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copyright

Keywords: general
MacAnova is conceived and programmed by Gary W. Oehlert and Christopher
Bingham, Department of Applied Statistics, University of Minnesota, and
is Copyright (C) 1994 - 2001 by them. Their e-mail addresses are
kb@stat.umn.edu and gary@stat.umn.edu.

MacAnova is distributed under the terms of the GNU Public License,
Version 2 (see file COPYING distributed with MacAnova).

Briefly, this means that MacAnova may be freely copied and distributed
and the source is available.  The source will be available by anonymous
ftp or other equivalent retrieval from stat.umn.edu.  Any changes others
make to the source must be clearly marked.

There is no warranty of any kind for MacAnova, either expressed or
implied.  MacAnova is distributed "as is".  See file COPYING for a more
complete statement.

The MacAnova WWW home page is
  http://www.stat.umn.edu/macanova/macanova.home.html
Executable versions of the Macintosh, DOS and Windows versions are
available there, along with source and Portable Document Format (PDF)
versions of the User's Guide and other documentation.  An up-to-date
mirror of these files is maintained by statlib at
  http://lib.stat.cmu.edu/

Reports of bugs should be emailed to kb@stat.umn.edu.

The Macintosh version uses TransSkel 3.12, a transportable Macintosh
application skeleton placed in the public domain by Paul Dubois
(dubois@primate.wisc.edu).

The extended memory MSDOS version (DJGPP) is compiled using a version of
Gnu gcc developed and copyrighted by D. J. Delorie (DJGPP) and
distributed under the terms of the GNU Public License.  Starting with
MacAnova 4.04, version 2 of this compiler has been used.  Source and
executable for DJGPP can be retrieved via anonymous ftp from
oak.oakland.edu (in pub/simtelnet/gnu/djgpp); modifications to the DJGPP
library for its use in MacAnova here can be retrieved via anonymous ftp
from umnstat.stat.umn.edu.

The Windows/Motif versions make use of the wxWin cross-platform
windowing interface developed Dr. Julian Smart, Artificial Intelligence
Applications Institute, The University of Edinburgh.  wxWin is Copyright
(c) 1995 Artificial Intelligence Applications Institute.  The WxWin home
page is
  http://web.ukonline.co.uk/julian.smart/wxwin/ .

Currently both the Windows and Motif versions are based on version 1.68
of wxWin, a copy of which is available through the MacAnova home page.

Plotting is done using a modification of GNUplot, Copyright (C) 1986,
1987 Thomas Williams, Colin Kelley.

The Unix/Linux version and the extended memory DOS version (DJGPP) allow
command line editing and history maintenance using the GNU Readline
Library, Copyright (C) 1988, 1991 Free Software Foundation, Inc.,
distributed under the terms of the GNU public license.  A compressed tar
archive of version 2.0 (used in the Unix/Linux version) is available
through the MacAnova home page.  The version used in the DOS DJGPP
version was included with the source for gdb4.12 found on
ftp://oak.oakland.edu/ which has been reorganized since we retrieved it.

Included in MacAnova's distribution are modified translations from
Fortran to C of the following programs written by others.

  Program screen and related subroutines for computing regressions by
  leaps and bounds by G.M.Furnival and R.W.Wilson supplied by Sanford
  Weisberg.  See their paper, Regression by Leaps and Bounds, Techno-
  metrics 16 (1974) 499-511.

  Subroutines rebak, reduc, rsg, tql2, tqlrat, tred1, tred2, svd,
  tridib, and tinvit from the Eispack library.

  Subroutines dchdc, dgeco, dgedi, dgefa, dgesl, and dqrdc from the
  Linpack library.

  Subroutines for computing mixed radix fast Fourier transforms written
  by Gordon Sande at the University of Chicago circa 1968.

  Program hc and related subroutines for computing hierarchical cluster
  analysis by F. Murtagh, retrieved from statlib.

  Subroutines for making stem and leaf displays from the book ABCs of
  EDA by David Hoaglin and Paul Velleman, Duxbury 1981.

  Subroutines to compute the roots of real polynomials from Algorithm
  493 published in TOMS retrieved from netlib.

  Code to compute the cumulative normal adapted from W. J. Kennedy and
  J. E. Gentle, Statistical Computing, Marcel Dekker, 1980, pp 90-92,
  which is based on W. J. Cody, Rational Chebyshev approximations for
  the error function, Math. Comp 23 (1969) 631-637.

