similar to: R-beta: Help: cov.mve in R? dgamma in Splus?

Is there public domain code for cov.mve? Thanks, -k =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-devel-request@stat.math.ethz.ch

Dear All, The Minimum Volume Ellipsoid estimator ("cov.mve") was part of S+ but I cannot find it in R. Where can I find it? Why was it excluded? Cheers, Patrik Sweden

Dear R users, I am trying to use MCD and MVE methods in the analysis of functional imaging (fMRI) data. But, before doing that, I want to understand the sampling distribution of the correlation parameter given by MCD and MVE (cov.mcd$cor, cov.mve$cor). To this end, I conducted a simulation where in each of 100000 epochs, I a. construct a matrix from two vectors, each containing 40 numbers

Dear R users, I am trying to use MCD and MVE methods in the analysis of functional imaging (fMRI) data. But, before doing that, I want to understand the sampling distribution of the correlation parameter given by MCD and MVE (cov.mcd$cor, cov.mve$cor). To this end, I conducted a simulation where in each of 100000 epochs, I a. construct a matrix from two vectors, each containing 40 numbers

Hello mailing-list, this is my first post and i hope that you enjoy my very bad but sometimes funny english. My Problem is the following: First of all, my server-config: Samba Version 3.0.20b-3.4-SUSE on a SUSE Linux Enterprise Server 9 with Kernel 2.6.5-7.252-smp Now the problem is, that samba generally works fine, but it doesn't work to work with a special application directly on a

Robust Covariance Estimation Software via Nearest Neighbor Variance Estimation (NNVE) Software to carry out robust covariance estimation by Nearest Neighbor Variance Estimation (NNVE) [Wang and Raftery (2002, J. Amer. Statist. Ass.)] is now available for R and Splus. In the simulation studies published in JASA, this had mean squared error at least 100 times smaller than that of other leading

Robust Covariance Estimation Software via Nearest Neighbor Variance Estimation (NNVE) Software to carry out robust covariance estimation by Nearest Neighbor Variance Estimation (NNVE) [Wang and Raftery (2002, J. Amer. Statist. Ass.)] is now available for R and Splus. In the simulation studies published in JASA, this had mean squared error at least 100 times smaller than that of other leading

On 11/6/20 12:39 PM, Sjoerd Meijer wrote: Hello Simon, Thanks for your replies, very useful. And yes, thanks for the example and making the target differences clear: ; Some examples: ; RISC-V V & VE(*): ; %mask = (splat i1 1) ; %evl = min(256, %n - %i) ; MVE/SVE : ; %mask = get.active.lane.mask(%i, %n) ; %evl = call @llvm.vscale() ; AVX: ; %mask = icmp (%i + (seq

Hi! The nobs() method for glm objects always returns the number of cases with non-null weights in the data, which does not correspond to the number of observations for Poisson regression/log-linear models, i.e. when family="poisson" or family="quasipoisson". This sounds dangerous since nobs() is, as the documentation states, primarily aimed at computing the Bayesian

On 11/6/20 8:49 AM, Roger Ferrer Ibáñez wrote: Hi Sjoerd, Trying to remember how everything fits together here, but could get.active.lane.mask not create the %mask of the VP intrinsics? Or in other words, in the vectoriser, who's producing the %mask and %evl that is consumed by the VP intrinsics? I'm not sure what would be the best way here. I think about the Loop Vectorizer. I imagine

Hi, I abandoned that approach and followed Eli's suggestion, see somewhere earlier in this thread, and emit an intrinsic that represents/calculates the active mask. I've just uploaded a new revision for D79100 that implements this. Cheers. ________________________________ From: Simon Moll <Simon.Moll at EMEA.NEC.COM> Sent: 18 May 2020 13:32 To: Sjoerd Meijer <Sjoerd.Meijer at

Hello llvm-devs, I'm currently starting to look at implementing compiler intrinsics for the Arm MVE vector instruction set. In a similar sort of style to the existing NEON intrinsics, this is going to involve describing the set of functions needed in Tablegen, and then processing that data into a header file and some bits and pieces to compile into clang (the list of builtins, data tables

Hello all (especially MCLUS users). I'm trying to make use of the MCLUST package by C. Fraley and A. Raftery. My problem is trying to figure out how the (model) identifier (e.g, EII, VII, VVI, etc.) relates to the covariance matrix. The parameterization of the covariance matrix makes use of the method of decomposition in Banfield and Rraftery (1993) and Fraley and Raftery (2002) where

Hi Developers, I am looking for another new project to help me get more up to speed on R and to learn something outside of R internals. One recent R issue I have run into is finding a fast implementations of the equivalent to the following SAS code: /* MDPC is an integer sort key made from two integer columns */ MDPC = (MD * 100000) + PCO; /* sort the dataset by the key */ PROC SORT;

Dear listers, Am currently using MCMC approaches to estimate some parameters of my model. One parameter has to be updated using a tuned gamma distribution. So at each iteration I estimate the mean and variance of the density of the gamma approximation using "vmmin" (i also supply the gradient argument). For moderate replications the procedure works, but if I increase them R crashes.

Hi all, The systems guys here in the stat dept don't seem to be able to compile R on the Dec Alphas or on the SGIs. Can anyone give them a hand? -Greg ------------------------------------------------------------------------------- Gregory R. Warnes | It is high time that the ideal of success warnes at biostat.washington.edu | be replaced by the ideal of service.

For RISC-V V and VE being explicit about %evl is important for performance & correctness and that is what VP does. The get.active.lane.mask intrinsic is used as a hint for the MVE, SVE backends to use hardware tail-predication (the backends reverse engineer that hint by pattern matching for get.active.lane.mask in the mask parameter of "some" masked intrinsics). IMHO, it's more

Hello everyone, I am searching for a covariate selection procedure in a cox model formulated as a counting process. I use intervals, my formula looks like coxph(Surv(start,stop,status)~ x1+x2+...+cluster(id),robust=T) where id is a country code (I study occurence of civil wars from 1962 to 1997). I'd like something not based on p-values, since they have several flaws for this purpose. I turned

> You have similar problems with https://reviews.llvm.org/D79100 The new revision D79100<https://reviews.llvm.org/D79100> solves your comment 1), and I don't think your comments2) and 3) apply as there are no vendor specific intrinsics involved at all here. Just to quickly discuss the optimisation pipeline, D79100<https://reviews.llvm.org/D79100> is a small extension for the

Hi Roger, That's a good example, that shows most of the moving parts involved here. In a nutshell, the difference is, and what we would like to make explicit, is the vector trip versus the scalar loop trip count. In your IR example, the loads/stores are predicated on a mask that is calculated from a splat induction variable, which is compared with the vector trip count. Illustrated with your