Bayesian Model Averaging Library
Extract a Model from a bma Object
Coefficients of the Best Models
Converting Binary Code to and from Hexadecimal Code
Class "bma"
BMS: Bayesian Model Averaging Library
Bayesian Model Sampling and Averaging
Concatenate bma objects
Posterior Inclusion Probabilities and Coefficients from a 'bma' Object
Coefficient Marginal Posterior Densities
Gaussian Hypergeometric Function F(a,b,c,z)
OLS Statistics for the Full Model Including All Potential Covariates
Posterior Density of the Shrinkage Factor
Class "gprior"
Plot Signs of Best Models
Tests for a 'bma' Object
Log Predictive Score
Class "mprior"
Plot Posterior Model Size and Model Probabilities
Compare Two or More bma Objects
Plot Convergence of BMA Sampler
Plot Model Size Distribution
Posterior Model Probabilities
Posterior Model Probability for any Model
Posterior Variance and Deviance
Predictive Densities for bma Objects
Predict Method for bma Objects
Predict Method for zlm Linear Model
Printing topmod Objects
Extract Quantiles from 'density' Objects
Summary Statistics for a 'bma' Object
Summarizing Linear Models under Zellner's g
Class "topmod"
Topmodel Object
Model Binaries and their Posterior model Probabilities
Variable names and design matrix
Variable names and design matrix
Class "zlm"
Bayesian Linear Model with Zellner's g
Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods.