Jennifer Hoeting: Statistical Software
Background information
- Acrobat
Reader: If you need the plug-in to read pdf
files (Acrobat Reader), click here.
- The
R software package is available at no charge here.
- The
winBUGS software is available at no charge here.
·
Some of the software listed below is available
in the form of shar files.
Abundance estimation for unknown number of
marked individuals
- Software to implement
MCMC to estimate parameters for Bayesian model for estimating abundance
when sighting data are acquired from distinct sampling occasions without
replacement, but the exact number of marked individuals is unknown.
- This code was written by
Brett McClintock [last updated 18/Nov/2008].
- An R version of the
software is available here.
- An
WinBUGS version of the software is available here.
- The paper that describes
this methodology: B. T. McClintock and J. A. Hoeting, “Bayesian
analysis of abundance for binomial sighting data with unknown number of
marked individuals,” Ecological
and Environmental Statistics, DOI 10.1007/s10651-009-0109-0.
AUTOLOGIT
- Software to perform
Bayesian estimation for an autologistic model
with covariates.
- S-Plus
Code, C++ Code, and manual for C++ Code
- 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.
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 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.
Bayesian inference for shape restricted
generalized linear models
·
This R code performs
Bayesian MCMC estimation for and Inference for Generalized Partial Linear
Models Using Shape-Restricted Splines.
·
The code was written by Mary Meyer and Amber Hackstadt.
·
The paper that describes this methodology: Meyer, M.
C., J. A. Hoeting, and A. Hackstadt (2009). “Bayesian Estimation and Inference for Generalized Partial
Linear Models Using Shape-Restricted Splines.”
Bayesian Model Averaging (BMA)
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 code is now
part of the bma package in R (see above).
Software and
references on Bayesian Model Averaging
Model selection for geostatistical
models
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 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.
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- 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].