Would package "teigen" help? Ranjan
On Thu, 29 Jun 2017 14:41:34 +0200 vare vare via R-help <r-help@r-project.org> wrote: > Hello! > > I am new to R (before used python exclusively and would actually call the R > solution for this issue inside a python notebook, hope that doesn’t > disqualify me right of the batch). > > Right now I am looking for a piece of software to fit a 1D data sample to a > mixture of t-distributions. > > I searched quite a while already and it seems to be that this is a somehwat > obscure endeavor as most search results turn up for mixture of gaussians > (what I am not interested here). > > The most promising candidates so far are the "AdMit" and "MitSEM" R packages. > However I do not know R and find the description of these packages rather > comlple and it seems their core objective is not the fitting of mixtures of > t’s but instead use this as a step to accomplish something else. > > This is in a nutshell what I want the software to accomplish: > > Fitting a mixture of t-distributions to some data and estimate the "location" > "scale" and "degrees of freedom" for each. > > I hope someone can point me to a simple package, I can’t believe that this is > such an obscure use case. > > Thanks! > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. -- Important Notice: This mailbox is ignored: e-mails are set to be deleted on receipt. Please respond to the mailing list if appropriate. For those needing to send personal or professional e-mail, please use appropriate addresses. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
