R: Empirical Bayes Variable Selection via ICM/M Algorithm

# Empirical Bayes Variable Selection via ICM/M Algorithm

## Documentation for package ‘icmm’ version 1.2

- DESCRIPTION file
- Package NEWS

## Help Pages

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