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deconvolveR  

Empirical Bayes Estimation Strategies
View on CRAN: Click here


Download and install deconvolveR package within the R console
Install from CRAN:
install.packages("deconvolveR")

Install from Github:
library("remotes")
install_github("cran/deconvolveR")

Install by package version:
library("remotes")
install_version("deconvolveR", "1.2-1")



Attach the package and use:
library("deconvolveR")
Maintained by
Balasubramanian Narasimhan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-12-01
Latest Update: 2020-08-30
Description:
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, ).
How to cite:
Balasubramanian Narasimhan (2016). deconvolveR: Empirical Bayes Estimation Strategies. R package version 1.2-1, https://cran.r-project.org/web/packages/deconvolveR. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0-3 (2016-12-01 19:44), 1.1 (2019-02-08 23:33)
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Complete documentation for deconvolveR
Functions, R codes and Examples using the deconvolveR R package
Some associated functions: bardWordCount . deconv . deconvolveR-package . disjointTheta . surg . 
Some associated R codes: deconv.R . deconvolveR-package.R .  Full deconvolveR package functions and examples
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