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 2/June/2008].
• An R version of the software is available here. The R software is available at no charge here.
• An WinBUGS version of the software is available here. The winBUGS software is available at no charge 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", submitted. Paper available from the authors upon request.

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 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.

Bayesian Model Averaging (BMA)

BMA in R

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.

BMA in S-Plus

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).

BMA Homepage

Software and references on Bayesian Model Averaging

Model selection for geostatistical models

• 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 Science to Achieve Results (STAR) Program, CR - 829095. See link below.

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>

SIMSEL

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].