deconvolveR
1.2-1Empirical Bayes Estimation Strategies
Overview
Empirical Bayes methods for learning prior distributions from data. An unknown prior distribution (g) has yielded (unobservable) parameters, each of which produces a data point from a parametric exponential family (f). The goal is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical Bayes methods. Details and examples are in the paper by Narasimhan and Efron (2020, doi:10.18637/jss.v094.i11).
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- NOTE2026-06-090 OK · 13 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- WARNING2026-06-080 OK · 12 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-03-100 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 80%
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2 development-tooling and community-health practices detected across 2 families in the upstream repository
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3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 1.2-1Latest
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 1.12019-02-08 · diff ↗
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- 1.0-32016-12-01
- RR 3.3.0 released · 2016-05-03
Package metadata
- First published
- 2016-12-01
- Total releases
- 3 / 10 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.0
- Bundled data
- 12 KB / 3 files
- Download size
- 1.7 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet