similar to: Package np version 0.20-0 released to CRAN

Dear R users, A parallel implementation of the np package titled `npRmpi' is now available on CRAN. This package can take advantage of multiple core computing environments to reduce the run time associated with the methods contained in the np package. Kindly see the vignette for details and examples on modifying np code and running it in a parallel environment. You are requested to seek

Dear R users, A parallel implementation of the np package titled `npRmpi' is now available on CRAN. This package can take advantage of multiple core computing environments to reduce the run time associated with the methods contained in the np package. Kindly see the vignette for details and examples on modifying np code and running it in a parallel environment. You are requested to seek

Dear R users, An updated version of the np package has recently been uploaded to CRAN (version 0.14-1). The package is briefly described in a recent issue of Rnews (October, 2007, http://cran.r-project.org/doc/Rnews/Rnews_2007-2.pdf) for those who might be interested. A somewhat more detailed paper that describes the np package is forthcoming in the Journal of Statistical Software

Dear R users, An updated version of the np package has recently been uploaded to CRAN (version 0.14-1). The package is briefly described in a recent issue of Rnews (October, 2007, http://cran.r-project.org/doc/Rnews/Rnews_2007-2.pdf) for those who might be interested. A somewhat more detailed paper that describes the np package is forthcoming in the Journal of Statistical Software

Dear R users, Version 0.30-1 of the np package has been released and uploaded to CRAN. The np package provides nonparametric kernel smoothing methods for mixed data types. We encourage anyone using the package to upgrade to the latest version. Description: This package provides a variety of nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered,

Dear R users, Version 0.30-1 of the np package has been released and uploaded to CRAN. The np package provides nonparametric kernel smoothing methods for mixed data types. We encourage anyone using the package to upgrade to the latest version. Description: This package provides a variety of nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered,

Dear useRs, We are pleased to announce that versions 0.50-1 of the np package and its MPI-aware counterpart, the npRmpi package, are now available on CRAN: http://cran.r-project.org/web/packages/np/index.html http://cran.r-project.org/web/packages/npRmpi/index.html In these versions we add new functionality: - new methods for multivariate mixed-data bandwidth selection for unconditional and

Dear useRs, We are pleased to announce that versions 0.50-1 of the np package and its MPI-aware counterpart, the npRmpi package, are now available on CRAN: http://cran.r-project.org/web/packages/np/index.html http://cran.r-project.org/web/packages/npRmpi/index.html In these versions we add new functionality: - new methods for multivariate mixed-data bandwidth selection for unconditional and

Dear R users, Version 0.30-6 of the np package has been uploaded to CRAN. See http://cran.r-project.org/package=np Note that the cubature package is now required in addition to the boot package. The recent updates in 0.30-4 through 0.30-6 provides additional functionality in the form of five new functions that incorporate frequently requested nonparametric entropy-based testing methods to the

Dear R users, Version 0.30-6 of the np package has been uploaded to CRAN. See http://cran.r-project.org/package=np Note that the cubature package is now required in addition to the boot package. The recent updates in 0.30-4 through 0.30-6 provides additional functionality in the form of five new functions that incorporate frequently requested nonparametric entropy-based testing methods to the

Dear R users, A new package titled `np' is now available from CRAN. The package implements recently developed kernel methods that seamlessly handle the mix of continuous, unordered, and ordered factor datatypes often found in applied settings. The package also allows users to create their own nonparametric/semiparametric routines using high-level function calls (via the function npksum())

Dear R users, A new package titled `np' is now available from CRAN. The package implements recently developed kernel methods that seamlessly handle the mix of continuous, unordered, and ordered factor datatypes often found in applied settings. The package also allows users to create their own nonparametric/semiparametric routines using high-level function calls (via the function npksum())

Dear all, I am using np package in order to estimate a model with Klein and Spady estimator. To estimate the model I use KS <- npindexbw (xdat=X, ydat=Y, bandwidth.compute=TRUE, method="kleinspady", optim.maxit=10^3, ckertype="epanechnikov", ckerorder=2) and to estimate beta hats standard errors I use KSi <- npindex(KS, gradients=T, boot.num=300) vcov(KSi) This is

Hi all, I've been trying to run a model using R2winBUGS, and recurrently I get the message: "Error in FUN(X[[3L]], ...) : invalid to change the storage mode of a factor" My model is the following: sink("GLMM_Poisson.txt") cat(" model{ mu~dnorm(0,0.01) beta1~dnorm(-1,1) for(j in 1:nsite){ alpha[j]~dnorm(mu.alpha,tau.alpha) } mu.alpha~dnorm(0,0.01)

Dear R-users, I am fitting a kernel regression model of the form y ~ x1 + factor(x2) + factor(x3) and am using the function npregbw in the np-package to find the optimal bandwidths. My dataset is relatively large and the optimization takes quite long. When testing different specifications I have noticed that the optimal bw for x3 is always very close to zero (around 10^-12 or so). I am

Centos 5.1 documentation states that the supported ext3 filesystem limit is 16TB, yet I have a 9.5TB partition that is claimed to be too large: mke2fs 1.39 (29-May-2006) mke2fs: Filesystem too large. No more than 2**31-1 blocks (8TB using a blocksize of 4k) are currently supported. Am I missing something? > uname -a Linux fileserver.sharcnet.ca 2.6.18-53.1.6.el5 #1 SMP Wed Jan

Hello r-help, I am using the excellent np package to conduct a nonparametric kernel regression and am having some trouble plotting the results. I have 2 covariates, x1 and x2, and a continuous outcome variable y. I am conducting a nonparametric regression of y on x1 and x2. The one somewhat unusual feature of these data is that, to be included in the dataset, x1 must be at least as large as x2.

Dear R-helpers, First I apologize if my question is quite simple I have a large datasets which more 100 variables. For a research I need to change all name of variables with add one or more letters on each variables. For example, > data(Pima.tr) > Pima.tr[1:5,] npreg glu bp skin bmi ped age type 1 5 86 68 28 30.2 0.364 24 No 2 7 195 70 33 25.1 0.163 55 Yes 3 5

Dear list, I have been comparing the outputs of two packages for latent class regression, namely 'flexmix', and 'mmlcr'. What I have noticed is that the flexmix package appears to come up with a much better fit than the mmlcr package (based on logLik, AIC, BIC, and visual inspection). Has anyone else observed such behaviour? Has anyone else been successful in using the mmlcr

Q: When R does 'plot()' in a context that yields boxplots, is there a way to force it to draw something even if there are only 1 or two data in the category? I'd like for it to draw the data, perhaps using the outlier symbols. My code is (*** marks the line in question) is the following, for R-1.0.0: d <- read.table("nserc-results-pgsb", header=FALSE,