.converged              check if ELBO is converged to some tolerance
                        threshold
.diff                   helper function gets difference in ELBO between
                        iterations
autoselect_scales_mix_exp
                        adapted from autoselect.mxisqp try to select a
                        default range for the sigmaa values that should
                        be used, based on the values of betahat and
                        sebetahat mode is the location about which
                        inference is going to be centered gridmult is
                        the multiplier by which the sds differ across
                        the grid
autoselect_scales_mix_norm
                        adapted from autoselect.mxisqp try to select a
                        default range for the sigmaa values that should
                        be used, based on the values of betahat and
                        sebetahat mode is the location about which
                        inference is going to be centered gridmult is
                        the multiplier by which the sds differ across
                        the grid
cEBMF                   covariate moderated Empirical Bayes Matrix
                        Factorization
cal_ind_moment12_mix_norm
                        Compute individual posterior first and second
                        moment
cal_purity              Cal purity ouput como2
como_prep_data          Prepare data for multinomial regression
compute_data_loglikelihood
                        Compute data likelihood
compute_elbo            Compute ELBO, but if there is not ELBO
compute_elbo.default    Default no ELBO
compute_posterior_assignment
                        Compute Posterior Assignment Probabilities For
                        each data point return posterior assignment
                        probabilities
convolved_logpdf        Convolved logpdf
convolved_logpdf.exp    Convolution between an exponential and Normal
                        distribution with variance given by sum of
                        component dist. and noise
convolved_logpdf.normal
                        Normal distribution with variance given by sum
                        of component dist. and noise
convolved_logpdf.point
                        Just a normal distribution with mean centered
                        at the point mass
data_initialize_como    data_initialize_como @param data description
                        @param scales description @param mu0
                        description @param var0 description @param
                        nullweight description @param mnreg_type
                        description @param param_nnet description
get_fdr                 Compute individual fdr value como/como2 model
                        with centered normal mixture
get_fdr.como            Compute individual fdr value como model with
                        centered normal mixture
get_lfdr                Compute individual lfdr value como/como2 model
                        with centered normal mixture
init_cEBMF              Initialize cEBMF object
initialize_como         Function implementation the como mode
out_prep.cEBMF          prepare output of cEMBF function
post_mean_sd            Compute post first and second moment from como
                        and como2 object
post_mean_sd.como       Compute individual posterior mean and sd under
                        como model
set_xi                  set xi, update tau
sigmoid                 Sigmoid sigmoid function coverts log-odds to
                        probability
sim_como_beta           Simulate data under como model with beta
                        distribution
sim_mixture_beta        Simulate mixture of beta distribution
sim_ser_with_covariates
                        simulate SER with 3 covariates
sim_susie               Simulate data under the logistic SuSiE model
sim_twococomo           Simulate data under the twococomo SuSiE model
t_ind_var               Compute individual posterior variance from
                        marginal normal mean model
t_ind_var.como          Compute individual posterior variance from
                        marginal normal mean model
update_params           Update Params
update_params.normal    Note: only updates the variance parameter,
                        these components are assumed to be mean 0
update_tau.cEBMF        Estimate noise value
