Package: ebnm 1.1-34

Peter Carbonetto

ebnm: Solve the Empirical Bayes Normal Means Problem

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 (2023) <arxiv:2110.00152>.

Authors:Jason Willwerscheid [aut], Matthew Stephens [aut], Peter Carbonetto [aut, cre], Andrew Goldstein [ctb], Yusha Liu [ctb]

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ebnm.pdf |ebnm.html✨
ebnm/json (API)

# Install 'ebnm' in R:
install.packages('ebnm', repos = c('https://stephenslab.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/stephenslab/ebnm/issues

Datasets:
  • wOBA - 2022 MLB wOBA Data

On CRAN:

8.29 score 12 stars 1 packages 110 scripts 351 downloads 28 exports 43 dependencies

Last updated 5 months agofrom:faaea4729b. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 14 2024
R-4.5-winOKNov 14 2024
R-4.5-linuxOKNov 14 2024
R-4.4-winNOTENov 14 2024
R-4.4-macNOTENov 14 2024
R-4.3-winNOTENov 14 2024
R-4.3-macNOTENov 14 2024

Exports:ebnmebnm_add_samplerebnm_ashebnm_check_fnebnm_deconvolverebnm_flatebnm_generalized_binaryebnm_groupebnm_horseshoeebnm_normalebnm_normal_scale_mixtureebnm_npmleebnm_output_allebnm_output_defaultebnm_point_exponentialebnm_point_laplaceebnm_point_massebnm_point_normalebnm_scale_normalmixebnm_scale_npmleebnm_scale_unimixebnm_unimodalebnm_unimodal_nonnegativeebnm_unimodal_nonpositiveebnm_unimodal_symmetricgammamixhorseshoelaplacemix

Dependencies:ashrclicolorspacedeconvolveRdplyretrunctfansifarvergenericsggplot2gluegtablehorseshoeinvgammairlbaisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmixsqpmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalesSQUAREMtibbletidyselecttruncnormtrustutf8vctrsviridisLitewithr

An analysis of weighted on-base averages using the ebnm package

Rendered frombaseball.Rmdusingknitr::rmarkdownon Nov 14 2024.

Last update: 2024-03-13
Started: 2024-02-15

Extending ebnm with custom ebnm-style functions

Rendered fromextending_ebnm.Rmdusingknitr::rmarkdownon Nov 14 2024.

Last update: 2024-06-06
Started: 2024-03-13

Introduction to the empirical Bayes normal means model via shrinkage estimation

Rendered fromshrink_intro.Rmdusingknitr::rmarkdownon Nov 14 2024.

Last update: 2024-03-11
Started: 2024-02-12

Readme and manuals

Help Manual

Help pageTopics
Extract posterior means from a fitted EBNM modelcoef.ebnm
Obtain credible intervals using a fitted EBNM modelconfint.ebnm
Solve the EBNM problemebnm ebnm_output_all ebnm_output_default
Add sampler to an ebnm_objectebnm_add_sampler
Solve the EBNM problem using an ash family of distributionsebnm_ash
Check a custom ebnm functionebnm_check_fn
Solve the EBNM problem using the "deconvolveR" family of distributionsebnm_deconvolver
Solve the EBNM problem using a flat priorebnm_flat
Solve the EBNM problem using generalized binary priorsebnm_generalized_binary
Solve the EBNM problem for grouped dataebnm_group
Solve the EBNM problem using horseshoe priorsebnm_horseshoe
Solve the EBNM problem using normal priorsebnm_normal
Solve the EBNM problem using scale mixtures of normalsebnm_normal_scale_mixture
Solve the EBNM problem using the family of all distributionsebnm_npmle
Solve the EBNM problem using point-exponential priorsebnm_point_exponential
Solve the EBNM problem using point-Laplace priorsebnm_point_laplace
Solve the EBNM problem using a point mass priorebnm_point_mass
Solve the EBNM problem using point-normal priorsebnm_point_normal
Set scale parameter for scale mixtures of normalsebnm_scale_normalmix
Set scale parameter for NPMLE and deconvolveR prior familyebnm_scale_npmle
Set scale parameter for nonparametric unimodal prior familiesebnm_scale_unimix
Solve the EBNM problem using unimodal distributionsebnm_unimodal
Solve the EBNM problem using unimodal nonnegative distributionsebnm_unimodal_nonnegative
Solve the EBNM problem using unimodal nonpositive distributionsebnm_unimodal_nonpositive
Solve the EBNM problem using symmetric unimodal distributionsebnm_unimodal_symmetric
Extract posterior estimates from a fitted EBNM modelfitted.ebnm
Constructor for gammamix classgammamix
Constructor for horseshoe classhorseshoe
Constructor for laplacemix classlaplacemix
Extract the log likelihood from a fitted EBNM modellogLik.ebnm
Get the number of observations used to fit an EBNM modelnobs.ebnm
Plot an ebnm objectplot.ebnm
Use the estimated prior from a fitted EBNM model to solve the EBNM problem for new datapredict.ebnm
Print an ebnm objectprint.ebnm
Print a summary.ebnm objectprint.summary.ebnm
Obtain posterior quantiles using a fitted EBNM modelquantile.ebnm
Calculate residuals for a fitted EBNM modelresiduals.ebnm
Sample from the posterior of a fitted EBNM modelsimulate.ebnm
Summarize an ebnm objectsummary.ebnm
Extract posterior variances from a fitted EBNM modelvcov.ebnm
2022 MLB wOBA DatawOBA