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ebnm
View on CRAN: Click
here
Download and install ebnm package within the R console
Install from CRAN:
install.packages("ebnm")
Install from Github:
library("remotes")
install_github("cran/ebnm")
Install by package version:
library("remotes")
install_version("ebnm", "1.1-2")
Attach the package and use:
library("ebnm")
Maintained by
Peter Carbonetto
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-03-08
Latest Update: 2022-03-08
Description:
Provides simple, fast, and stable functions to fit the normal
means model using empirical Bayes. For available models and details, see
function ebnm(). A detailed introduction to the package is provided
by Willwerscheid and Stephens (2021) .
How to cite:
Peter Carbonetto (2022). ebnm: Solve the Empirical Bayes Normal Means Problem. R package version 1.1-2, https://cran.r-project.org/web/packages/ebnm. Accessed 05 Jan. 2025.
Previous versions and publish date:
1.0-9 (2022-03-08 21:10)
Other packages that cited ebnm R package
View ebnm citation profile
Other R packages that ebnm depends,
imports, suggests or enhances
Complete documentation for ebnm
Functions, R codes and Examples using
the ebnm R package
Some associated functions: coef.ebnm . confint.ebnm . ebnm . ebnm_add_sampler . ebnm_ash . ebnm_deconvolver . ebnm_flat . ebnm_generalized_binary . ebnm_group . ebnm_horseshoe . ebnm_normal . ebnm_normal_scale_mixture . ebnm_npmle . ebnm_point_exponential . ebnm_point_laplace . ebnm_point_mass . ebnm_point_normal . ebnm_scale_normalmix . ebnm_scale_npmle . ebnm_scale_unimix . ebnm_unimodal . ebnm_unimodal_nonnegative . ebnm_unimodal_nonpositive . ebnm_unimodal_symmetric . fitted.ebnm . gammamix . horseshoe . laplacemix . logLik.ebnm . nobs.ebnm . plot.ebnm . predict.ebnm . print.ebnm . print.summary.ebnm . quantile.ebnm . residuals.ebnm . simulate.ebnm . summary.ebnm . vcov.ebnm . wOBA .
Some associated R codes: add_sampler.R . data.R . deconvolver.R . ebnm.R . ebnm_fns.R . ebnm_group.R . ebnm_methods.R . flat_prior.R . generalized_binary.R . grid_selection.R . horseshoe.R . infer_prior_family.R . internal_data_fns.R . loglik_point_laplace.R . loglik_point_normal.R . mle_pn_special_cases.R . opt_control_defaults.R . output.R . point_exponential.R . point_laplace.R . point_normal.R . rebayes.R . workhorse_ash.R . workhorse_normal_mix.R . workhorse_parametric.R . Full ebnm package functions and examples
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