Function reference • ebnm

All functions
`coef(<ebnm>)`	Extract posterior means from a fitted EBNM model
`confint(<ebnm>)`	Obtain credible intervals using a fitted EBNM model
`ebnm()` `ebnm_output_default()` `ebnm_output_all()`	Solve the EBNM problem
`ebnm_add_sampler()`	Add sampler to an ebnm_object
`ebnm_ash()`	Solve the EBNM problem using an ash family of distributions
`ebnm_check_fn()`	Check a custom ebnm function
`ebnm_deconvolver()`	Solve the EBNM problem using the "deconvolveR" family of distributions
`ebnm_flat()`	Solve the EBNM problem using a flat prior
`ebnm_generalized_binary()`	Solve the EBNM problem using generalized binary priors
`ebnm_group()`	Solve the EBNM problem for grouped data
`ebnm_horseshoe()`	Solve the EBNM problem using horseshoe priors
`ebnm_normal()`	Solve the EBNM problem using normal priors
`ebnm_normal_scale_mixture()`	Solve the EBNM problem using scale mixtures of normals
`ebnm_npmle()`	Solve the EBNM problem using the family of all distributions
`ebnm_point_exponential()`	Solve the EBNM problem using point-exponential priors
`ebnm_point_laplace()`	Solve the EBNM problem using point-Laplace priors
`ebnm_point_mass()`	Solve the EBNM problem using a point mass prior
`ebnm_point_normal()`	Solve the EBNM problem using point-normal priors
`ebnm_scale_normalmix()`	Set scale parameter for scale mixtures of normals
`ebnm_scale_npmle()`	Set scale parameter for NPMLE and deconvolveR prior family
`ebnm_scale_unimix()`	Set scale parameter for nonparametric unimodal prior families
`ebnm_unimodal()`	Solve the EBNM problem using unimodal distributions
`ebnm_unimodal_nonnegative()`	Solve the EBNM problem using unimodal nonnegative distributions
`ebnm_unimodal_nonpositive()`	Solve the EBNM problem using unimodal nonpositive distributions
`ebnm_unimodal_symmetric()`	Solve the EBNM problem using symmetric unimodal distributions
`fitted(<ebnm>)`	Extract posterior estimates from a fitted EBNM model
`gammamix()`	Constructor for gammamix class
`horseshoe()`	Constructor for horseshoe class
`laplacemix()`	Constructor for laplacemix class
`logLik(<ebnm>)`	Extract the log likelihood from a fitted EBNM model
`nobs(<ebnm>)`	Get the number of observations used to fit an EBNM model
`plot(<ebnm>)`	Plot an ebnm object
`predict(<ebnm>)`	Use the estimated prior from a fitted EBNM model to solve the EBNM problem for new data
`print(<ebnm>)`	Print an ebnm object
`print(<summary.ebnm>)`	Print a summary.ebnm object
`quantile(<ebnm>)`	Obtain posterior quantiles using a fitted EBNM model
`residuals(<ebnm>)`	Calculate residuals for a fitted EBNM model
`simulate(<ebnm>)`	Sample from the posterior of a fitted EBNM model
`summary(<ebnm>)`	Summarize an ebnm object
`vcov(<ebnm>)`	Extract posterior variances from a fitted EBNM model
`wOBA`	2022 MLB wOBA Data

Reference

All functions