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

Hello! I have a program in Fortran and would like to build a matrix with random numbers, I have a function in C. However, I have problems with the use of function in R. Code to compile: R CMD SHLIB mat.f myrbeta.c -o func.so Code in C. #include <R.h> #include <Rmath.h> void F77_SUB(fseedi)(void){   GetRNGstate(); } void F77_SUB(fseedo)(void){   PutRNGstate(); } void