Jennifer Hoeting: Statistical Software
Some of the software listed below is available in the form of shar
files (a way of packaging a set of files). Click here for more
information on shar
files. If you need the plug-in to read pdf files (Acrobat
Reader), click here.
- Software to perform Bayesian estimation for an autologistic
model with covariates.
- S-plus code written by Jennifer Hoeting [last updated: 3/Jul/00]
[4/23/03: The Splus code needs revision as it uses Scompile
which is no longer available. ]
- C++ Code written by Greg Young [last updated: 22/Mar/01]. The manual
is a pdf file.
- R code to perform Bayesian model averaging (BMA) to
account for model uncertainty in
linear regression models, GLMs, and survival models. This code
was written by Adrian Raftery, Jennifer Hoeting, Chris Volinsky, Ian Painter, Ka Yee Yeung.
- S-Plus code
to perform Bayesian model averaging (BMA) to account for model
uncertainty in linear regression models. Written by Jennifer Hoeting
[last updated: 18/Apr/98]. This software is now part of the bma
package in R (see above).
- Software and references on Bayesian Model Averaging
- A set of XLISP-STAT functions to perform Bayesian Predictive
Simultaneous Variable and Transformation Selection for regression.
A criterion-based approach to model selection. This code was
written by Jennifer Hoeting [last updated:
13/Dec/96].
Autoregressive Models
for Capture-Recapture Data
-
This winBUGS code performs Bayesian estimation for
an AR(2) band recovery model.
-
The code was written by Devin Johnson [last updated: 1/May/02]. The
winBUGS software is available at no charge here.
-
The paper that describes this methodology:
Johnson, D. S. and J. A. Hoeting (2003) ``Autoregressive Models
for Capture-Recapture Data: A Bayesian Approach,''
Biometrics, 59:340-349.
Random Effects Graphical Regression Models for Multidimensional
Categorical Data
-
This winBUGS code comes in two parts: a saturated model (full dependence)
for the response and the covariate model.
The corresponding
data are the saturated model data (full dependence)
for the response and data for the covariate model data.
- The code was written by Devin Johnson [last updated: 15/August/2007]. The
winBUGS software is available at no charge here.
- The paper that describes this methodology:
Johnson, D. S., J. A. Hoeting, and B. S.
Fadely (2007),
"Random Effects Graphical Regression Models for Multidimensional
Categorical Data", submitted.
<\ul>
- Software to compute
AIC and MDL for geostatistical models for R. The code was written by
Andrew Merton [last updated: 2/April/04].
This project was supported by US EPA:
