similar to: R-alpha: cov.mve

Hi all I have a couple of obscure questions for R/Splus experts (which unfortunately isn't me!) I am trying to compute Bayes Factors using some Splus code of Raftery in Gilks et al (1996). Only problem is 1) R doesn't seem to have a robust covariance (cov.mve) which I suspect I need rather than a non-robust classical estimate 2) Splus has cov.mve BUT dgamma in Splus doesn't have a

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

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

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

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;

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

> 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

Hello, We are working on predication for our vector extension (MVE). Since quite a few people are working on predication and different forms of it (e.g. SVE, RISC-V, NEC), I thought I would share what we would like to add to the loop vectoriser. Hopefully it's just a minor one and not intrusive, but could be interesting and useful for others, and feedback on this is welcome of course. TL;DR:

Does R have a function like the S(plus) function, lmsreg, Least Median of Squares Regression? I am using R-0.61. Thank you, Mike Fleming mfleming at nass.usda.gov -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Does R have a function like the S(plus) function, lmsreg, Least Median of Squares Regression? I am using R-0.61. Thank you, Mike Fleming mfleming at nass.usda.gov -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Hi, so I don't speak computer and I have no idea what this code is telling the program to do, but I apparently need to be able to find and isolate influencial observations. Problem, I have no idea what the error means and where it may be from in the code. error I get is below the code { ## OLS results NameC<- lm(gpanew~female+female:lastinit+agenew+canadian+mom_ed+yearstudy) ## default:

(all in subject). If I want to do a PC analysis in a situation with missing data, I may want to have same flexibility as with "cor(.)", e.g., I may want princomp(x, ..., use.obs = "pairwise.complete") Actually, I may want even more flexibility. Currently, princomp(.) has if (cor) cv <- get("cor", envir = .GlobalEnv)(z) else cv <-

Hi, Arm have recently announced the v8.1-M architecture specification for our next generation microcontrollers. The architecture includes vector extensions (MVE) and support for low-overhead branches (LoB), which can be thought of a style of hardware loop. Hardware loops aren't new to LLVM, other backends (at least Hexagon and PPC that I know of) also include support. These implementations

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