similar to: Cumulative density (kernel smoothing)

Dear all, I'm trying to estimate the parameters of a lognormal distribution fitted from some data. The tricky thing is that my data represent the time at which I recorded certain events. However, in many cases I don't really know when the event happened. I' only know the time at which I recorded it as already happened. Therefore I want to fit the lognormal from the cumulative

Hi Ralph, Again, thanks so much for doing all this! Plus thanks to all the folks who contributed to the new release! Quick clarifying question -- Isn't CVE-2018-10392 (looks like it’s fixed in https://gitlab.xiph.org/xiph/vorbis/-/issues/2335) also included in new version 1.3.7? If so can you please add it to release notes? (I asked the same question in

Hi folks, I know that density function will give a estimated density for a give dataset. Now from that I want to have a percentage estimation for a certain range. For examle: > y = density(c(-20,rep(0,98),20)) > plot(y, xlim=c(-4,4)) Now if I want to know the percentage of data lying in (-20,2). Basically it should be the area of the curve from -20 to 2. Anybody knows a simple

Yes, the gitlab instance is the correct upstream development repository. We maintain a mirror at github for the convenience of developers there. Cheers, Ralph On Mon, 2020-06-29 at 21:27 +0000, Ellen Johnson wrote: > Hi Ralph and libvorbis developers, > I thought the vorbis gitlab project was the main development site ( > https://gitlab.xiph.org/xiph/vorbis) because that's what

Hi libvorbis developers! I'm wondering if you had a chance to see my request for releasing a new libvorvis version - this is to have an official libvorbis release containing the CVE fixes that appear to be fixed in the master branch. Is there anything we can do to help with getting a release out? We're happy to work with you on this. Please let us know if we can do anything to help

Hi Ralph, Thank you for your reply! For context -- we consider reported CVEs as bugs even if it's in a third-party library we use (such as libvorbis). We first determine if the CVE is something that would impact our customer workflows. In this case because of our use of libvorbis for audio I/O, it does impact our customers so we need to resolve the CVE as soon as possible. In the

Hello, I have a data x with normal (or very close to normal) distribution, I can plot a density distribution with density(x,...). My question is is there any way to calculate an area under this distribution (=probability) for particular range of x values, let's say for x from 0 to 2? I was not able to find any kind of simple procedure to do this. Thanks in advance for your help, Evgeny.

Hi all, A friend just sent me this: http://www.mathworks.com/company/events/webinars/index.html?id=&language=en <http://www.mathworks.com/company/events/webinars/index.html?id=&language=en>And asked me if there is something of the like in the R community. Does anyone know of such a think ? Cheers, Tal ----------------Contact

Dear R-list, I have made a simple kernel density estimation by x <- c(2,1,3,2,3,0,4,5,10,11,12,11,10) kde <- density(x,n=100) Now I would like to know the estimated probability that a new observation falls into the interval 0<x<3. How can I integrate over the corresponding interval? In several R-packages for kernel density estimation I did not found a corresponding function. I

I've been trying to figure this out for a while, but my knowledge of R is obviously still too limited. The context is as follows: I have some time series, and I would like to estimate their densities, and then use the actual densities in a monte carlo simulation. Now, I can easily estimate the density using density(); I can write a random number generator to fit an arbitrary density

Thanks for testing out LLVM PGO and evaluated the performance. We are currently still more focused on infrastructure improvement which is the foundation for performance improvement. We are making great progress in this direction, but there are still some key missing pieces such as profile data in inliner etc. We are working on that. Once those are done, more focus will be on making more passes

Hello, I need density function so that I can find expected value (using integration). I use density(): f= density(data) but f isn't a function and I can't get values and integrate it This is very urget, so please help. Greetings Peter -- View this message in context: http://r.789695.n4.nabble.com/density-function-tp4634563.html Sent from the R help mailing list archive at Nabble.com.

Hello, I'm writing some genetic simulations in R where I would like to place genes along a chromosome proportional to the density of markers in a given region. For example, a chromosome can be presented as a list of marker locations: Chr1<-c(0, 6.5, 17.5, 26.2, 30.5, 36.4, 44.8, 45.7, 47.8, 48.7, 49.2, 50.9, 52.9, 54.5, 56.5, 58.9, 61.2, 64.1) Where the numbers refer to the locations of

Hi all, Do any packages implement density estimation in a modelling framework? I want to be able to do something like: dmodel <- density(~ a + b, data = mydata) predict(dmodel, newdata) This isn't how sm or KernSmooth or base density estimation works. Are there other packages that do density estimation? Or is there some reason that this is a bad idea. Hadley -- http://had.co.nz/

Dear R users, I would like to show the estimated density of a (0, 1) uniformly distributed random variable. The density curve, however, goes beyond 0 and 1 because of the kernel smoothing. Example: x = runif(10000) plot(density(x)) Is there a way to estimate the density curve strictly within (0, 1) and still use some sort of smoothing? Any help would be greatly appreciated. Best regards,

https://bugs.freedesktop.org/show_bug.cgi?id=74815 Priority: medium Bug ID: 74815 Assignee: nouveau at lists.freedesktop.org Summary: nouveau: no displayport output on quadro 400/600 cards QA Contact: xorg-team at lists.x.org Severity: normal Classification: Unclassified OS: Linux (All) Reporter: mgulick at

Can anyone recommend a good nonparametric density approach for data bounded (say between 0 and 1)? For example, using the basic Gaussian density approach doesn't generate a very realistic shape (nor should it): > set.seed(1) > dat <- rbeta(100, 1, 2) > plot(density(dat)) (note the area outside of 0/1) The data I have may be bimodal or have other odd properties (e.g. point mass

Dear R help contributers, I have several x,y scatter plots and I would like to plot them slightly offset, similar to: http://www.mathworks.com/matlabcentral/fx_files/24368/2/content/style4.jpg I've looked all over for this answer with no luck. Just a function name or previous example would probably be enough for me to figure it out. Thank-you in advance, Carly

hi, all I am looking at R package RGL to draw a colored mesh/surface plot like this one (from matlab). http://www.mathworks.com/access/helpdesk/help/techdoc/visualize/cbar.gif The key features I am looking for is surfaced with grid and color, but not the terrain-like gradient. but I didn't come even close to it after browsing through rgl help file. have anyone drawn something like this

I would like to efficiently find the first index of each unique value in a very large vector. For example, if I have a vector A<-c(9,2,9,5) I would like to return not only the unique values (2,5,9) but also their first indices (2,4,1). I tried using a for loop with which(A==unique(A)[i])[1] to find the first index of each unique value but it is very slow. What I am trying to do is easily