Hello, One approach would be to fit your distribution using MCMC with, for example, the rjags package. Then you can use the "zeroes trick" or "ones trick" to implement your new distribution as described here...
http://mathstat.helsinki.fi/openbugs/data/Docu/Tricks.html You will find a summary of Bayesian / MCMC packages here... http://cran.r-project.org/web/views/Bayesian.html Of these, rjags is the only one I've used directly so I can't comment on which would be easiest. Hopefully others here can offer advice. Michael On 5 November 2010 00:25, Roes Da <r0ez...@gmail.com> wrote: > hello,i'm roesda from indonesia > I have trouble when they have to perform parameter estimation by MLE method > using the R programming.because, the distribution that will be used instead > of not like the distribution that already known distributions such as gamma > distribution, Poisson or binomial. the distribution that i would estimate > the parameters are the joint distribution between the negative binomial > distribution and Lindley. how do I translate it in R if the distribution is > still new as I mentioned? i hope everyone can help me. thank you very much > Simak > Baca secara fonetik > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.