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...tools) to fit them in finite mixture narmal distributions,but get some mistakes.I don't know which steps make the error. I have used the following functions before >x<-read.table("data") >log.x<-log10(x$V1) >log.x<-sort(log.x) >outx = normalmixEM(log.x,arbmean = TRUE, arbvar = TRUE, k=2,epsilon = 1e-03,maxit = 10000, verb = FALSE) The output is given as: Need new starting values due to singularity... Need new starting values due to singularity... Need new starting values due to singularity... error in normalmixEM(log.x, arbmean = TRUE, arbv...

...(X,100,freq=FALSE, ylim=c(0,1.5))lines(density(x.1), lty=2, lwd=2)lines(density(x.2), lty=2, lwd=2)lines(density(X), lty=4) Currently i am using mixtools and just run: library(mixtools)mixmdl = normalmixEM(X, k=2, epsilon = 1e-08, maxit = 1000, maxrestarts=20, verb = TRUE, fast=FALSE, ECM = FALSE, arbmean = TRUE, arbvar = TRUE) plot(mixmdl,which=2)lines(density(X), lty=2, lwd=2) This is obviously not the best way of doing this. The estimates it gives me are:mu 3.6595737(x.2 log()=1.29) 2.3113135(x.1) These are not too far off but I was wondering if someone knows of a better R package/way of doing th...