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sm (version 2.0-2)

sm: The library sm: summary information

Description

This library implements nonparametric smoothing methods described in the book of Bowman & Azzalini (1997)

Arguments

Main Features

The functions in the library use kernel methods to construct nonparametric estimates of density functions and regression curves in a variety of settings, and to perform some inferential operations.

Specifically, density estimates can be performed for 1-, 2- and 3-dimensional data. Nonparametric regresion for continuous data can be constructed with one or two covariates, and a variety of goodness-of-fit test for linear models can be carried out. Many other data types can be handled; these include survival data, time series, count and binomial data.

Functions

The main functions are sm.density and sm.regression; other functions intended for direct access by the user are: binning, sm.ancova, sm.autoregression, sm.binomial, sm.binomial.bootstrap, sm.poisson, sm.poisson.bootstrap, sm.options, sm.rm, sm.script, sm.sphere, sm.survival, sm.ts.pdf. There are undocumented functions which are called by the above ones.

REquirements

The library has been tested on S-plus 3.x, 4.0, 5.1

Version

You are using version 2 (November 2000). The most recent version of the library can be obtained from either of the WWW pages: http://www.stats.gla.ac.uk/~adrian/sm http://www.stat.unipd.it/~azzalini/Book_sm

Manual

There is no manual except for on-line documentation. The book by Bowman and Azzalini (1997) provides more detailed and background information. Algorithmic aspects of the software are discussed by Bowman & Azzalini (2001). Differences between the first version of the library and the current one are summarized in the file history.txt which is distributed with the library.

Acknowledgements

Important contributions to prototype versions of functions for some specific techniques included here were made by a succession of students; these include Stuart Young, Eileen Wright, Peter Foster, Angela Diblasi, Mitchum Bock and Adrian Hines. We are grateful for all these interactions. These preliminary version have been subsequently re-written for inclusion in the public release of the library, with the exception of the functions for three-dimensional density estimation, written by Stuart Young. We also thank Luca Scrucca for useful remarks and Brian Ripley for substantial help in the production of installation files, leading to much improved versions with respect to our original ones, and for tools to produce the MS-windows version starting from the Unix one.

Licence

This library and its documentation are usable under the terms of the "GNU General Public License", a copy of which is distributed with the package.

Details

Missing data are allowed; they are simply removed, togeter with the associated variates from the same case, if any.

Datasets of arbitrary size can be handled by the current version of sm.density, sm.regression and sm.ancova, using binning operations.

References

Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.

Bowman, A.W. and Azzalini, A. (2001). Computational aspects of nonparametric smoothing, with illustrations from the sm library. To appear.