similar to: Normal deviate generation - Marsaglia's ziggurat method

Hello everyone. If I do f <- function(n){max(rnorm(n))} plot(sapply(rep(5000,4000),f)) #[this takes my PC about 30 seconds] then I get something quite unexpected: gaps in the distribution. For me, the most noticable one is at about 3.6. Do others get this? Is it an optical illusion? It can't be right, can it? Or maybe I just don't understand the good ol' Gaussian very

Dear R-developer (Marsaglia??) The following piece of code from package SuppDist , routine "dist.cc" seems to have a bug ULONG MWC1019(void){ ULONG long t; int i = endQ-1; t = 147669672LL*Q[i] + Q[endQ]; Q[endQ] = (t>>32); if(i>0) return(Q[i--] = t); i = endQ-1; return(Q[0] = t); } in fact , being "i" a local variable that have automatic storage, it is

On r-help Bill Simpson wrote: >Marsaglia's multiply-with-carry generator is very easy to implement--he >has posted some C code to newsgroups--and seems to test out OK. I wonder >if it would make sense to use one of his generators instead. I would like a system where the random number generator can be specified (perhaps in options or a .RandomGenerator variable). Most often everyone

Full_Name: Richard Simard Version: OS: Linux Submission from: (NULL) (132.204.25.139) In the random number generator MARSAGLIA_MULTICARRY, your algorithm is different than the one in the original reference that you give in the R manual: Marsaglia in his post to the mailing list {\it sci.stat.math} on September 29, 1997. The last line in the R program has a ^ while Marsaglia's algorithm has

SuppDists is orphaned on CRAN (and has been since 2013). https://cran.r-project.org/web/checks/check_results_.html Oddly, the simulate method for the inverse.gaussian family [inverse.gaussian()$simulate] depends (in a loose sense) on SuppDists (it fails if the SuppDists namespace is not available: if (!requireNamespace("SuppDists", quietly = TRUE)) stop("need CRAN

On 2019-09-25 3:26 a.m., Martin Maechler wrote: >>>>>> Ben Bolker >>>>>> on Tue, 24 Sep 2019 20:09:55 -0400 writes: > > > SuppDists is orphaned on CRAN (and has been since 2013). > > https://cran.r-project.org/web/checks/check_results_.html > > > Oddly, the simulate method for the inverse.gaussian family > >

Ah, I spoke too soon. I started putting the demo code into a test suite and ran one check with valgrind and ? sure enough ? there's def more issues (a cpl functions) than the overt/easy ones (and, I went back to the check results page and, also sure enough, they're there, too). They look to be fairly straightforward to resolve but it's going to take a bit longer than "this

Hello I am searching ghyper package (generalized hypergeometric distributions). Does anyone can send it to me? Regards from Mexico Lizbeth Román [[alternative HTML version deleted]]

Dear list, I would like to suggest a small update the ?Distributions man page of the stats package. The current version contains the following line. The CRAN package \pkg{SuppDists} for additional distributions. I think it would be better to put in this man page a link to the CRAN task view on Distributions http://cran.r-project.org/web/views/Distributions.html. It is not true that the

cor.test and SuppDists give me different P-values for the same Spearman's rho. Which is correct, or am I doing something wrong? > x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1) > y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8) > cor.test(x,y,method="spearman") Spearman's rank correlation rho data: x and y S = 48, p-value =

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

Hi, I used the commands below to make Hartley's table, but some values are NA. require(SuppDists) trat = seq(2, 15, 1) gl = seq(2, 40, 1) har = matrix(0, nr=length(gl), nc=length(trat)) for(i in 1:length(gl)) for(j in 1:length(trat)) har[i,j] <- qmaxFratio(.95, df=gl[i], k=trat[j]) rownames(har) <- gl colnames(har) <- trat head(har) The output (head): 2

Hello! I am trying with this question again: I would like to test few distributional assumptions for some behavioral response data. There are few theories about true distribution of those data, like: normal, lognormal, gamma, ex-Gaussian (exponential-Gaussian), Wald (inverse Gaussian) etc. The best way would be via qq-plot, to show to students differences. First two are trivial: qqnorm(dat$X)

I think I should have posted this question here as well. I am posting my question here since it is R related. Please see below. I originally posted this to sci.stat.math "Nasser Abbasi" <nma at 12000.org> wrote in message news:Mm4gj.28843$R92.4987 at newsfe16.phx... > > I think R documentation is a bit hard for me to sort out at this time. > > I was wondering if

Hi, I am very new to R. Just started yesterday. I have to generate a sequence of 40 random treatments using permuted block randomization with a block size of 8. Then I have to plot moving averages for the resulting sequence. I have tried the sample function but I dont know if wht i did is right or not > sample(c(1,1,1,1,2,2,2,2),8) Any ideas please? -- Ayesha Khan MS Bioinformatics Dept. of

I have found the "simulate" method (incorporated in some packages) very handy. As far as I can tell the only class for which simulate is actually implemented in base R is lm ... this is actually a little dangerous for a naive user who might be tempted to try simulate(X) where X is a glm fit instead, because it defaults to simulate.lm (since glm inherits from the lm class), and the

Hi All, I've just rebuild the latest R with the Goto BLAS on our new Intel quad core machines. I did a few basic matrix calculations, and I was very impressed by the performance I saw. I wonder if anyone has a more rigorous benchmarking program for R. I downloaded a old R test/benchmarking program (see below), and this didn't work with the current R, and so I wondered if anyone could

Hi all, Where could I get souce code for samba-2.2.8a-1-sol8-suncc-64bit.pkg, I checked in samba.org but I just found pacakage( samba-2.2.8a-1-sol8-suncc-64bit.pkg). Thanks, Madhavi

Dear everyone: I am a new user of R. I have a dataset with a dependent variable (DV) censored at different values. The dataset looks like, conditions .....IDV1 IDV2 DV 1 2 4 89 1 6 6 75 1 4 5 0 ( DV<=70) ...... 2 3 5 15 2 5 5 0

Dear R helpers,   I have a data of 2102 observations (consisting of 0's also), to which I am trying to fit Normal distribution using "lmom" pacakage. If I use Excel, its easy to estimate the parameters of Normal distribution as simple mean and standard devaition. The results I get if I use teh excel are as   Parameters of Normal distribution :-   Mean = 22986.44 and standard