similar to: pls 2.3.0 released

Version 1.1-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Version 1.1-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Version 2.0-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Version 2.0-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Bj??rn-Helge Mevik writes: > > tools to make it easy to convert R packages to S-PLUS. > > Not the other way around as well? Actually, we'll be discussing tools to make packages in general that can work in both S-PLUS and R, and also how to make some S-PLUS-only libraries available in an open-source environment as well as working with package authors to port R packages to work in

On Mon, Aug 24, 2015 at 11:29 AM, Bj?rn-Helge Mevik <b.h.mevik at usit.uio.no> wrote: > arnaud gaboury <arnaud.gaboury at gmail.com> writes: > >> - Intel MKL: this is part of Intel Parallel Studio and is a paid >> software. Now, there is the MKL package distributed by >> Revolutionanalytics, but I am not certain how this can be distributed >> for free. Is

On Fri, Sep 4, 2015, 9:24 AM Bj?rn-Helge Mevik <b.h.mevik at usit.uio.no> wrote: arnaud gaboury <arnaud.gaboury at gmail.com> writes: > After a few days of reading and headache, I finally gave a try at > building R from source with Intel MKL and ICC. Documentation and posts > on this topic are rather incomplete, sometime fantasist et do not give > much explanations about

Margarida Soares <margaridapmsoares at gmail.com> writes: > Thanks for your reply on pls! > I have tried to do a correlation plot but I get the following group of > graphs. Any way of having only 1 plot? > This is my script: > > corrplot(plsrcue1, comp = 1:4, radii = c(sqrt(1/2), 1), identify = FALSE, > type = "p" ) "Correlation loadings" are the

Version 1.0-3 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Version 1.2-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Version 1.2-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Version 1.2-1 of the pls package is now available on CRAN. This is mainly a bugfix-release. If you fit multi-response models, you are strongly engouraged to upgrade! The main changes since 1.2-0 are - Fixed bug in kernelpls.fit() that resulted in incorrect results when fitting mulitresponse models with fewer responses than predictors - Changed default radii in corrplot() - It is now

Version 1.2-1 of the pls package is now available on CRAN. This is mainly a bugfix-release. If you fit multi-response models, you are strongly engouraged to upgrade! The main changes since 1.2-0 are - Fixed bug in kernelpls.fit() that resulted in incorrect results when fitting mulitresponse models with fewer responses than predictors - Changed default radii in corrplot() - It is now

Version 2.1-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls, wide kernel pls, and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and

Version 2.1-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls, wide kernel pls, and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and

Version 1.0-3 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

I'm using R for calculations and plots in a statistical paper. Is there a canonical reference to use in the bibliography? (Or is it not common to acknowledge computer programs in journal papers?) -- Bj?rn-Helge Mevik <bhm at math.uio.no> -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

I'm writing a couple of related functions, and I'd like to generate a (single) .R file (containing the function definitions), and separate .Rd files (documenting each function). Would this be possible with Sweave/noewb? Has anyone tried something along this idea? -- Regards, Bj?rn-Helge Mevik

I haven't been able to connect to http://bugs.r-project.org the last few days. Is there a problem with the site (or am I having a problem :-) ? -- Bj??rn-Helge Mevik

Dear developeRs, I've searched the documentation, FAQ, and mailing lists, but haven't found the answer(*) to the following: When should one specify LazyLoad, LazyData, and ZipData? And what is the default if they are left unspecified? (*)Except that 1) If the package you are writing uses the methods package, specify LazyLoad: yes, and 2) The optional ZipData field controls whether the