orderplot
(Package: BMA) :
Orderplot of iBMA objects
This function displays a plot showing the selection and rejection of variables being considered in an iterated Bayesian model averaging variable selection procedure.
bic.surv
(Package: BMA) :
Bayesian Model Averaging for Survival models.
Bayesian Model Averaging for Cox proportional hazards models for censored survival data. This accounts for the model uncertainty inherent in the variable selection problem by averaging over the best models in the model class according to approximate posterior model probability.
bic.glm
(Package: BMA) :
Bayesian Model Averaging for generalized linear models.
Bayesian Model Averaging accounts for the model uncertainty inherent in the variable selection problem by averaging over the best models in the model class according to approximate posterior model probability.
plot.bicreg
(Package: BMA) :
Plots the posterior distributions of coefficients derived from Bayesian model averaging
Displays plots of the posterior distributions of the coefficients generated by Bayesian model averaging over linear regression, generalized linear and survival analysis models.
bicreg
(Package: BMA) :
Bayesian Model Averaging for linear regression models.
Bayesian Model Averaging accounts for the model uncertainty inherent in the variable selection problem by averaging over the best models in the model class according to approximate posterior model probability.
iBMA
(Package: BMA) :
Iterated Bayesian Model Averaging variable selection for generalized linear models, linear models or survival models.
This function implements the iterated Bayesian Model Averaging method for variable selection. This method works by making repeated calls to a Bayesian model averaging procedure, iterating through the variables in a fixed order. After each call to the Bayesian model averaging procedure only those variables which have posterior probability greater than a specified threshold are retained, those variables whose posterior probabilities do not meet the threshold are replaced with the next set of variables. The order in which the variables are to be considered is usually determined on the basis of the some measure of goodness of fit calculated univariately for each variable.
predict.bic.glm
(Package: BMA) :
Predict function for Bayesian Model Averaging for generalized
Bayesian Model Averaging (BMA) accounts for the model uncertainty inherent in the variable selection problem by averaging over the best models in the model class according to approximate posterior model probability. This function predicts the response resulting from a BMA generalized linear model from given data.
providing 
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