search for: kotze

...but it complains about a number of missing packages. Is there any docs on how to compile compiz on SLED 10 ? I have searched in a lot of places, but have not been able to dig up anything. A detailed list of what packages is required (and where to locate them) would be greatly appreciated. Johan Kotze -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.freedesktop.org/archives/compiz/attachments/20061017/3b856611/attachment.html

About two years ago there was a thread about this which suggested that at that time nobody had these coefficients ready to go. (a) has anyone in the meanwhile programmed them? (b) I think I can see how to do the tetrachoric one with mvtnorm on similar lines to an example on the help page so will try that if nobody else already has (c) looking at the polychoric one makes me realise yet again

...not met: No package 'xrandr' found No package 'xinerama' found No package 'ice' found No package 'sm' found The packages that it complains about are all part of xorg and all the xorg devel files are installed. What am I missing ? Johan Kotze -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.freedesktop.org/archives/compiz/attachments/20070408/eea7ef09/attachment.html

> On Aug 14, 2017, at 5:17 AM, peter dalgaard <pdalgd at gmail.com> wrote: > > >> On 14 Aug 2017, at 13:43 , Spencer Graves <spencer.graves at effectivedefense.org> wrote: >> >> >> >> On 2017-08-14 5:53 AM, peter dalgaard wrote: >>>> On 14 Aug 2017, at 10:13 , Troels Ring <tring at gvdnet.dk> wrote: >>>>

Hi, I am fairly new to R, and am stuck. I want to write an R function with argument n that returns a vector of length n with n simulated observations from the double exponential distribution with density: ??g(y) = 1/2e^-y ? For the double exponential, I want to generate y~Exp(1) and then take ?y with probability 0.5 ? Does anyone know how I can do this in R? Thanks! Fran [[alternative

Hello R-Team, I am trying to construct a Copula from a multivariate Gamma distribution with its marginals gamma-distributed. The multivariate Gamma should be able to contain a correlation coeficient or matrix. I have studied the book "Continuous Multivariate Distributions vol.I Models and applications" by Johnson & Kotz & Balakrishnan but what I constructed doesn't really

HI Every one Could some one provide me definitions of following bivariate distributions gamma, exponencial, Weibull, half-normal , Rayleigh, Erlang,chi-square thanks A.S. Qureshi

Users of R-Help might be interested in this new article by Bob O'Hara and Johan Kotze published on Methods in Ecology and Evolution on why you should not log-transform count data, including an R code: http://www3.interscience.wiley.com/journal/123328987/abstract Thanks Kind regards, Graziella ----------------------------------------------- Dr Graziella Iossa Journal Coordinator...

Hi Freebies!! I know F-BSD 4.8 supports a framework in the kernel to use crypto functions from hifn crypto cards. Is there any of these cards that support custom crypto? What is the best route to go if I want to support IPSec (and maybe other) crypto functions but with custom crypto algorithms? Any info or ideas will be appreciated. Thanks Peut

Hello, I would like to know if there is an R-package available for computing the density, distribution function, quantiles and random numbers of the p-generalized normal distribution or if somebody is already working on it. Best regards, Steve Kalke

Two questions: 1. inverse.gaussian is up there as one of the glm families, but do people ever use it? There is no inverse.gaussian in the R distribution family, and when I checked McCullagh & Nelder, it only appeared twice in the book (according to subject index), once in the table on p. 30 and once on p. 38 in a passing sentence. Is there a good reference on this distribution? 2. When I

I would like to try a simple parametric bootstrap, but unfortunately (stupidly?) my models are "overdispersed" gams & glms. I'm hoping for a function that generates overdispersed poisson or negative binomial data with a given mean, scale (& shape parameter). The loose definition I'm using is overdispersed poisson produces integer values with variance=const*mean &

Suppose F(x, y; rho) is the cdf of a bivariate normal distribution, with standardized marginals and correlation parameter rho. For any fixed x and y, I wonder if F(x, y; rho) is a monotone increasing function of rho, i.e., there is a 1 to 1 map from rho to F(x, y; rho). I explored it using the function pmvnorm in package mvtnorm with different x and y. The plot suggests the statement may be true.

Hi all, I apologize for this off-topic question but I need your help -- I know there are lots of experts here. As a lover and student of statistics, I am thinking of building a tree of various branches of statistics and keeping track of the greatest historical inventions/discoveries in statistics and the latest development of each branch. The goal is to understand in what context did the great

Hi, Below I have a function mlogl_k, later it's called with "nlm" . __BEGIN__ vsamples<- c(14.7, 18.8, 14, 15.9, 9.7, 12.8) mlogl_k <- function( k_func, x_func, theta_func, samp) { tot_mll <- 0 for (comp in 1:k_func) { curr_mll <- (- sum(dgamma(samp, shape = x_func, scale=theta_func, log = TRUE))) tot_mll <- tot_mll + curr_mll }

Hi all, I'm looking for an analytic method to obtain the PDF & CDF of the sampling distribution of a given correlation (rho) at a given sample size (N). I've attached code describing a monte carlo method of achieving this, and while it is relatively fast, an analytic solution would obviously be optimal. get.cors <- function(i, x, y, N){ end=i*N

I've built R functions to ``effect'' a particular distribution, and would like to find out if that distribution is already ``known'' by an existing name. (I.e. suppose it were called the ``Melvin'' distribution --- I've built dmelvin, pmelvin, qmelvin, and rmelvin as it were, but I need a real name to substitute for melvin.) The distribution is really just a toy

> On 14 Aug 2017, at 13:43 , Spencer Graves <spencer.graves at effectivedefense.org> wrote: > > > > On 2017-08-14 5:53 AM, peter dalgaard wrote: >>> On 14 Aug 2017, at 10:13 , Troels Ring <tring at gvdnet.dk> wrote: >>> >>> Dear friends - I hope you will accept a naive question on lm: R version 3.4.1, Windows 10 >>> >>> I

On 2017-08-14 5:53 AM, peter dalgaard wrote: >> On 14 Aug 2017, at 10:13 , Troels Ring <tring at gvdnet.dk> wrote: >> >> Dear friends - I hope you will accept a naive question on lm: R version 3.4.1, Windows 10 >> >> I have 204 "baskets" of three types corresponding to factor F, each of size from 2 to 33 containing measurements, and need to know if the

Dear list: I am attempting to use what I thought would be a pretty straightforward practical application of Cox regression. I figure users of the survival package must have come across this problem before, so I would like to ask you how you dealt with it. I have set up an illustrative example and included it at the end of this post. I took a sample of 100 data points from each of two populations