similar to: Rmpfr: build vector sequentially -- c(.) not working

Hi: I'm trying to reproduce an arbitrary precision constant from 'Why and How to Use Arbitrary Precision' (Ghazi et al., COMPUTING IN SCIENCE & ENGINEERING May/June 2010; http://perso.ens-lyon.fr/philippe.theveny/cise.pdf): d = 173746a + 94228b ? 78487c where: a = sin(1022), b = log(17.1), and c = exp(0.42). Ghazi et al. report: d = ?1.341818958e?12 whit IEEE-754 quadruple

I have been asked (by Roger; thank you for the good question, and I hope it's fine to answer to the public) : > with Pi a sparse matrix and x,y, and ones > compatible n-vectors ? when I do: >> c(t(x) %*% Pi %*% ones, t(ones) %*% Pi %*% y ) > [[1]] 1 x 1 Matrix of class "dgeMatrix" > [,1] [1,] > 0.1338527 >

>>>>> John Chambers <jmc at r-project.org> >>>>> on Sat, 10 Sep 2016 09:16:38 -0700 writes: > (Brief reply, I'm traveling but as per below, this is on my radar right now so wanted to comment.) > Two points regarding "dotsMethods". > 1. To clarify the limitation. It's not that all the arguments have to be of the same

Thank you Martin for giving to know and developing 'Rmpfr' library for unlimited size integers (GNU C GMP) and arbitrary precision floats (GNU C MPFR): https://cran.r-project.org/package=Rmpfr My question is: In the long term (For R3.7.0 or R3.8.0): Does it have sense that CMP substitutes INTSXP, and MPFR substitutes REALSXP code? With this we would achieve that an integer is always an

Hello everyone, R beginner, I am confronted with the need to use Rmpf. In my first scripts I made use of X=read.table(file.choose(), header=FALSE, sep=",",dec=".") X=as.matrix(X) to load into a matrix data from file before matrix use. How can I do to load the same data in a "mpfrMatrix". Is it possible to use with "mpfrMatrix" the same as operations

All, Martin: Thanks for this and all the other things you are doing to both drive R forward and engage more with the community about things like this. Apologies for missing this discussion the first time it came around and if anything here has already been brought up, but I wonder what exactly you mean when you want recycling behavior. Specifically, based on an unrelated discussion with Henrik

Dear R-devel readers, ( = people interested in the improvement and development of R). This is not the first time that this topic is raised. and I am in now state to promise that anything will result from this thread ... Still, I think the majority among us has agreed that 1) you should never use ifelse(test, yes, no) if you know that length(test) == 1, in which case if(test) yes

(Brief reply, I'm traveling but as per below, this is on my radar right now so wanted to comment.) Two points regarding "dotsMethods". 1. To clarify the limitation. It's not that all the arguments have to be of the same class, but there must be one class that they belong to or subclass. (So, as in the example in the documentation, the method could be defined for a class

Dear Team, Maybe somebody already tried to install mpfr and gsl packages for R on SLES. Exactly I try to install Rmpfr_0.6-1.tar.gz and gsl_1.9-10.3.tar.gz on SLES11 SP4. These are the available packages from official SLES SDK media: rtest:/home/ruser # rpm -qa | grep -i gsl gsl-devel-1.11-1.30 gsl-1.11-1.30 rtest:/home/ruser # As I understand gsl is from 1.11 till 1.30 level.

Just stating, in 'ifelse', 'test' is not recycled. As I said in "R-intro: length of 'ifelse' result" (https://stat.ethz.ch/pipermail/r-devel/2016-September/073136.html), ifelse(condition, a, b) returns a vector of the length of 'condition', even if 'a' or 'b' is longer. On current 'ifelse' code in R: * The part ans[nas] <- NA

>>>>> G?bor Cs?rdi <csardi.gabor at gmail.com> >>>>> on Wed, 29 Apr 2015 23:07:09 -0400 writes: > On Wed, Apr 29, 2015 at 8:12 PM, Paul Gilbert <pgilbert902 at gmail.com> wrote: >> >> As I recall, several packages mask the simulate generic in stats, if you >> are looking for examples. >> > FWIW,

Dear all, Thanks for Baptiste's help. He offered me the website http://win-builder.r-project.org/ This is very good. However, I face another problem recently that I need to program a package, which require 'multiple-precision floating-point computations'. I found that MPFR is very good tools. It can be installed on the linux system, if you want to use it just include some head

As a side note, Splus makes sin(x) NA, with a warning, for abs(x)>1.6*2^48 (about 4.51e+14) because more than half the digits are incorrect in sin(x) for such x. E.g., in R we get: > options(digits=16) > library(Rmpfr) > sin(4.6e14) [1] -0.792253849684354 > sin(mpfr(4.6e14, precBits=500)) 1 'mpfr' number of precision 500 bits [1]

On Wed, Apr 29, 2015 at 8:12 PM, Paul Gilbert <pgilbert902 at gmail.com> wrote: > > As I recall, several packages mask the simulate generic in stats, if you > are looking for examples. > FWIW, here is a list of base* functions masked** by CRAN packages: https://github.com/gaborcsardi/rfunctions/blob/master/rfunctions.md Look at the long table in the end. simulate indeed comes up

Thank you Gabriel for valuable insights on the 64-bit integers topic. In addition, my statement was wrong, as Python3 seems to have unlimited (and variable) size integers. Here is related CPython Code: https://github.com/python/cpython/blob/master/Objects/longobject.c Division between Int-32 and Int-64 seems to only happen in Python2. Best, Juan El mi?rcoles, 29 de mayo de 2019, Gabriel

I agree with many the sentiments about the wisdom of computing very small p-values (although the example below may win some kind of a prize: I've seen people talking about p-values of the order of 10^(-2000), but never 10^(-(10^8)) !). That said, there are a several tricks for getting more reasonable sums of very small probabilities. The first is to scale the p-values by dividing the

Le 18/02/2023 ? 21:44, J C Nash a ?crit?: > I wrote first cut at unirootR for Martin M and he revised and put in > Rmpfr. > > The following extends Ben's example, but adds the unirootR with trace > output. > > c1 <- 4469.822 > c2 <- 572.3413 > f <- function(x) { c1/x - c2/(1-x) }; uniroot(f, c(1e-6, 1)) > uniroot(f, c(1e-6, 1)) > library(Rmpfr) >

Hi, I'm working on an S4 class that is expected to behave like an array. I have some difficulties when defining '[' and I wonder if someone could point me to the right direction: 1) Call the S4 object "obj" 2) Assume dim(obj) = c(10, 4, 2) 3) Suppose someone calls: obj[1:3,] , which is a mistake, given dim(obj); how do I detect such situations? Thank you very much for

>>>>> Juan Telleria Ruiz de Aguirre >>>>> on Mon, 3 Jun 2019 06:50:17 +0200 writes: > Thank you Martin for giving to know and developing 'Rmpfr' library for > unlimited size integers (GNU C GMP) and arbitrary precision floats (GNU C > MPFR): > https://cran.r-project.org/package=Rmpfr > My question is: In the long term

On Fri, Aug 12, 2016 at 11:31 AM, Hadley Wickham <h.wickham at gmail.com> wrote: >> >> One possibility would also be to consider a "numbers-only" or >> >> rather "same type"-only {e.g., would also work for characters} >> >> version. >> >> > I don't know what you mean by these. >> >> In the