similar to: R installation problem with recommended packages (PR#13899)

Full_Name: Uwe F. Mayer Version: 2.9.1 OS: SunOS myhost 5.10 Generic_120012-14 i86pc i386 i86pc Solaris Submission from: (NULL) (216.113.168.130) With the change of the NLS handling from 2.9.0 to 2.9.1 the compilation breaks, even as I do specifically not request nls. Specifics: gcc -v Using built-in specs. Target: x86_64-pc-solaris2.10 Configured with: ../../src/gcc-4.4.0/configure

Full_Name: Uwe F. Mayer Version: 2.9.1 OS: SunOS myhost 5.10 Generic_120012-14 i86pc i386 i86pc Solaris Submission from: (NULL) (216.113.168.130) Configuration problem with the creation of shared libraries: Compilation only succeeds if SHLIB_LDFLAGS="-shared" is added to the configure command line. This is likely due to a wellknown gcc-4 feature, in that gcc requires -shared and not

Hi, When I install new packages from CRAN, I frequently find that some packages were missing from the download queue. For example, on one of my computer with R2.6.2, I can not find package glmpath from the download queue. On my other computer with R2.5.1, I could still find that particular package. What could be the reason for this? Is this computer related or R version related? I downloaded the

Hi R-users! I am trying to learn how to use the glmpath package. I have a dataframe like this > dim(data) [1] 605 109 and selected the following > response <- data[,1] > features<-as.matrix(data[,3:109]) > mymodel <- glmpath(features,response, family = binomial) Error in if (lambda <= min.lambda) { : missing value where TRUE/FALSE expected Reading the glmpath pdf, I

Hello, I'm having trouble getting the igraph package to load on Solaris. I can get igraph to compile and install from a local directory without issues using simply "R CMD INSTALL igraph", however "library(igraph)" from within R gives the following errors: Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library

Hello, I''m having problems compiling Ruby 1.8.5 on a 64-bit Intel machine running Solaris 10 U3. I''m using Sun Studio 12 for building an optimized package for our company. We''re not interested in coolstack (from Sun used with GCC). Any help below would be appreciated: root@host # uname -an SunOS host 5.10 Generic_125101-08 i86pc i386 i86pc Solaris root@host # isainfo

Dear R-HELP, We are compiling R-2.12.1 for 64 bits onto a Sun Sparc machine below: SunOS 5.10 Generic_142900-13 sun4u sparc SUNW,Netra-T12 The source was compiled successfully with "gcc version 3.4.6" by using the default configuration and has produced R-2.12.1 in 32 bits. Compiling R-2.12.1 for the 64 bits by configuring the config.site file as follow: CC="gcc -m64"

On 03/02/2010, at 10:16 PM, Anton Korobeynikov wrote: > Hello, Nathan > >> I've put together some preliminary patches to add frontend support for the sparcv9-* subtarget (ie 64-bit SPARC), modelled on the corresponding x86-64 code - do these look reasonable for inclusion? This doesn't address the codegen side of things yet (isel falls over when trying to actually emit

We have recently upgraded to Solaris 10 on a couple of sparc machines with the usual mildly mysterious consequences for library locations, etc, etc. I've managed to configure R 2.1.0 for a 64 bit version with: R is now configured for sparc-sun-solaris2.10 Source directory: . Installation directory: /usr/local C compiler: gcc -m64 -g -O2 C++

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA256 configure:17280: result: no configure:17300: checking for mblen configure:17356: gcc -o conftest -O3 -m64 -mtune=native -pipe -Wall -Wpointer-arith -Wuninitialized -Wsign-compare -Wformat-security -Wsizeof-pointer-memaccess -Wno-pointer-sign -Wno-unused-result -fno-strict-aliasing -D_FORTIFY_SOURCE=2 -ftrapv -fno-builtin-memset

I was looking forward to trying this out, given the puported Solaris support. However, in attempting to install the prereqs for dashboard, I hit a major snag. My main server uses the blastwave MySQL install, which is a 64-bit version of MySQL 5. mysql Ver 14.12 Distrib 5.0.90, for pc-solaris2.10 (x86_64) Apparently the mysql bindings for ruby don''t like the 64-bit version. Output of

We have uploaded to CRAN a new version of glmpath, a package which fits the L1 regularization path for generalized linear models. The revision includes: - coxpath, a function for fitting the L1-regularization path for the Cox ph model; - bootstrap functions for analyzing sparse solutions; - the ability to mix in L2 regularization along with L1 (elasticnet). We have also completed a report that

We have uploaded to CRAN a new version of glmpath, a package which fits the L1 regularization path for generalized linear models. The revision includes: - coxpath, a function for fitting the L1-regularization path for the Cox ph model; - bootstrap functions for analyzing sparse solutions; - the ability to mix in L2 regularization along with L1 (elasticnet). We have also completed a report that

The problem first appeared in R 2.6.1 and is still there in R 2.6.2 On Windows running R CMD check command for glmpath package fails. The reason seems to be that when R is running the examples file (glmpath-Ex.R), it skips about 50 lines and as a result gives a syntax error. I'm working with a modified version of the CRAN glmpath 0.94. My version happens to give a more clear example of a

I'm trying to do LASSO in R with the package glmpath. However, I'm not sure if I am using the accompanying prediction function *predict.glmpath()* correctly. Suppose I fit some regularized binomial regression model like so: library(glmpath);load(heart.data);attach(heart.data); fit <- glmpath(x, y, family=binomial) Then I can use predict.glmpath() to estimate the value of the

We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit the

We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit the

Hi all, I've put together some preliminary patches to add frontend support for the sparcv9-* subtarget (ie 64-bit SPARC), modelled on the corresponding x86-64 code - do these look reasonable for inclusion? This doesn't address the codegen side of things yet (isel falls over when trying to actually emit 64-bit code), but at least bitcode generation looks correct now. Tested on

I am trying to get a basic apache 2.0.55 + mongrel + rails app configuration setup and i keep getting this: # mongrel_rails /usr/local/lib/ruby/gems/1.8/gems/mongrel-0.3.12.4/lib/mongrel/rails.rb:28: uninitialized constant Mongrel::HttpHandler (NameError) from /usr/local/lib/ruby/site_ruby/1.8/rubygems/custom_require.rb:21:in `require'' from

Hi, perhaps you can help me to find out, how to find the best Lambda in a LASSO-model. I have a feature selection problem with 150 proteins potentially predicting Cancer or Noncancer. With a lasso model fit.glm <- glmpath(x=as.matrix(X), y=target, family="binomial") (target is 0, 1 <- Cancer non cancer, X the proteins, numerical in expression), I get following path (PICTURE