HOAHigherOrder AsymptoticsCopyright cond, marg, sampling © 2000 Alessandra R. Brazzale Copyright nlreg © 2000 Ruggero Bellio & Alessandra R. Brazzale 
A library of SPLUS functions (and data) for higherorder asymptotic inference written by Alessandra R. Brazzale. 
This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 2 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. Please send any comments, suggestions or errors reported to A. R. Brazzale. 
The SPLUS library HOA contains four sections.
They can be obtained from here. Instructions how to install
the library are given here.
cond v.1.0
A short presentation is given in:
Approximate conditional inference in logistic and loglinear models. Journal of Computational and Graphical Statistics, Vol. 8, 653661. Winner of the "1998 ASA Statistical Computing Section Student Paper Competition".
marg v.1.0
nlreg v.1.0 (joint with Ruggero Bellio)
Winner of the "14th IWSM Best Student Poster Competition". Theory and applications can be found in:
Likelihood Asymptotics: Applications in Biostatistics. (PostScript) Ph.D. Thesis, Department of Statistics, University of Padova. (Note: This is the online version. For the original manuscript, please contact the author.)
Higherorder asymptotics unleashed: Software design for nonlinear heteroscedastic models. Journal of Computational and Graphical Statistics, 12, 682697.
sampling v.1.1
(updated & bug fixed!)
Theory and example are given in:
Conditional simulation for regressionscale models. Journal of the Italian Statistical Society, 8, 101114
The library also contains some data sets. All of the major functions and the
data sets are fully documented with help files. The whole implementation is
described in:
Practical SmallSample Parametric Inference. (PostScript) Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne.

