Package: GreedyEPL 1.2

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_1.2.tar.gz
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GreedyEPL_1.2.tgz(r-4.5-x86_64)GreedyEPL_1.2.tgz(r-4.5-arm64)GreedyEPL_1.2.tgz(r-4.4-x86_64)GreedyEPL_1.2.tgz(r-4.4-arm64)GreedyEPL_1.2.tgz(r-4.3-x86_64)GreedyEPL_1.2.tgz(r-4.3-arm64)
GreedyEPL_1.2.tar.gz(r-4.5-noble)GreedyEPL_1.2.tar.gz(r-4.4-noble)
GreedyEPL_1.2.tgz(r-4.4-emscripten)GreedyEPL_1.2.tgz(r-4.3-emscripten)
GreedyEPL.pdf |GreedyEPL.html✨
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 205 downloads 1 mentions 2 exports 2 dependencies

Last updated 4 years agofrom:5a72356b5e. Checks:4 OK, 8 NOTE. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 07 2025
R-4.5-win-x86_64	NOTE	Mar 07 2025
R-4.5-mac-x86_64	NOTE	Mar 07 2025
R-4.5-mac-aarch64	NOTE	Mar 07 2025
R-4.5-linux-x86_64	NOTE	Mar 07 2025
R-4.4-win-x86_64	NOTE	Mar 07 2025
R-4.4-mac-x86_64	NOTE	Mar 07 2025
R-4.4-mac-aarch64	NOTE	Mar 07 2025
R-4.4-linux-x86_64	NOTE	Mar 07 2025
R-4.3-win-x86_64	OK	Mar 07 2025
R-4.3-mac-x86_64	OK	Mar 07 2025
R-4.3-mac-aarch64	OK	Mar 07 2025