analyze_dataset         analyze_dataset: Basically performs
                        preprocessing and then returns analyzed RNASeq
                        dataset (diff. expression) , i.e. the DESeq2
                        result whose p-values and baseMean statistics
                        can then be used with IHW
bh                      bh: Wrapper for Benjamini Hochberg
bonf                    bonf: Wrapper for Bonferroni
clfdr                   clfdr: Cai's local fdr based method
continuous_wrap         Benchmarking wrapper: Given a multiple testing
                        method, convert it so that it takes a
                        simulation object (see simulation function) and
                        a nominal level alpha as inputs
ddhf                    ddhf: Greedy independent filtering
du_ttest_sim            t-test simulation: Simulate rowwise t-tests
gbh                     gbh: Grouped Benjamini Hochberg
ihw_naive               IHW wrappers
lsl_pi0_est             LSL (Least-Slope) pi0 estimator
null_sim                Null simulation: Generate uniformly distributed
                        p-values and covariates
pretty_legend           helper function to create nice legends
run_evals               run_evals: Main function to benchmark FDR
                        methods on given simulations.
scott_fdrreg            scott_fdrreg: Wrapper for FDR regression
                        (https://github.com/jgscott/FDRreg)
storey_qvalue           storey_qvalue: Wrapper for Storey's qvalue
                        package
stratified_bh           stratified_bh: Stratified Benjamini Hochberg
tst_pi0_est             TST (Two-Step) pi0 estimator
wasserman_normal_prds_sim
                        Normal PRDS simulation: Covariate is effect
                        size under alternative, there are latent
                        factors driving PRDS correlations among
                        hypotheses
wasserman_normal_sim    Normal simulation: Covariate is effect size
                        under alternative
