[R-pkgs] np 0.30-1 (nonparametric kernel smoothing methods for mixed data types) is available on CRAN...

[User Jracine Jeffrey S. Racine]

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, and ordered factor data types. We would like to
gratefully acknowledge support from  the Natural Sciences and
Engineering Research Council of Canada (NSERC:www.nserc.ca), the Social
Sciences and Humanities Research Council of Canada (SSHRC:www.sshrc.ca),
and the Shared Hierarchical Academic Research Computing Network
(SHARCNET:www.sharcnet.ca).

License: GPL
 
Note that version 0.30-0 and 0.30-1 provide some much needed
functionality and bug fixes to the package.

Changes from Version 0.30-0 to 0.30-1 [29-Jan-2009]

* predict now supports bandwidth, density, distribution, conbandwidth,
  condensity, and condistribution objects

* Consistently allow predictions for categorical values outside of
  support of training data

  Note that predictions based upon unconditional density objects
  defined over categorical variables that lie outside the support of
  the training data may no longer be true probabilities (i.e., as
  defined over the training data and the extended/augmented support --
  their sum may exceed one) and may therefore require renormalization
  by the user 

* Fixed a numerical issue which could hinder npregbw()'s cross
  validation with higher-order kernels

* Default nmulti in npplregbw() is now set correctly

* Fixed a bug with the ridging routine in npscoefbw(), added ridging to
  npscoef

* Fixed trivial i/o issue with "Multistart 1 of" using npscoefbw()

Changes from Version 0.20-4 to 0.30-0 [15-Jan-2009]

* Added basic user-interrupt checking for all underlying C code so
  that either <Ctrl-C> (Rterm) or the `STOP' icon (Rgui) will
  interrupt all running processes. This has a number of desirable side
  effects in addition to being able to interrupt C-based processes
  including i) R no longer showing up as `not responding' under the
  task manager (Windows) or the activity monitor (Mac OS X) and ii)
  buffered output now being correctly displayed when using Rgui under
  Windows and Mac OS X

  Note that repeated interruption of large jobs can reduce available
  memory under R - if this becomes an issue (i.e., you get a `cannot
  allocate...' error under R) simply restart R (i.e., exit then run a
  fresh R session)

* Added a function npseed() that allows the user to set/reset the
  random seed for all underlying C routines

* Fixed a bug that caused npplregbw() to ignore any kernel options
  for the regression of y on z

* Refined certain constants used in the normal-reference density
  bandwidth rule for increased accuracy

* Moved from using the maximum likelihood estimate of variance
  throughout to the degrees of freedom corrected estimate (all
  variance estimates now change by the factor (n-1)/n)

Feedback, comments, bug reports, and suggestions for improvement are
always welcome.

Best regards,

Jeff Racine & Tristen Hayfield

-- 
Professor J. S. Racine         Phone:  (905) 525 9140 x 23825
Department of Economics        FAX:    (905) 521-8232
McMaster University            e-mail: racinej at mcmaster.ca
1280 Main St. W.,Hamilton,     URL:
http://www.economics.mcmaster.ca/racine/
Ontario, Canada. L8S 4M4

`The generation of random numbers is too important to be left to
chance.'