  Code to compute the inverse of a normal distribution from Algorithm AS
  111 by J.D. Beasley and S. G. Springer, Appl. Statist. 26 (1977),
  118-121 retrieved from statlib.

  Code to compute the inverse Student's t-distribution from CACM
  Algorithm 396, by G. W. Hill retrieved from netlib.

  Code to compute the (central) Beta distribution from a subroutine of
  W.  Fullerton, Los Alamos, based on Bosten and Battiste, Remark on
  Algorithm 179, CACM 17 (1974) p. 153

  Code to compute the inverse Beta distribution from Algorithm AS 109 by
  G.  W. Cran, K. J. Martin and G. E. Thomas, Appl. Statist. 26 (1977),
  111-114 retrieved from statlib.

  Code to compute the non-central Beta distribution from Algorithm AS
  226 by R. V. Lenth, Appl. Statist. 36 (1987) 241-244, incorporating
  changes by H. Frick, Appl. Statist. 39 (1990) 311-12, retrieved from
  statlib

  Code to compute the gamma and chi-squared cumulative distributions
  from Algorithm AS 91 by D. J. Best and D. E. Roberts,
  Appl. Statist. 24 (1975), 385-388, incorporating revisions by
  B. L. Shea, Appl. Statist. 40 (1991), 233-235), retrieved from
  statlib.

  Code to compute the non-central chi-squared cumulative distribution
  from Algorithm AS 275 by Cherng G. Ding, Appl. Statist. 24 (1992),
  478-482, retrieved from statlib.

  Code to compute the non-central Student's t cumulative distribution
  from Algorithm AS 243 by Russell V. Lenth,  Appl. Statist. 38 (1989),
  185-189, retrieved from statlib.

  Code to compute the cumulative distribution function and its inverse
  for the Studentized range from Algorithm AS 190 by R. E. Lund and
  J. R. Lund, Appl. Statist. 32 (1983) 204-210, incorporating
  corrections by Lund and Lund, Appl. Statist. 34 (1985) 104 and
  I. D. Hill, Appl. Statist. 36 (1987) 119, retrieved from statlib.

  Code for a combined uniform pseudo-random number generator for 32 bit
  machines in P. L'Ecuyer 1988 Comm. ACM, retrieved from netlib.

  Code implementing the Singleton quicksort algorithm (Comm. ACM
  Algorithm 347) adapted from ssort.f in cmlib.

  Code computing the cumulative distribution for Dunnett's t was adapted
  from Algorithm AS 251 by C. W. Dunnett, Appl. Statist. 38 (1989)
  564-579 incorporating a correction by C. W. Dunnett, Appl. Statist. 42
  (1993) p. 709, and subroutine mvstud, also by Dunnett, that is part of
  the AS 251 distribution from statlib.

  Code generating a pseudo-random Poisson variable adapted from a
  Fortran program in C. D. Kemp and W. A. Kemp, Poisson random
  variate generation, Appl. Statist. 40 (1991) 143-158.

  Code generating a pseudo-random binomial variable adapted from
  Algorithm 678, Transactions on Math. Software 15, 394-397 by Voratas
  Kachitvichyanukul and Bruce Schmeiser.

  Code implementing varimax rotation from subroutine varmx supplied by
  Douglas Hawkins (doug@stat.umn.edu).

  Code implementing k-means clustering from subroutine trwcla supplied
  by Douglas Hawkins (doug@stat.umn.edu).

  Code used to compute inverses to cumulative distributions from
  subroutine fsolve supplied by Douglas Hawkins (doug@stat.umn.edu).  It
  is used by invchi() to compute the inverse of non-central chi-squared
  and by invdunnett() to compute probability points of Dunnett's t.

Certain macros are also based on Fortran code:
  Macro levmar in file Arima.mac is based on a Fortran program of Ken
  Brown.  See Brown,K.,M. and Dennis,J.,E., Derivative free analogues of
  the Levenberg-Marquardt and Gauss algorithms for nonlinear least
  squares approximation.  Numerische Mathematik, Vol. 18, pp. 289-297
  (1972)

  Macro neldermead in file Math.mac is based on Fortran subroutine MINIM
  by D. E. Shaw, CSIRO, Division of Mathematics & Statistics, with
  amendments by R. W. M. Wedderburn, Rothamsted Experimental Station,
  and Alan Miller, CSIRO, Division of Mathematics & Statistics.  See
  also Nelder & Mead, The Computer Journal 7 (1965), 308-313.  MINIM was
  retrieved from statlib.


Gary Oehlert 2003-01-15