Package: GreedyEPL 1.3

GreedyEPL: Greedy Expected Posterior Loss

Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2017) "Optimal Bayesian estimators for latent variable cluster models" <doi:10.1007/s11222-017-9786-y>.

Authors:Riccardo Rastelli [aut, cre]

GreedyEPL_1.3.tar.gz
GreedyEPL_1.3.zip(r-4.7-x86_64)GreedyEPL_1.3.zip(r-4.6-x86_64)GreedyEPL_1.3.zip(r-4.5-x86_64)
GreedyEPL_1.3.tgz(r-4.6-x86_64)GreedyEPL_1.3.tgz(r-4.6-arm64)GreedyEPL_1.3.tgz(r-4.5-x86_64)GreedyEPL_1.3.tgz(r-4.5-arm64)
GreedyEPL_1.3.tar.gz(r-4.7-arm64)GreedyEPL_1.3.tar.gz(r-4.7-x86_64)GreedyEPL_1.3.tar.gz(r-4.6-arm64)GreedyEPL_1.3.tar.gz(r-4.6-x86_64)
GreedyEPL_1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
GreedyEPL/json (API)

# Install 'GreedyEPL' in R:

install.packages('GreedyEPL', repos = c('https://riccardorastelli.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblas cpp

1.08 score 12 scripts 254 downloads 1 mentions 2 exports 2 dependencies

Last updated from:08f4df062a. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	142
linux-devel-x86_64	OK	127
source / vignettes	OK	206
linux-release-arm64	OK	145
linux-release-x86_64	OK	140
macos-release-arm64	OK	139
macos-release-x86_64	OK	220
macos-oldrel-arm64	OK	151
macos-oldrel-x86_64	OK	168
windows-devel	OK	150
windows-release	OK	123
windows-oldrel	OK	121
wasm-release	OK	121