SIMMS-package           SIMMS - Subnetwork Integration for Multi-Modal
                        Signatures
calculate.meta.survival
                        Fit a meta-analytic Cox proportional hazards
                        model to a single feature
calculate.network.coefficients
                        Calculate Cox statistics for input dataset
calculate.sensitivity.stats
                        Computes sensitivity measures
create.KM.plot          Plots Kaplan-meier survival curve for a given
                        risk grouping & survival params
create.classifier.multivariate
                        Trains and tests a multivariate survival model
create.classifier.univariate
                        Trains and tests a univariate (per subnetwork
                        module) survival model
create.sensitivity.plot
                        Plots sensitivity analysis for class label
                        dichotomization at supplied survtime cutoffs
create.survivalplots    Plots Kaplan-meier survival curves
create.survobj          Utility function for loading meta-analysis
                        lists
derive.network.features
                        Derive univariate features from pathway-derived
                        networks
dichotomize.dataset     Dichotomize a single dataset
dichotomize.meta.dataset
                        Dichotomize and unlist a meta-analysis list
fit.coxmodel            Fit a Cox proportional hazards model
fit.interaction.model   Cox model two features separately and together
fit.survivalmodel       Trains a multivariate survival model
get.adjacency.matrix    A utility function to convert tab delimited
                        networks file into adjacency matrices
get.chisq.stats         Applies survdiff function
get.program.defaults    A utility function to return the inst/
                        directory of the installed package and other
                        default settings
load.cancer.datasets    Load all cancer meta-analysis datasets
make.matrix             Utility function used by
                        'get.adjacency.matrix()'
pred.survivalmodel      Apply a multivariate survival model to
                        validation datasets
prepare.training.validation.datasets
                        Prepare training and validation datasets
