Empirical Bayes Variable Selection via ICM/M Algorithm

Documentation for package ‘icmm’ version 1.2

icmm-package	Empirical Bayes Variable Selection via ICM/M
get.ab	Hyperparameter estimation for 'a' and 'b'.
get.alpha	Hyperparameter estimation for 'alpha'.
get.beta	Obtain model coefficient without assuming prior on structure of predictors.
get.beta.ising	Obtain a regression coefficient when assuming Ising prior (with structured predictors).
get.pseudodata.binomial	Obtain pseudodata based on the binary logistic regression model.
get.pseudodata.cox	Obtain pseudodata based on the Cox's regression model.
get.sigma	Standard deviation estimation.
get.wpost	Estimate posterior probability of mixing weight.
get.wprior	Mixing weight estimation.
get.zeta	Local posterior probability estimation
get.zeta.ising	Local posterior probability estimation.
icmm	Empirical Bayes Variable Selection
initbetaBinomial	Initial values for the regression coefficients used in example for running ICM/M algorithm in binary logistic model
initbetaCox	Initial values for the regression coefficients used in example for running ICM/M algorithm in Cox's model
initbetaGaussian	Initial values for the regression coefficients used in example for running ICM/M algorithm in normal linear regression model
linearrelation	Linear structure of predictors
simBinomial	Simulated data from the binary logistic regression model
simCox	Simulated data from Cox's regression model
simGaussian	Simulated data from the normal linear regression model