Future Software for Statistical Models
This is a proposal for discussion and eventually for joint work on
software for statistical models.
The goal is to bring together people interested in as many as possible of the major current
research directions in this area, with the hope that we can design
software that provides broader, better designed, and more useful
support for those using statistical models.
The proposal is being made initially as part of the
Omega project for statistical
computing,
a joint research effort aimed generally at producing open software for
statistical computing.
Anyone with an interest in the topic is invited to contribute
suggestions and comments to the discussion.
A Mailing List
The mailing list omega-models@omegahat.net
has been
established to be forum for discussions of future directions in
software for statistical models.
Anyone interested in the topic is encouraged to join and contribute to
the list.
To join, send e-mail to omega-models-request@omegahat.net
, with "subscribe
" as the body of the
message.
On March 19-23, an informal meeting will be held in Vienna on
The Future of Statistical Computing.
Future directions for statistical models will be one of the sessions
at a that meeting.
The following topics are among those for discussion.
They are not intended to be at all restrictive, just some areas that
seem particularly interesting.
- 1.
- New classes of models: One way software for statistical
models has grown is by introducing new types of models, either
extending our abilities into new areas or integrating various models
under a new, general approach. New or newly extended types of
models from the last five years or
so include mixed-effects models, spatial
models, and survival models.
New classes of models challenge us to provide good new software. In
addition, bringing the new classes of models together with each
other and with older classes of models can give users easier access.
- 2.
- Bayesian techniques: Great advances have been made in
recent years in the application of Bayesian inference about
statistical models. The advances have introduced both large areas
of new computational techniques and new conceptual frameworks for
specifying models, for example using graphical model descriptions.
Bringing these techniques together with other aspects of software
for statistical models would be of great value to users.
- 3.
- Distributed computing for models: software
on the internet and the use of languages that can take advantage
of distributed software, such as Java, open many new opportunities.
These range from enhanced user interfaces to algorithmic techniques
such as high-level parallelism in fitting models.
New software organization for distributed computing will be needed.
Along the way, we will want to incorporate exisiting software as
much as we can.
Omega project for statistical
computing
Last modified: Wed Feb 17 10:33:22 EST 1999