptable. I would be pleased if someone could solve this problem.