sm                    package:sm                    R Documentation

_T_h_e _l_i_b_r_a_r_y _s_m: _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 library 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 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.

_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: `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.

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

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

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

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

_M_a_n_u_a_l:

     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.

_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 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.

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

     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.

_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 abovementioned WWW page.

_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. (2001). Computational aspects of
     nonparametric smoothing,  with illustrations from the `sm'
     library. To appear.

