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Cordial saludo para cada uno. De manera amable les pido ayuda para acceder al código R usado para el algoritmo de la función qnorm. Gracias por su ayuda. César Escalante C. [[alternative HTML version deleted]]

Hi, I am working with some extremely small p-values and I want to capture the corresponding quantiles. I see the help file it says: 'qnorm' is based on Wichura's algorithm AS 241 which provides precise results up to about 16 digits. What happen after the 16th digits? If I am running R in a server 64-bit, can that improve the chances that beyond 16th digits to still have precision? Thanks, Aldi --

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 reference, but this algorithm does not discuss the generation of random values. help(RNG) indicates that the "Mersenne-Twister" is the default random number gener...

...there. The questions Firstly, is this a known problem with the qnorm and qt implementations? I could not find anything in the documentation, the algorithm is supposed to be accurate 16 digits for p values from 10^-314 as described in the Algorithm AS 241 paper. Quote from R doc for qnorm: "Wichura, M. J. (1988) Algorithm AS 241: The percentage points of the normal distribution. Applied Statistics, 37, 477?484. which provides precise results up to about 16 digits." If the R code implements the 7 digit version, why does it claim 16 digits? Or is it "accurate" but the original...

I am using pnorm() with the log.p=T argument to get approximations to ln \Phi(x) and qnorm with the log.p=T argument to get estimates of \Phi^{-1}(exp(x)). What approximations are used in these two functions (I noticed in the source pnorm.c it doesn't look like Abramowitz and Stegen) and where can I find the citation? Thanks, Richard Morey

how R implement qnorm() I wonder anyone knows the mathematical process that R calculated the quantile? The reason I asked is soly by curiosity. I know the probability of a normal distribution is calculated through integrate the Gaussian function, which can be implemented easily (see code), while the calculation of quantile (or Zα) in R is a bit confusing as it requires inverse error function (X