search for: monotoni

I am not going to put my 2 cents in about added features. I just appreciate the reams of technical support the OpenBSD developers offers us for the code they give us for free. $400 for an F-Secure license? I have my OpenSSH T-shirt! My request will add zero bytes to the OpenSSH code base, not even in the contribs directory. Could the subject lines on the mailing list begin with something like

...zph), B(t) is fairly non-constant. This isn't inherently a problem for me. I don't need a hard single number to characterize the shape of the excess risk. However, I'd like to be able to say something qualitative about the shape of the excess risk for the predictor. E.g., is it linear, monotonically increasing, monotonially decreasing, etc. Is it safe to use the coxph diagnostic plot for this purpose? I did try heuristically subtracting out the background and then fitting a spline using locfit as described in the MASS supplement, but this seemed a little more ad hoc than I was hoping for...

Hello, This is about fortune package, but I think that might be related also to base R, so I am sending to package maintainer and R-devel list. I have the following in my .Rprofile to break monotony of code writing library(utils) library(fortunes) fortune() detach("package:fortunes") so I get a fortune every time I start R. It seems that this conflicts with R CMD INSTALL in latest

> Almost any method of fitting a density estimate would work on > integrating (numerically) the result. it is a nice idea concerning the monotony property, which will be obtained automatically, but I am going to use results of approximation analytically > In particular, look at package polspline, where > p(old)logspline does the integration for you. thank you, I am going to

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Hi all, is there any package in R capable of smooth approximation of CDF basing on given sample? (Thus, I am not speaking about ecdf) In particular, I expect very much that the approximation should subject to the property: f(x0)<=f(x1) for x0<x1, where x0 and x1 belong to range of the sample given. Polynomial approximation could be OK for me as well. P.S.

Hi, while testing some APERF/MPERF semantics I discovered that this feature is enabled in Xen Dom0, but is not reliable. The Linux kernel''s scheduler uses this feature if it sees the CPUID bit, leading to costly RDMSR traps (a few 100,000s during a kernel compile) and bogus values due to VCPU migration during the measurement. The attached patch explicitly disables this CPU capability