similar to: Fortran routines from LINPACK in S+ but not R

I have dealt with this on so many platforms in the past that I should know the answer by now. I looked in V&R S Programming, R manuals and FAQ and still can't find the answer. In the Hmisc library I used the R scripts to build the package, which creates Hmisc.so. I have library.dynam('Hmisc', ...) in the .First.lib. When doing library(Hmisc) I get Error in dyn.load ....

man for `qr` says that the function uses LINPACK's DQRDC, while it in fact uses DQRDC2. ``` The QR decomposition of the matrix as computed by LINPACK or LAPACK. The components in the returned value correspond directly to the values returned by DQRDC/DGEQP3/ZGEQP3 ```

In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); if (*info == 0){ F77_CALL(dpodi)(*hessian, &bdim, &bdim, det, &job); ........ This usually works OK, but with an ill-conditioned data

Ordinary functions should not alter their inputs but in R-2.15.2 svd(LINPACK=TRUE,X) does. (It worked in 2.15.0 but not in 2.15.1 or 2.15.2 and became deprecated in 2.15.2.) > X <- matrix(c(1,2,3, 5,7,11, 13,17,19), 3, 3) > X [,1] [,2] [,3] [1,] 1 5 13 [2,] 2 7 17 [3,] 3 11 19 > svd(X, LINPACK=TRUE)$d [1] 31.9718214 2.3882717 0.3143114 Warning message:

> On 10 Feb 2017, at 14:53, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > Thanks to all who answered my third question. I learned something, but: > > On 2017-02-09 17:44, Martin Maechler wrote: >> >>>> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: >>>> >>>> In my package 'glmmML'

> > On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > > > In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: > > > > F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); > > if (*info == 0){ > >

Hello. I am trying to invert a matrix, and I am finding that I can get different answers depending on whether I set LAPACK true or false using "qr". I had understood that LAPACK is, in general more robust and faster than LINPACK, so I am confused as to why I am getting what seems to be invalid answers. The matrix is ostensibly the Hessian for a function I am optimizing. I want to get

Hi everybody. I'm studying the Harrell's book on regression modeling strategies, and I'd like to replicate the examples using R (instead of S+). So how's going the porting of the hmisc and design libraries? Are there available, though in beta version? TIA, Stefano Calza -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: > > F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); > if (*info == 0){ > F77_CALL(dpodi)(*hessian,

Thanks to all who answered my third question. I learned something, but: On 2017-02-09 17:44, Martin Maechler wrote: > >>> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: >>> >>> In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this:

Hello, Like QR decomposition, I am looking for decomposition to get U and V matrix of SVD decomposition (according to LINPACK, X = UDV'). Do you know if there is a function which could calculate this decomposition? Look forward to your reply, Haleh

Hi, I understand that the glm.fit calls LINPACK fortran routines instead of LAPACK because it can handle the 'rank deficiency problem'. If my data matrix is not rank deficient, would a glm.fit function which runs on LAPACK be faster? Would this be worthwhile to convert glm.fit to use LAPACK? Has anyone done this already?? What is the best way to do this? I'm looking at very

> Date: Wed, 30 Jun 1999 11:12:24 +0200 (MET DST) > From: Torsten Hothorn <hothorn at amadeus.statistik.uni-dortmund.de> > > yesterday I had a little shock using qr (or lm). having a matrix > > X <- cbind(1,diag(3)) > y <- 1:3 > > the qr.coef returns one NA (because X is singular). So I computed the > Moore-Penrose inverse of X (just from the

Dear Sirs, I am trying to compile R v1.2.1 under Digital Unix 4.0D. The configure-script runs without any problems but during 'make' I receive the following error-message: [......] g77 -mieee -g -O2 -c dtrco.f -o dtrco.o g77 -mieee -g -O2 -c dtrsl.f -o dtrsl.o g77 -mieee -g -O2 -c eigen.f -o eigen.o g77 -mieee -g -O2 -c lminfl.f -o lminfl.o ar cr libappl.a Rsock.o approx.o bakslv.o

Recent sensible changes to the dynload mechanism have made an old problem resurface: how should we deal with packages which contain Fortran code and may need to be linked against additional libraries such as -lf2c? The current consensus is that extra Fortran libraries maybe needed are handled via the make variable FLIBS, and that `-lf2c' or `-lg2c' are added by default if g77 is used.

Dear developers, I am trying to reproduce the results of applying opt -O3 to a source file in the form of LLVM IR. I want to get the same IR by manually ordering the passes used by O3 and passing them to opt. To illustrate what I am doing on an example, as an input I use linpack benchmark from the LLVM test suite[1]: 1. First I produce the intermediate representation using clang: clang -O3

After fixing princomp(), recently, {tiny negative eigen-values are possible for non-negative definite matrices} Fritz Leisch drew my attention to the fact the not only eigen() can be funny, but also svd(). Adrian Trappleti found that the singular values returned can be "-0" instead of "0". This will be a problem in something like sd <- svd(Mat) $ d

I think this has to do with how the pass manager is populated when we give -O3 vs when we give particular pass names. Some passes have multiple createXYZPass() methods that accept arguments too. These methods call non-default pass constructors, which in turn cause the passes to behave in a different manner. eg: Pass *llvm::createLICMPass() { return new LegacyLICMPass(); } Pass

Hello, This morning I downloaded, unzipped, and compiled the latest version of R for unix, in my HOME/APPLICATIONS directory. My current unix machine is a sparc-sun-solaris2.9, running SunOS 5.9. Here are the last few lines of the output following ./configure: R is now configured for sparc-sun-solaris2.9 Source directory: . Installation directory: /usr/local C compiler:

The first one failed on a diff: ******************** TEST (simple) 'sse.expandfft' FAILED! ******************** Execution Context Diff: /home/rkotler/llvmpb3/build/projects/test-suite/tools/fpcmp: Compared: 1.139094e-07 and 1.159249e-07 abs. diff = 2.015500e-09 rel.diff = 1.738626e-02 Out of tolerance: rel/abs: 1.600000e-02/0.000000e+00 ******************** TEST (simple)