similar to: call nlm from a dll?

Linking my VC++ application with the standalone Rmath library yields the following; ------ Build started: Project: Complex plugin, Configuration: Debug Win32 ------ Linking... Creating library .\../Debug/complex_plugin.lib and object .\../Debug/complex_plugin.exp libRmath.a(mlutils.o) : warning LNK4217: locally defined symbol __iob imported in function _REprintf libRmath.a(dbeta.o) :

Hi, I am facing a problem which i think i need to explain it to you with some background. I need to use the Project R pnorm function in Visual Basic 6.0. I have already installed R and this is how i perform and get back the result: > pnorm(2, 15) [1] 6.117164e-39 which is what i need. I have already installed R, i generated the Rmath.DLL file out so i can import it into my VB6 and use it.

I currently have R 2.12.1 installed, both 32 and 64 bit. I also have a file that was passed to me named rmath.dll. I do not know what version of R it was created from, but I do know it is 32-bit only. I am developing an application in C# that uses this library as a reference but I have to downgrade it to 32-bit in order to use the DLL file. I wish to make a rmath.dll from the version of R I have

Dear R Community, I am trying to create a com dll wrapper for R in a mannor similiar to the R Com Server by Thomas Baier except that I would much rather avoid using the proxy dll ("Rproxy.dll"). Perhaps I don't really understand the reason for going throug the proxy in the first place, but it seems like an unnecessary indirection for an application which is not intended to use DCom.

Dear R Community, I am trying to create a com dll wrapper for R in a mannor similiar to the R Com Server by Thomas Baier except that I would much rather avoid using the proxy dll ("Rproxy.dll"). Perhaps I don't really understand the reason for going throug the proxy in the first place, but it seems like an unnecessary indirection for an application which is not intended to use DCom.

Hello, Has anybody successfully called the Rmath library from C using the MS Visual Studio compiler (I am using Visual Studio 6.0)? I have compiled the Rmath library using gcc, and the 'test.c' program (which makes a call to qnorm) works fine when compiled with gcc. However, I get a fatal memory error when I run it after compiling it with Visual C. Would this memory problem be related

  Hi Helpers. I am creating one wrapper VC++ dll for use some simple statistical functions like correlation/covariance for my college project. I have done the following:   I have gone thru R-exts.pdf u r mentioning. I have follwed the steps gievn under topic "The R api: entry points for C code". I have done these things 1) Created vc++ simple dll project. 2)inclued <R.h> in the

Hi, Is there any docs on R.dll? Or is it "use the source Luke" time? (The NT 0.63.1) Can anyone give me pointers on where to look? I am trying to wrap it under a GUI. The bdr release has examples, but it seems to be limited in scope. Has anyone compiled R under VC++. I see that the older version has been compiled under VC++ at some point. Is this best place to start to see how to

Hello: I am sure this question was dealt with several years ago. Is the function vmmin() available from Rmath Standalone? If not is it possible to call optim() or nlm() from Rmath in C. Thank you. Mervyn

I have two questions about using a user-defined random number generator (RNG) with the standalone Rmath library. The default RNG with the standalone Rmath library is the Marsaglia-multicarry generator, which has poor properties. The "R Installation and Administration" manual, in the section "The standalone Rmath library", states that: ``` A little care is needed to use the

Dear expeRts, first of all I'd like to thank you for the quick help on my last which() problem. Here is another one I could not tackle: I have data on an absorption measurement which I want to fit with an voigt profile: fn.1 <- function(p){ for (i1 in ilong){ ff <- f[i1] ex[i1] <- exp(S*n*L*voigt(u,v,ff,p[1],p[2],p[3])[[1]]) } sum((t-ex)^2) } out <-

Dear useRs, I am analysing the behaviour of MLE for the two parameters of a kind of exponential distribution, leaving as initial values the estimators moments produced by the variation coefficient. I do using simulations, giving them an accountant, r. But running my codes remains a problem with the nlm function. To review details wearing On one of the lines put status

Hi I am trying to use nlm with an additional call to nlm within the function but after the first pass, the parameters to the outer call are being passed to the inner call. The inner call is a very trivial problem. ie: test.outer<-function(param.outer){ slope<-nlm(test.inner,param.inner) ... loglikelihood<-sum(...) return(-loglikelihood) } and nlm(test.outer,param.outer) on the

Out of curiosity, I have tried, without success, to use the new facility in nlm to specify the gradient and hessian. (It is many years since I had a problem simple enough to make analytic derivation of these worthwhile.) The help now says that the function must have attributes with these names but gives no indication as to what should be in the attributes. The online example and demo do not use

In the course of revising a paper I have had occasion to attempt to maximize a rather complicated log likelihood using the function nlm(). This is at the demand of a referee who claims that this will work better than my proposed use of a home- grown implementation of the Levenberg-Marquardt algorithm. I have run into serious hiccups in attempting to apply nlm(). If I provide gradient and

Hello, I am trying to compute MLE for non-Gaussian AR(1). The error term follows a difference poisson distribution. This distribution has one parameter (vector[2]). So in total I want to estimate two parameters: the AR(1) paramter (vector[1]) and the distribution parameter. My function is the negative loglikelihood derived from a mixing operator. f=function(vector)

I am using nlm to maximize a likelihood function. When I call the likelihood function (garchLLH) via nlm however, nlm returns the wrong value of the function. When I test the likelihood function manually I get the correct answer. I'm probably doing something really stupid, maybe someone can point it out for me. ###############this is the function i am trying to minimize ############

<FONT face="Default Sans Serif, Verdana, Arial, Helvetica, sans-serif" size=2><DIV>Dear list members,</DIV><DIV>&nbsp;</DIV><DIV>I am trying to use "nlm" function to maximize a mixture likelihood of beta densities. There are five unknown parameters in the likelihood. Since I can get the analytic gradient, I attach the "gradient"

Dear R users, I am trying to find the mle that involves integration. I am using the following code and get an error when I use the nlm function d<-matrix(c(1,1,0,0,0,0,0,0,2,1,0,0,1,1,0,1,2,2,1,0),nrow=10,ncol=2) h<-matrix(runif(20,0,1),10) integ<-matrix(c(0),nrow=10, ncol=2) ll<-function(p){ for (k in 1:2){ for(s in 1:10){ integrand<-function(x)

Hi R-Gurus, I've been cutting along quite nicely with nlm, until I threw in the following condition in the function that nlm is minimising: if (((term*bexp) < 0.0001)) { #warning(term*bexp, "=term*bexp",psi,"=psi") theta<-2000 } Now when I run this function anywhere else, there is no problem, whether or the if's condition is met. When