similar to: registering Fortran routines in R packages

Thanks for your email. I try to change the name in lowercase but it conflicts with a C implementation also named halton. So I rename the C function halton2() and sobol2() while the Fortran function are HALTON() and SOBOL() (I also try lower case in the Fortran code). Unfortunately, it does not help since I get init.c:97:25: error: use of undeclared identifier 'halton_'; did you mean

Have you tried using tools:::package_native_routine_registration_skeleton()? If you don't like its output, you can easily edit its results and still avoid most pitfalls. Cheers, Jari Oksanen ________________________________________ From: R-devel <r-devel-bounces at r-project.org> on behalf of Berend Hasselman <bhh at xs4all.nl> Sent: 10 May 2017 09:48 To: Christophe Dutang Cc:

> On 9 May 2017, at 13:44, Christophe Dutang <dutangc at gmail.com> wrote: > > Dear list, > > I?m trying to register Fortran routines in randtoolbox (in srt/init.c file), see https://r-forge.r-project.org/scm/viewvc.php/pkg/randtoolbox/src/init.c?view=markup&root=rmetrics. > > Reading

Hi all, I need to transform classic 32bit Fortran code to 64bit Fortran code, see the discussion [R-SIG-Mac] rnorm.halton. But I'm clearly a beginner in Fortran... Does someone already do this for his package? From here, http://techpubs.sgi.com/library/tpl/cgi-bin/getdoc.cgi?coll=linux&db=bks&fname=/SGI_Developer/Porting_Guide/ch03.html , I identify the following changes

Dear list useRs, I have to generate a random set of coordinates (x,y) in [-1 ; 1]^2 for say, N points. At each of these points is drawn a circle (later on, an ellipse) of random size, as in: > N <- 100 > > positions <- matrix(rnorm(2 * N, mean = 0 , sd= 0.5), nrow=N) > sizes<-rnorm(N, mean = 0 , sd= 1) > plot(positions,type="p",cex=sizes) My problem is to

In package fOptions, there are functions that generate Halton sequences. The van der Corput sequence for base 2 is a particular case of the Halton sequence generated by: n <- 8 # anything here... x <- runif.halton(n, 1) In fact, x <- runif.halton(n, dim) will generate the van der Corput sequences for the base b as the i-th prime number in x[,i]. (in other words, if I want the van der

Dear All, I have seen a number of e mails on this topic but I have not seen a general solution to date. I have Fortran and C source codes and they have been compiled successfully using: R CMD build mypackage And R CMD install mypackage Without error messages. I then open R and tests out two functions and get: > pgl(0.2,1,2,3,4) Error in

Dear All, I have seen a number of e mails on this topic but I have not seen a general solution to date. I have Fortran and C source codes and they have been compiled successfully using: R CMD build mypackage And R CMD install mypackage Without error messages. I then open R and tests out two functions and get: > pgl(0.2,1,2,3,4) Error in

I have had the following problem with R 2.10, 2.13.1, and 2.13.2, running on Ubuntu linux 10.04, xubuntu 11.10, and a version of Redhat (I think 5). rnorm.sobol is producing impossible random values, and occasionally the routine crashes. Here are samples of the output and the crash message. library(fOptions) Zs <- rnorm.sobol(50, dimension=1) produces this: [,1] [1,]

Having not much success with my previous question I try to reformulate it: I'm simulating a Markow chain in Fortran interfaced with R. Each loop of my Fortran calls various functions of the R RNG through the wrapper given below. In a run of 100 iterations of the Markov kernel, after 20 iterations, the RNG seems like frozen. For example, the first call to the RNG in my loop is:

I would like to call C routines from Fortran under linux as suggested in section 5.6 of the "Writing R extensions" documentation. I'm familiar with Fortran but not with C. I understand the example provided in Fortran: subroutine testit() double precision normrnd, x call rndstart() x = normrnd() call dblepr("X was", 5, x, 1) call rndend() end but I don't understand

I need to calculate the probability that a sample quantile will exceed a threshold given the size of the iid sample and the parameters describing the distribution of each observation (normal, in my case). I can compute the probability with brute force simulation: simulate a size N sample, apply R's quantile() function on it, compare it to the threshold, replicate this MANY times, and count the

Hi, I have some trouble with the result of a fortran function calling the R binomial RNG: The C function rbinom is wrapped as in the file attached. My main fortran program starts as call rndstart() and ends as call rndend() I happen to call the binomial RNG within a loop as b = ggrbinom(1.d0,0.5d0) write(*,*) 'b=',b In certain cases, after a few iterations in the loop, b get

Hi, I'm currently R porting SF Mersenne Twister algorithm of Matsumoto and Saito. To get the full power of their code, I want to use their fonction fill_array32 which need aligned memory. That is to say I need to use the C function memalign on windows, posix_memalign on linux and classic malloc on Mac OS. In 'writing R extenstion', they recommand to use R_alloc function to

Hi, I'm currently R porting SF Mersenne Twister algorithm of Matsumoto and Saito. To get the full power of their code, I want to use their fonction fill_array32 which need aligned memory. That is to say I need to use the C function memalign on windows, posix_memalign on linux and classic malloc on Mac OS. In 'writing R extenstion', they recommand to use R_alloc function to

Dear Listers; Does anybody has experience in doing simulation via Qusi-Monte carlo in R or S-plus, if so, could you like to send a small copy of your program to me, I appreciate and thanks in advance!! Frankly speaking, I am struggling to write this kind of program, while I could not figure out, painful!!!!! Best regards, Tony --------------------------------- [[alternative HTML

Hi all, I'm currently implementing quasi random generation (torus) on R (package randtoolbox available on CRAN). Even if it is not a great idea, I decided to use the machine time to initiate the seed. So when the seed is not specified by the user, the pkg uses the time machine time. Hence the following R code should produce different uniform variates : > for(i in

Is there any way to _test_ if a given sequence is a low-discrepancy sequence? Or, equivalently, is there any way to measure the "discrepancy" of a sequence? WTIW, ... http://en.wikipedia.org/wiki/Low-discrepancy_sequence ... doesn't look like something implementation-friendly :-) Alberto Monteiro PS: low discrepany sequences are easily generated in R by the functions

I would like to call C routines from Fortran as suggested in section 5.6 of the "Writing R extensions" documentation. I'm familiar with Fortran but not with C. I understand the example provided in Fortran: subroutine testit() double precision normrnd, x call rndstart() x = normrnd() call dblepr("X was", 5, x, 1) call rndend() end but I don't understand the purpose

I've recently updated the coxme package, which calls internal routines from the bdsmatrix package.? (It is in fact mentioned as an example of this in the Extensions manual.) The call connections are a blocks like this, one for each of the 9 called C routines. void bdsmatrix_prod4(int nrow,??? int nblock,?? int *bsize, ??????????????????? double *bmat, double *rmat, ??????????????????? int