similar to: two almost identical packages: best practice

Hi I thought it would be fun to develop R functionality for the octonions (there is already some work on quaternions). The octonions are an 8 dimensional algebra over the reals, so an octonion may nicely be represented as a real vector of length 8. Applications are many and varied, mostly quantum mechanics. I would like to develop some R functionality in this area. My first problem is how

Hi it says in R-exts that A method must have all the arguments of the generic, including ... if the generic does. A method must have arguments in exactly the same order as the generic. A method should use the same defaults as the generic. So, how come the arguments for rep() are (x, times, ...) and the arguments for rep.default() are (x, times, length.out, each, ...) ?

Hi How do I test for an object having a particular virtual class? In the following, "onion" is a virtual class, and "octonion" is a non-virtual class contained in onion. The last call to inherits() gives FALSE [R-2.5.0], when inherits.Rd led me to expect TRUE. setClass("onion", representation = "VIRTUAL" )

On Oct 21, 2007, at 6:23 PM, BGB wrote: > ok, just in my case, I guess I am a little fussy/weird, wanting > efficient dot and cross products (if at all possible, though these > operations at present don't exist natively within SSE...), among > many other operations (length, lerp, renormalization, ..). > > then again, probably a fairly smart compiler could still get

Hi In NEWS, it says R CMD check now tests that the package can be loaded, and that the package and namespace (if there is one) can each be loaded in startup code (before the standard packages are loaded). For package "onion", I think I have followed all of sections 1.6.1 - 1.6.6 of R-exts, and it passes R CMD check for R-2.3.1. However, using R version 2.4.0 Under

I'm trying to create a package for working on orientation data, i.e. data where the observations are 3D rotations. There are several different representations of orientations in common use: SO(3) matrices, Euler angles, unit quaternions, etc. One thing I'd like is to make it convenient to work in any representation, and have conversions to others done as needed. I'm trying to do

Hello everybody When one is working with complex matrices, "transpose" very nearly always means *Hermitian* transpose, that is, A[i,j] <- Conj(A[j,i]). One often writes A^* for the Hermitian transpose. I have only once seen a "real-life" case where transposition does not occur simultaneously with complex conjugation. And I'm not 100% sure that that wasn't a

Hi all I have run into a case where I don't understand why predict.lrm and predict.glm don't yield the same results. My data look like this: set.seed(1) library(Design); ilogit <- function(x) { 1/(1+exp(-x)) } ORDER <- factor(sample(c("mc-sc", "sc-mc"), 403, TRUE)) CONJ <- factor(sample(c("als", "bevor", "nachdem",

well, sadly, I am not sure how people are on this list... in any case, LLVM is an interesting project, and may well continue being interesting. but, in my case, I have done my own compilation framework... ok, I didn't really hear about the really interesting bits of LLVM until after I had (more or less) wrote mine... ok, my point is to maybe to have something interesting to talk about, not

Dear R-users, I am trying to translate a matlab code for calculating the Local Whittle estimator in time series with long memory originally written by Shimotsu and available free in his webpage ( http://www.econ.queensu.ca/pub/faculty/shimotsu/ ) The Matlab code is ======================================================================================= function[r] = whittle(d,x,m) % WHITTLE.M

Hi, All: Is there a simple way to detect complex conjugates in the roots returned by 'polyroot'? The obvious comparison of each root with the complex conjugate of the next sometimes produces roundoff error, and I don't know how to bound its magnitude: (tst <- polyroot(c(1, -.6, .4))) tst[-1]-Conj(tst[-2]) [1] 3.108624e-15+2.22045e-16i

Hi section 6.1 of R-exts suggests that a package can take over a function in the base package and make it generic. I want to do this with Re() and have the following lines in my R code: "Re" <- function(x){UseMethod("Re" )} "Re.default" <- get("Re" ,pos=NULL,mode="function") "Re.octonion" <- function(x){give.comp(x,1)}

Dear List I have just uploaded a new package, "onion", to CRAN. It provides some functionality for manipulating and visualizing quaternions and octonions. All comments welcome! enjoy rksh -- Robin Hankin Uncertainty Analyst National Oceanography Centre, Southampton European Way, Southampton SO14 3ZH, UK tel 023-8059-7743 _______________________________________________

Hi, Man page for 'convolve' says: conj: logical; if 'TRUE', take the complex _conjugate_ before back-transforming (default, and used for usual convolution). The complex conjugate of 'x', of 'y', of both? In fact it seems that it takes the complex conjugate of 'y' only which is OK but might be worth mentioning because (1) conj=TRUE is the

On two laptops running 32-bit kubuntu, I have found that svd(), invoked within R 2.9.1 as supplied with the current ubuntu package, returns very incorrect results when presented with complex-valued input. One of the laptops is a Dell D620, the other a MacBook Pro. I've also verified the problem on a 32-bit desktop. On these same systems, R compiled from source provides apparently

Dear R-ophiles, I've found something very odd when I apply convolve to ever larger vectors. Here is an example below with vectors ranging from 2^11 to 2^17. There is a funny bump up at 2^12. Then it gets very slow at 2^16. > for( i in 11:20 )print( system.time(convolve(1:2^i,1:2^i,type="o"))) user system elapsed 0.002 0.000 0.002 user system elapsed 0.373

Hi: I create a hermitian matrix and then perform its singular value decomposition. But when I put it back, I don't get the original hermitian matrix. I am having the same problem with spectral value decomposition as well. I am using R 1.7.0 on Windows. Here is my code: X <- matrix(rnorm(16)+1i*rnorm(16),4) X <- X + t(X) X[upper.tri(X)] <- Conj(X[upper.tri(X)]) Y <-

In the Green Book, section 7.5 discusses new vector classes and uses quaternions as an example of a vector class that needs more than one number per element. I would like to define a new class that has a numeric vector and a logical vector of the same length that specifies whether the measurement was accurate. The following code does not behave as desired: >

On Oct 21, 2007, at 5:27 PM, BGB wrote: > well, sadly, I am not sure how people are on this list... I'd suggest browsing through the llvmdev mailing list archive. > now, what I do with it is this: > I use C as a scripting language... > Sounds like a fun project. Note that the clang C front-end also supports use in a JIT environment: this will allow you to JIT the full

Hi, Has anyone in R core thought about providing "hyper-dual numbers" in R? Hyper-dual (HD) numbers, invented by Jeffrey Fike at Stanford, are useful for computing exact second-order derivatives (e.g., Hessian). HD numbers are extensions of complex numbers. They are like "quaternions" and have 4 parts to them (one real and 3 non-real). They seem to be available in Julia.