R packages - Dec 2007 - Update of the np package (version 0.14-1)

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
(http://www.jstatsoft.org) for those might be interested.

A much more thorough treatment of the subject matter can be found in Li,
Q. and J. S. Racine (2007), Nonparametric Econometrics: Theory and
Practice, Princeton University Press, ISBN: 0691121613 (768 Pages) for
those who might be interested
(http://press.princeton.edu/titles/8355.html)

Information on the np package:

This package provides a variety of nonparametric (and semiparametric)
kernel methods that seamlessly handle a mix of continuous, unordered,
and ordered factor datatypes. 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).

Changes from version 0.13-1 to 0.14-1:

* now use optim rather than nlm for minimisation in single index and
smooth coefficient models
* fixed bug in klein-spady objective function
* regression standard errors are now available in the case of no
  continuous variables
* summary should look prettier, print additional information
* tidied up lingering issues with out-of-sample data and conditional
modes
* fixed error when plotting asymptotic errors with conditional densities
* fixed a bug in npplot with partially linear regressions and 
plot.behavior='data' or 'plot-data'
* maximum default number of multistarts is now set to 5
* least-squares cross-validation of conditional densities uses a new,
 faster algorithm
* new, faster algorithm for least-squares cross-validation for both 
local-constant and local linear regressions.
   The estimator has changed somewhat: both cross-validation and
   the estimator use a method of shrinking towards the local constant
   estimator rather than the standard ridge approach that shrinks
   towards zero
* optimised smooth coefficient code, added ridging
* fixed bug in uniform CDF kernel
* fixed bug where npindexbw would ignore bandwidth.compute = FALSE and
   compute bandwidths when supplied with a preexisting bw object
* now can handle estimation out of discrete support.
* summary would misreport the values of discrete scale factors which
   were computed with bwscaling = TRUE

We are grateful to John Fox, Achim Zeilies, Roger Koenker, and numerous
users for their valuable feedback which resulted in an improved version
of the package.

-- Jeffrey 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'

_______________________________________________
R-packages mailing list
R-packages at r-project.org
https://stat.ethz.ch/mailman/listinfo/r-packages