sm                    package:sm                    R Documentation

_T_h_e _s_m _p_a_c_k_a_g_e: _s_u_m_m_a_r_y _i_n_f_o_r_m_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

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

_D_e_t_a_i_l_s:

     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.

_M_a_i_n _F_e_a_t_u_r_e_s:

     The functions in the package 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 constructed for 1-, 2- and
     3-dimensional data. Nonparametric regression for continuous data
     can be constructed with one or two covariates, and a variety of
     tests can be carried out.  Several other data types can be
     handled, including survival data, time series, count and binomial
     data.

_F_u_n_c_t_i_o_n_s:

     The main functions are 'sm.density' and 'sm.regression'; other
     functions intended for direct access by the user are: 'h.select',
     '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
     these.

_S_c_r_i_p_t_s:

     The function 'sm.script' is used to run a set of examples (called
     scripts) presented in the book quoted below. These scripts are
     associated with the package but the package can be used
     independently of them.  The scripts are generally based on the
     functions of the package 'sm', but a few of them make used of the
     'gam' package.

_R_e_q_u_i_r_e_m_e_n_t_s:

     R version >= 2.1.0. Packages 'gam' and 'akima' are used by some of
     the scripts launched via 'sm.script', but they are not used by the
     functions of this package.

_V_e_r_s_i_o_n:

     This is version 2.1. The most recent version of the package can be
     obtained from either of the web pages: <URL:
     http://www.stats.gla.ac.uk/~adrian/sm>, <URL:
     http://azzalini.stat.unipd.it/Book_sm>

_D_e_t_a_i_l_s:

     The book by Bowman and Azzalini (1997) provides more detailed and
     background information.  Algorithmic aspects of the software are
     discussed by Bowman & Azzalini (2003).  Differences between the
     first version of the package, described in the book, and the
     current one are summarized in the file 'history.txt' which is
     distributed with the package.

_A_c_k_n_o_w_l_e_d_g_e_m_e_n_t_s:

     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 package, with the exception of the functions for
     three-dimensional density estimation, written by Stuart Young.  We
     also thank Luca Scrucca who made useful comments and who has
     ported the software to XLispStat. We are particularly grateful to
     Brian Ripley for substantial help in the production of
     installation files, the creation of MS-Windows versions, initial
     porting of the software from S-Plus to R and for maintaining the
     package on CRAN for several years.

_L_i_c_e_n_c_e:

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

_A_u_t_h_o_r(_s):

     Adrian Bowman (Dept Statistics, University of Glasgow, UK) and
     Adelchi Azzalini (Dept Statistical Sciences, University of Padua,
     Italy). Please send comments, error reports, etc. to the authors
     via the web pages mentioned above.

_R_e_f_e_r_e_n_c_e_s:

     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. (2003). Computational aspects of
     nonparametric smoothing with illustrations from the 'sm' library.
     _Computational Statistics and Data Analysis_, *42*, 545-560.

