coef.ebnm |
Extract posterior means from a fitted EBNM model |
confint.ebnm |
Obtain confidence intervals using a fitted EBNM model |
ebnm |
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_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_output_all |
Solve the EBNM problem |
ebnm_output_default |
Solve the EBNM problem |
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 |