similar to: structure only

Hi, I was trying to draw an empirical distribution function with uniform confidence bands. So I tried to find a way to calculate values of the Kolmogorov-Smirnov Distribution but failed. I guess it must be hidden somewhere (since the ks-test is implemented), but I was unable to find it. Is there any way to do this? Thanks Leif Boysen

Hi, I am experiencing a failure running virt-v2v to convert a Linux guest on an ESX host to a RedHat KVM hypervisor. The output with the failure follows. Any help/guidance is appreciated. [root at storage-024 ~]# virt-v2v -ic esx://<ip address>/?no_verify=1 -op transferimages --bridge br0 dev-03 > /tmp/virt-v2v.output error from Term::ReadKey::GetTerminalSize(): Unable to get

Hallo da mein English nicht so gut ist und bei der ?bersetzung auch noch Missverst?ndnisse auftretten k?nnten, hier das Orginal :-) Das Problem ist, dass bei der Kombination openssh pam und ldap, die Verbindung zum Ldapserver so lange offen gehalten wird bis die ssh Session geschlossen wird. Das ist nur bei SSH so ! Alle andere Dienste sprechen den Server an und schliessen nach Best?ttigung des

Hi together, I have some data in a matrix structure - say 1000 rows with 10 columns. And I like to do some calculations (like max, avg or min) on row level. The only solution I found so fare was using a loop like for (i in 1:1000) { max[i] <- max(matrix[I,]) } It looks like that this is not very fast. Does an other way exists? Kind regards Ulrich

Dear R users, It is my pleasure to announce the availability of package stepR (1.0-2) on CRAN. The main purpose of the package is to fit piecewise constant functions (a.k.a. step-functions or block signals) to serial data in a fully data-driven manner under certain (Gaussian or non-Gaussian) distributional assumptions. It mainly implements the algorithms described in the references below -

Dear R users, It is my pleasure to announce the availability of package stepR (1.0-2) on CRAN. The main purpose of the package is to fit piecewise constant functions (a.k.a. step-functions or block signals) to serial data in a fully data-driven manner under certain (Gaussian or non-Gaussian) distributional assumptions. It mainly implements the algorithms described in the references below -

The answer to my question was contained in http://cran.r-project.org/contrib/extra/winedt/ReadMe , although it helped to receive the restatements that were emailed to me. As mentioned by Uwe Ligges, Renuad Lancelot, Niels Waller, my mistake was not starting R before using R-WindEdt. Below I will copy the instructions on using R-WinEdt by Renaud Lancelot (except for the last paragraph which is