similar to: precision of rnorm

On 30 August 2016 at 18:29, Duncan Murdoch wrote: | I don't see evidence of a bug. There have been several versions of the | MT; we may be using a different version than you are. Ours is the | 1999/10/28 version; the web page you cite uses one from 2002. | | Perhaps the newer version fixes some problems, and then it would be | worth considering a change. But changing the default RNG

Dear List, Recently I got the comment that the implementation of the random number generator used by default in R (Mersenne-Twister) could not be "safe" for 64-bits machines, so I decided to put the question here because I do not have expertise in that topic, and because this question could be "too technical for R-help's audience". I apologise if this is not the case.

Whomever, I recently sent the "bug report" below toR-core at r-project.org and have just been asked to instead submit it to you. Although I am basically not an R user, I have installed version 3.3.1 and am also the author of a statistics program written in Visual Basic that contains a component which correctly implements the Mersenne Twister (MT) algorithm. I believe that it is

Dear R experts, we are preparing an R-package to compute the Oja Median which contains some C++ code in which random numbers are needed. To generate the random numbers we use the following Mersenne-Twister implementation: // MersenneTwister.h // Mersenne Twister random number generator -- a C++ class MTRand // Based on code by Makoto Matsumoto, Takuji Nishimura, and Shawn Cokus // Richard J.

On 08/30/2016 06:29 PM, Duncan Murdoch wrote: > I don't see evidence of a bug. There have been several versions of the > MT; we may be using a different version than you are. Ours is the > 1999/10/28 version; the web page you cite uses one from 2002. > > Perhaps the newer version fixes some problems, and then it would be > worth considering a change. But changing the

Gabe As mentioned on Twitter, I think the following behavior should be fixed as part of the upcoming changes: R.version.string ## [1] "R Under development (unstable) (2019-02-25 r76160)" .Machine$double.digits ## [1] 53 set.seed(123) RNGkind() ## [1] "Mersenne-Twister" "Inversion"??????? "Rejection" length(table(runif(1e6))) ## [1] 999863 I don't

Hi, It seems to me that the Mersenne-Twister PRNG can be initialised using one integer instead of 624 integers, since inside RNG.c code there's a function defined as MT_sgenrand(Int32). How do I actually set this seed within R? I've tried: > .Random.seed <- c(3, 1) > runif(1) Error in runif(1) : .Random.seed has wrong length In addition, is '3' actually the

Ralf I don't doubt this is expected with the current implementation, I doubt the implementation is desirable. Suggesting to turn this to pbirthday(1e6, classes = 2^53) ## [1] 5.550956e-05 (which is still non-zero, but much less likely to cause confusion.) Best regards Kirill On 26.02.19 10:18, Ralf Stubner wrote: > Kirill, > > I think some level of collision is actually

Full_Name: Wolfgang Huber Version: 1.8.0 OS: Linux Submission from: (NULL) (193.174.58.146) The man page for RNGkind says that the default is Mersenne-Twister, and when I start R interactively, I get in fact > RNGkind() [1] "Mersenne-Twister" "Inversion" However, during the execution of "R CMD check" I get > > ### ** Examples > > > > RNGkind()

Dear all, Recently I am generating large random samples (10M) and any duplicated numbers are not desired. We tried several RNGs in R and found Wichmann-Hill did not produce duplications. The duplication problem is the interesting birthday problem. If there are M possible numbers, randomly draw N numbers from them, the average number of dupilcations D = N(N-1)/2/M. For Knuth-TAOCP and

Dear All, The editor of a journal to which I had submitted a publication asked whether R has a "tested random number generator." My paper included Monte Carlo simulations generating random normal and random chi-square values. help(rnorm) lists Wichura, M. J. (1988) Algorithm AS 241: The Percentage Points of the Normal Distribution. Applied Statistics, 37, 477-484. as a

Dear list, For the implementation of a particular optimization algorithm it is very important the random number generator. I would like to know if somebody could tell me what is the random number generator used by default in the 'runif' function. >From the help page of 'runif' and '.Random.seed' I guess that the default algorithm is 'Mersenne-Twister', but I

I sent this to R-devel early last month, but have received no response, so I guess it really is a bug. This looks like a bug to me, and is a bit hard to describe, but easy to reproduce. ? Basically, if RNGkind is saved as something other than the default, and if the first operation in a session is a set.seed(), the default is reverted to. ?Reproduce by: cafe-rozo> ?R --vanilla R version

Dear list, I have noticed surprisingly big performance differences of runif() between Windows XP and (Debian) linux on similar CPUs (Pentium D 3.0GHz (WinXP)/3.2GHz (Linux)) and I wonder if there is a simple explanation for the difference. On a linux system (with a slightly better CPU and 1GB more RAM), execution of runif() seems to consume about 80% more CPU time than on a Windows XP

Hi, subject more or less says it all. I freely admit to not having bothered to find some of the online papers about method of testing the quality of random number generators -- but in an idle moment I wondered what to expect from something like the following: randa<-runif(1000) randb<-runif(1000) t.test(randa,randb)$p.value var.test(randa,randb)$p.value [repeat ad nauseum] Is the

Martin, Thanks for the helpful reply. Alas I had forgotten that (implied) unfavorable comparisons of *nix systems with Windows systems would likely draw irate (but always substantive) responses on the R-devel list -- poor phrasing on my part. :) Regardless, let me try to address some of the concerns related to the construction of the MRE itself and try to see if we can clean away the shrubbery

Dear Sir/Madam, I am a beginner in R. Here is my questions. 1. Can you give me one test for randomness (a name and descriptive paragraph is sufficient). 2. I have learned a uniform random number generator [e.g. not the algorithms: i)Wichmann-Hill, ii) Marsaglia-Multicarry, iii) Super-Duper (Marsaglia), iv) Mersenne-Twister, v) TAOCP-1997 (Knuth), or vi) TAOCP-2002 (Knuth)] . Is there any other

Hi All. I have R code whose functionality is being replicated within a C+ program. The outputs are to be compared to validate the conversion somewhat - however (as is always the case) I have stuffed my code with random number calls. Random uniform numbers in C+ are being produced using the (Boost) mersenne-twister generators (mt11213b & mt19937) - which is the default type of generator

A few simple questions from a novice R user. (I am running R version 0.62.3 Beta (Sept 8, 1998) under Windows NT.) 1) How do I time the execution of a function/program in R for Windows? Is there the equivalent of a dos.time function? 2) Can anyone send me details on the random number generator used by R (period, etc.). Also, why is it different from the SuperDuper RNG in S? 3) Will there

Mersenne Twister generator is known to be sensitive to the algorithm used to generate its initial state. The initialization used in R generates the initial state in a way, which leaves linear dependencies mod 2 among the bits in the initial state. Since Mersenne Twister performs only operations, which are linear mod 2, these dependencies propagate to the output sequence. An easy to see