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Hi, I am a PhD student interested in participating in GSoC09. Being compilers my area of research, LLVM had got my attention until I read this last post: http://lists.cs.uiuc.edu/pipermail/llvmdev/2009-March/021060.html I would like if one of the official LLVM Developers (http://llvm.org/developers.cgi) could confirm this. I understand if you are looking for students who can hit the ground

I have a function t.test.cluster for which R CMD check results in a documentation warning because its argument names don't match those of t( ). Is there a way around this error besides renaming the function? Will this disqualify a package from admission to CRAN? How did t.test avoid this warning? Thanks, Frank --- Frank E Harrell Jr Prof. of Biostatistics & Statistics

>>> I am wondering about the status of GSoc this year. The mailing list seems to be rather quiet on the topic. Has anyone thought of project ideas? >> We just received confirmation that LLVM was accepted for this year GSoC. >> The ideas list, as usual, is placed at >> http://llvm.org/OpenProjects.html (it also contains links to >> subproject's ideas pages).

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On Sep 8, 2015, at 12:26 PM, Renato Golin via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > On 8 September 2015 at 19:20, Tanya Lattner <tonic at nondot.org> wrote: >> Youtube has been discussed in the past but we have usually defaulted to >> mainly hosted on llvm.org and then a copy could potentially be on YouTube. I >> would want this to be an official

my models borrower ----- has_many :loans loan ----- belongs_to :borrower my loans _controller def new @borrower = Borrower.find(params[:borrower_id]) logger.debug '' @borrower.id'' logger.debug @borrower.id @loan = Loan.new respond_to do |format| format.html # new.html.erb format.xml { render :xml => @loan } end

Hello, I'm interested in correcting for and measuring unobserved heterogeneity ("missing variables") using R. In particular, I'm searching for a simple way to measure the amount of unobserved heterogeneity remaining in a series of increasingly complex models (adding additional variables to each new model) on the same data. I have a static database of 400,000 or

Would package "teigen" help? Ranjan On Thu, 29 Jun 2017 14:41:34 +0200 vare vare via R-help <r-help at 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

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Offlist, because this is (a) an opinion and (b) about statistics and therefore offtopic. I don't know whether any such package exists, but I would predict that this is likely to be overdetermined (too many parameters) and therefore unlikely to be a successful strategy. Fitting a mixture of Gaussians is already difficult enough. Feel free to ignore, of course, and no need to reply. Cheers,

I have a loans table and a payments table. Each loan has_many payments. So far no problem. Now, the payments table has a payment_number field, and each payment has a payment_number unique within that loan. For example, Loan A can have payment_numbers 0, 1, and 2 and Loan B can have payment_numbers 0, 1, 2, and 3 (but only one of each payment_number). I can enforce this with a unique index in

Thanks Ranjan, that provides exactly what I need. I also got a more elaborate answer on stackoverflow (after the same question got rejected form cross validated?) with a running example for this package: https://stackoverflow.com/questions/44825529/package-to-fit-mixtures-of-student-t-distributions/44827220#44827220

gamlss.mx can fit these I believe (although no experience with these myself) flexmix may be (relatively easily) adaptable to accomplish this as well hth, Ingmar Ingmar Visser Universitair Hoofddocent ontwikkelingspsychologie | Directeur College Psychologie Afdeling Psychologie | Faculteit Maatschappij- en Gedragswetenschappen | Universiteit van Amsterdam Bezoek | Nieuwe Achtergracht 129B | Kamer

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

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