similar to: Package np update (0.30-6) adds nonparametric entropy test functionality...

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 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 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, An updated version of the np package has recently been uploaded to CRAN (version 0.20-0). Version 0.20-0 is documented in Tristen Hayfield and Jeffrey S. Racine (2008). Nonparametric Econometrics: The np Package. Journal of Statistical Software 27(5). URL http://www.jstatsoft.org/v27/i05/ and also in a vignette (vignette("np",package="np")). There is also a FAQ

Dear R users, An updated version of the np package has recently been uploaded to CRAN (version 0.20-0). Version 0.20-0 is documented in Tristen Hayfield and Jeffrey S. Racine (2008). Nonparametric Econometrics: The np Package. Journal of Statistical Software 27(5). URL http://www.jstatsoft.org/v27/i05/ and also in a vignette (vignette("np",package="np")). There is also a FAQ

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

Greetings. For R gurus this may be a no brainer, but I could not find pointers to efficient computation of this beast in past help files. Background - I wish to implement a Cramer-von Mises type test statistic which involves double sums of max(X_i,Y_j) where X and Y are vectors of differing length. I am currently using ifelse pointwise in a vector, but have a nagging suspicion that there is a

Hi, and thanks in advance for your time. Background - I am working on a package and wish to have a routine's progress reported. The routine can take some time, and I would like to inform the user about the routine's progress. I have scoured the archives but to no avail, so would like to solicit input from this list. I am successfully using cat("\rBootstrap replication ", i,

Dear R users, I have a problem for which my objective function depends on both discrete and continuous arguments. The problem is that the number of combinations for the (multivariate) discrete arguments can become overwhelming (when it is univariate this is not an issue) hence search over the continuous arguments for each possible combination of the discrete arguments may not be feasible. Guided

Hi. I have recently noticed that when using boxplot with outline=FALSE, the default ylim (xlim if horizontal=TRUE) might be improved on. The default can result in much wasted display and hard to read plots. A simple snippet of test code is given below that illustrates the issue along with a suggested improvement. Thanks ever so much for your wonderful and ongoing contributions to the open source

Hi. I have been using the splines package for my work, in particular, the bs() function and associated predict() method. I now find myself in need of the derivatives of this beast. In the man page for predict.bSpline I found `predict(object, x, nseg=50, deriv=0, ...)' but alas this is not implemented (deriv= is ignored). I contacted the package maintainers who were most helpful. Bill

Hi. Background - I am working with a dataset involving around 750K observations, where many of the variables (8/11) are unordered factors. The typical model used to model this relationship in the literature has been a simple linear additive model, but this is rejected out of hand by the data. I was asked to model this via kernel methods, but first wanted to play with the parametric

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

Full_Name: Jeffrey Racine Version: 2.6.1 and previous... OS: FreeBSD Submission from: (NULL) (130.113.139.86) Hi. I almost feel bad reporting this, but here goes. The summary() for lm() (and possibly others?) uses a capitalized `S' in Multiple R-Squared but a lowercase `s' in Adjusted R-squared. For instance, Residual standard error: 0.5608 on 202 degrees of freedom Multiple