mined: Minimum Energy Designs

This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2019) <doi:10.1080/00401706.2018.1552203>. The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate. This research is supported by a U.S. National Science Foundation grant DMS-1712642.

Version: 1.0-3
Imports: Rcpp (≥ 0.12.17)
LinkingTo: Rcpp, RcppEigen
Published: 2022-06-26
Author: Dianpeng Wang and V. Roshan Joseph
Maintainer: Dianpeng Wang <wdp at bit.edu.cn>
License: LGPL-2.1
NeedsCompilation: yes
CRAN checks: mined results


Reference manual: mined.pdf


Package source: mined_1.0-3.tar.gz
Windows binaries: r-devel: mined_1.0-3.zip, r-release: mined_1.0-3.zip, r-oldrel: mined_1.0-3.zip
macOS binaries: r-release (arm64): mined_1.0-3.tgz, r-oldrel (arm64): mined_1.0-3.tgz, r-release (x86_64): mined_1.0-3.tgz, r-oldrel (x86_64): mined_1.0-3.tgz
Old sources: mined archive


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