Bayesian Model Selection Software

• ace.glm.
  Perform ACE transformation detection on Generalized Linear Models. Extension of ace.logit. Simon Byers (byers@stat.washington.edu) [13/Dec/96] (5k)

• ace.logit.
  Calculate and plot the ACE transformations for logistic regression (adapted from linear regression). [23/Jan/95] (4k)

• ar.robust.
  S-function to compute robust estimates of partial autocorrelation for AR processes (upto order 6). This function can also be used to obtain posterior probabilities for the competing AR models.
  Nhu Le (nhu@epi.terryfox.ubc.ca). [1/Mar/95] (99 k)

• bicreg.
  S function to do Bayesian model selection and accounting for model uncertainty in linear regression models, using the BIC approximation.
  Contributed by "Adrian E. Raftery" (raftery@stat.washington.edu) [17/Oct/94][23/Jan/95] (24 kbytes).

• bic.glm
  S function to do Bayesian model selection and accounting for model uncertainty in generalized linear models, using the BIC approximation. Written by Chris T. Volinsky
  
• bic.logit.
  S function to do logistic regression with Bayesian model selection and accounting for model uncertainty, using the BIC approximation.
  Contributed by "Adrian E. Raftery" (raftery@stat.washington.edu) [29/Nov/94] (30 kbytes)

• bic.surv
  S-Plus function to implement Bayesian Model Averaging for Cox Proportional Hazards Models, using the BIC Approximation. Includes a summary function.
  Contributed by Chris T. Volinsky (volinsky@thelma.stat.washington.edu) [1/Dec/95] (9 kbytes)

• bma.
  Splus function to do Bayesian model averaging (BMA) to account for model uncertainty in linear regression models.
  Contributed by Jennifer Hoeting (jah@lamar.stat.colostate.edu) [3/Apr/95] (24kbytes)

• glib.
  Bayesian generalized linear modeling, model comparison and accounting for model uncertainty. An S-PLUS function.
  Adrian Raftery, (raftery@stat.washington.edu). [17/May/93][18/May/93] (29 Kbytes).

• laplacpg.
  Fortran program used for Bayes factors in hierarchical models: calculates the Laplace approximation to the marginal likelihood for the Poisson-Gamma (negative binomial) regression model.
  Susan Rosenkranz (slr@biostat.washington.edu) [29/Mar/94] (252 kbytes)

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