ebTobit: Empirical Bayesian Tobit Matrix Estimation

Estimation tools for multidimensional Gaussian means using empirical Bayesian g-modeling. Methods are able to handle fully observed data as well as left-, right-, and interval-censored observations (Tobit likelihood); descriptions of these methods can be found in Barbehenn and Zhao (2023) <doi:10.48550/arXiv.2306.07239>. Additional, lower-level functionality based on Kiefer and Wolfowitz (1956) <doi:10.1214/aoms/1177728066> and Jiang and Zhang (2009) <doi:10.1214/08-AOS638> is provided that can be used to accelerate many empirical Bayes and nonparametric maximum likelihood problems.

Version: 1.0.1
Depends: R (≥ 3.3.0)
Imports: Rcpp (≥ 1.0.10), RcppParallel, stats
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Suggests: REBayes
Published: 2023-06-15
Author: Alton Barbehenn ORCID iD [aut, cre], Sihai Dave Zhao [aut]
Maintainer: Alton Barbehenn <altonbarbehenn at gmail.com>
BugReports: https://github.com/barbehenna/ebTobit/issues
License: GPL-3
URL: https://github.com/barbehenna/ebTobit
NeedsCompilation: yes
Materials: README
CRAN checks: ebTobit results

Documentation:

Reference manual: ebTobit.pdf

Downloads:

Package source: ebTobit_1.0.1.tar.gz
Windows binaries: r-devel: ebTobit_1.0.1.zip, r-release: ebTobit_1.0.1.zip, r-oldrel: ebTobit_1.0.1.zip
macOS binaries: r-release (arm64): ebTobit_1.0.1.tgz, r-oldrel (arm64): ebTobit_1.0.1.tgz, r-release (x86_64): ebTobit_1.0.1.tgz

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