similar to: Using R's LAPACK & Related files in Visual C++

The following were the replies to my question about using R's LAPACK and other .h files in some of my C programs. From what was said, it appears that buying a ready-made library (MKL = $200, for example) or using CLapack according to the Shumway lecture notes are the best approaches: -------------------------------- >From Andy Liaw: (1) If you just want linear algebra routines in your C

hello, I have a c code which uses clapack. I want to integrate R with this code. It said that I create a R package including my c code. Then build it with putting PKG_LIBS = $(LAPACK_LIBS). I want to know the interface LAPACK_LIBS is Fortran or C? If it is written in Fortran, how can I build them together? If I can build them together, I still use the function in cLapack or the function in

I apologize if this has been addressed before; recently I read an article in Forbes which discussed how SCO was going after companies that have been using Linux. The article made the point that the ideas behind GPL are under attack precisely because no one is making sure that the code being put into the freely avail. packages isn't owned by someone else. Here's my question: Is R

I want to use R function Matrix inverse in my c code, please tell me how I can. If there is a sample which can tell me how it works. It will be fantastic.

I have a question that must have a simple answer (but eludes me). I need a row-by-row logical comparison across three numeric variables in a data frame: foo$x, foo$y, foo$z. The logic is if( x < y || x > z ) 1 else 0 for a particular row. It is simple and very inefficient to use for(i in 1:length(foo$x)){ } loops. How can I accomplish this using sappy( ) / lapply( ) / apply( ) or

R2.3, WinXP Dear all, I am using the following functions: f1 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(x))/exp(log(Phi4))) f2 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(r)-log(x))/exp(log(Phi4))) subject to the residual weighting Var(e[i]) = sigma^2 * abs( E(y) )^(2*Delta) Here is my question, in steps: 1. Function f1 is separately fitted to two different datasets corresponding to

Peter, Is there not already a significant maintenance burden from cherry-picking routines? From my own experience (https://bugs.r-project.org/bugzilla/show_bug.cgi?id=16482 <https://bugs.r-project.org/bugzilla/show_bug.cgi?id=16482>) this is a painful process. And while users may not call these complex routines directly, R is often used as a front-end for libraries that do, so

Hello, I was building a R addin for Excel. I was able to build R.dll, export to Rdll.lib and wrap it in a Excel addin (.xll) format. Most basic functions work fine within Excel except functions that use LAPACK, e.g. eigen, solve etc. But if you use solve(..., LINPACK = T). That's fine. So apparently the problem is with loading the shared library ../modules/lapack.dll. I read one of the

Hi, I'm just wondering if it is planned to include the Lapack routine DGESDD (and friends) in R-1.4.0? This is faster (supposedly by a factor of ~6 for large matrices) than DGESVD which is currently (R-1.3.1) called by La.svd. And if it is not in the plans yet, is there a chance it could be? I've added it to my local version of R-1.3.1 and so far see a factor of 4 improvement over

I apologize if this question is not completely appropriate for this list. I have been using SAS for a while and am now in the process of learning some C and R as a part of my graduate studies. All of the statistical packages I have used generally yield p-values as a default output to standard procedures. This week I have been reading "Testing Precise Hypotheses" by J.O. Berger

Hey Everyone, I'm running R 2.4.0 on Debian etch 4.0, and I'm trying to call some LAPACK functions from the C code in my package. Actually, to be honest I'm not really having trouble using commands such as La_dgesv from within my C code, but I do get warning when compiling the package saying: ***.c: In function '***': ***.c:37: warning: implicit declaration of function

I am interested in tapping into LAPACK functionality directly from R. Using R-2.4.1 for Windows, I was able to do so ala: dyn.load("bin/Rlapack.dll") is.loaded("dstebz") # returns TRUE N <- 100 NW <- 4 n.tapers <- 5 tpW <- (2 * pi * NW)/N otNmo <- 1:N D <- as.double(cos(tpW) * ((N - 1 - 2 * (0:(N - 1)))/2)^2) E <- as.double((otNmo * (N - otNmo))/2) z

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

Hi All, Can someone tell me if solve function shown below for my version of R is proper or not? I am using R 2.7.2 .Wherever i have used this function ,i got results which were different from the expected results as computed using SPSS. Description of this function says:-- Solve a System of EquationsDescription This generic function solves the equation a %*% x = b for x, where b can be either

I want to speed up computations (involving matrices) by writing some C-code to be loaded. In the C-code, I need to invert matrices etc. As I've understood the "writing R extensions" doc, I can use use #include <R_ext/Linpack.h> in my .c-file and get access to linpack's facilities within my C-code. Is that correct? If so, can anyone point me to examples on how this is

On 11/09/2019 21:38, Berend Hasselman wrote: > The Lapack library is loaded automatically by R itself when it needs it for doing some calculation. > You can force it to do that with a (dummy) solve for example. > Put this at start of your script: > > <code> > # dummy code to get LAPACK library loaded > X1 <- diag(2,2) > x1 <- rep(2,2) > # X1;x1 > z <-

(I asked an earlier question on the rpy2 installation to the rpy2 list, the solution there was to edit the setup.py script, but this looks beyond what I can do unless someone tells me exactly what to modify). I have R 2.8.1 on 2 machines running ubuntu 8.04 (packages R-base and R-base-dev, among others, installed using Synaptic) and while the installation of rpy2 went fine on machine1, failed on

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, I wonder where I can find an example of using a function in LAPACK library in a user's own C code. I wrote a C program which will be compiled and linked to produce a DLL file and then loaded into R. I hope to use a function from LAPACK library, for example, dgesdd, in the program. Following R manual, I call the function by F77_CALL(dgesdd) in the program. The program can be compiled